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# FLAVA
## Overview
The FLAVA model was proposed in [FLAVA: A Foundational Language And Vision Alignment Model](https://arxiv.org/abs/2112.04482) by Amanpreet Singh, Ronghang Hu, Vedanuj Goswami, Guillaume Couairon, Wojciech Galuba, Marcus Rohrbach, and Douwe Kiela and is accepted at CVPR 2022.
The paper aims at creating a single unified foundation model which can work across vision, language
as well as vision-and-language multimodal tasks.
The abstract from the paper is the following:
*State-of-the-art vision and vision-and-language models rely on large-scale visio-linguistic pretraining for obtaining good performance on a variety
of downstream tasks. Generally, such models are often either cross-modal (contrastive) or multi-modal
(with earlier fusion) but not both; and they often only target specific modalities or tasks. A promising
direction would be to use a single holistic universal model, as a "foundation", that targets all modalities
at once -- a true vision and language foundation model should be good at vision tasks, language tasks, and
cross- and multi-modal vision and language tasks. We introduce FLAVA as such a model and demonstrate
impressive performance on a wide range of 35 tasks spanning these target modalities.*
This model was contributed by [aps](https://huggingface.co/aps). The original code can be found [here](https://github.com/facebookresearch/multimodal/tree/main/examples/flava).
## FlavaConfig
[[autodoc]] FlavaConfig
## FlavaTextConfig
[[autodoc]] FlavaTextConfig
## FlavaImageConfig
[[autodoc]] FlavaImageConfig
## FlavaMultimodalConfig
[[autodoc]] FlavaMultimodalConfig
## FlavaImageCodebookConfig
[[autodoc]] FlavaImageCodebookConfig
## FlavaProcessor
[[autodoc]] FlavaProcessor
## FlavaFeatureExtractor
[[autodoc]] FlavaFeatureExtractor
## FlavaImageProcessor
[[autodoc]] FlavaImageProcessor
- preprocess
## FlavaForPreTraining
[[autodoc]] FlavaForPreTraining
- forward
## FlavaModel
[[autodoc]] FlavaModel
- forward
- get_text_features
- get_image_features
## FlavaImageCodebook
[[autodoc]] FlavaImageCodebook
- forward
- get_codebook_indices
- get_codebook_probs
## FlavaTextModel
[[autodoc]] FlavaTextModel
- forward
## FlavaImageModel
[[autodoc]] FlavaImageModel
- forward
## FlavaMultimodalModel
[[autodoc]] FlavaMultimodalModel
- forward
| huggingface/transformers/blob/main/docs/source/en/model_doc/flava.md |
!--Copyright 2020 The HuggingFace Team. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
the License. You may obtain a copy of the License at
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# CTRL
<div class="flex flex-wrap space-x-1">
<a href="https://huggingface.co/models?filter=ctrl">
<img alt="Models" src="https://img.shields.io/badge/All_model_pages-ctrl-blueviolet">
</a>
<a href="https://huggingface.co/spaces/docs-demos/tiny-ctrl">
<img alt="Spaces" src="https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Spaces-blue">
</a>
</div>
## Overview
CTRL model was proposed in [CTRL: A Conditional Transformer Language Model for Controllable Generation](https://arxiv.org/abs/1909.05858) by Nitish Shirish Keskar*, Bryan McCann*, Lav R. Varshney, Caiming Xiong and
Richard Socher. It's a causal (unidirectional) transformer pre-trained using language modeling on a very large corpus
of ~140 GB of text data with the first token reserved as a control code (such as Links, Books, Wikipedia etc.).
The abstract from the paper is the following:
*Large-scale language models show promising text generation capabilities, but users cannot easily control particular
aspects of the generated text. We release CTRL, a 1.63 billion-parameter conditional transformer language model,
trained to condition on control codes that govern style, content, and task-specific behavior. Control codes were
derived from structure that naturally co-occurs with raw text, preserving the advantages of unsupervised learning while
providing more explicit control over text generation. These codes also allow CTRL to predict which parts of the
training data are most likely given a sequence. This provides a potential method for analyzing large amounts of data
via model-based source attribution.*
This model was contributed by [keskarnitishr](https://huggingface.co/keskarnitishr). The original code can be found
[here](https://github.com/salesforce/ctrl).
## Usage tips
- CTRL makes use of control codes to generate text: it requires generations to be started by certain words, sentences
or links to generate coherent text. Refer to the [original implementation](https://github.com/salesforce/ctrl) for
more information.
- CTRL is a model with absolute position embeddings so it's usually advised to pad the inputs on the right rather than
the left.
- CTRL was trained with a causal language modeling (CLM) objective and is therefore powerful at predicting the next
token in a sequence. Leveraging this feature allows CTRL to generate syntactically coherent text as it can be
observed in the *run_generation.py* example script.
- The PyTorch models can take the `past_key_values` as input, which is the previously computed key/value attention pairs.
TensorFlow models accepts `past` as input. Using the `past_key_values` value prevents the model from re-computing
pre-computed values in the context of text generation. See the [`forward`](model_doc/ctrl#transformers.CTRLModel.forward)
method for more information on the usage of this argument.
## Resources
- [Text classification task guide](../tasks/sequence_classification)
- [Causal language modeling task guide](../tasks/language_modeling)
## CTRLConfig
[[autodoc]] CTRLConfig
## CTRLTokenizer
[[autodoc]] CTRLTokenizer
- save_vocabulary
<frameworkcontent>
<pt>
## CTRLModel
[[autodoc]] CTRLModel
- forward
## CTRLLMHeadModel
[[autodoc]] CTRLLMHeadModel
- forward
## CTRLForSequenceClassification
[[autodoc]] CTRLForSequenceClassification
- forward
</pt>
<tf>
## TFCTRLModel
[[autodoc]] TFCTRLModel
- call
## TFCTRLLMHeadModel
[[autodoc]] TFCTRLLMHeadModel
- call
## TFCTRLForSequenceClassification
[[autodoc]] TFCTRLForSequenceClassification
- call
</tf>
</frameworkcontent>
| huggingface/transformers/blob/main/docs/source/en/model_doc/ctrl.md |
!--Copyright 2021 The HuggingFace Team. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
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Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
specific language governing permissions and limitations under the License.
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# T5v1.1
## Overview
T5v1.1 was released in the [google-research/text-to-text-transfer-transformer](https://github.com/google-research/text-to-text-transfer-transformer/blob/main/released_checkpoints.md#t511)
repository by Colin Raffel et al. It's an improved version of the original T5 model.
This model was contributed by [patrickvonplaten](https://huggingface.co/patrickvonplaten). The original code can be
found [here](https://github.com/google-research/text-to-text-transfer-transformer/blob/main/released_checkpoints.md#t511).
## Usage tips
One can directly plug in the weights of T5v1.1 into a T5 model, like so:
```python
>>> from transformers import T5ForConditionalGeneration
>>> model = T5ForConditionalGeneration.from_pretrained("google/t5-v1_1-base")
```
T5 Version 1.1 includes the following improvements compared to the original T5 model:
- GEGLU activation in the feed-forward hidden layer, rather than ReLU. See [this paper](https://arxiv.org/abs/2002.05202).
- Dropout was turned off in pre-training (quality win). Dropout should be re-enabled during fine-tuning.
- Pre-trained on C4 only without mixing in the downstream tasks.
- No parameter sharing between the embedding and classifier layer.
- "xl" and "xxl" replace "3B" and "11B". The model shapes are a bit different - larger `d_model` and smaller
`num_heads` and `d_ff`.
Note: T5 Version 1.1 was only pre-trained on [C4](https://huggingface.co/datasets/c4) excluding any supervised
training. Therefore, this model has to be fine-tuned before it is usable on a downstream task, unlike the original T5
model. Since t5v1.1 was pre-trained unsupervisedly, there's no real advantage to using a task prefix during single-task
fine-tuning. If you are doing multi-task fine-tuning, you should use a prefix.
Google has released the following variants:
- [google/t5-v1_1-small](https://huggingface.co/google/t5-v1_1-small)
- [google/t5-v1_1-base](https://huggingface.co/google/t5-v1_1-base)
- [google/t5-v1_1-large](https://huggingface.co/google/t5-v1_1-large)
- [google/t5-v1_1-xl](https://huggingface.co/google/t5-v1_1-xl)
- [google/t5-v1_1-xxl](https://huggingface.co/google/t5-v1_1-xxl).
<Tip>
Refer to [T5's documentation page](t5) for all API reference, tips, code examples and notebooks.
</Tip> | huggingface/transformers/blob/main/docs/source/en/model_doc/t5v1.1.md |
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# Feature Extractor
A feature extractor is in charge of preparing input features for audio or vision models. This includes feature extraction from sequences, e.g., pre-processing audio files to generate Log-Mel Spectrogram features, feature extraction from images, e.g., cropping image files, but also padding, normalization, and conversion to NumPy, PyTorch, and TensorFlow tensors.
## FeatureExtractionMixin
[[autodoc]] feature_extraction_utils.FeatureExtractionMixin
- from_pretrained
- save_pretrained
## SequenceFeatureExtractor
[[autodoc]] SequenceFeatureExtractor
- pad
## BatchFeature
[[autodoc]] BatchFeature
## ImageFeatureExtractionMixin
[[autodoc]] image_utils.ImageFeatureExtractionMixin
| huggingface/transformers/blob/main/docs/source/en/main_classes/feature_extractor.md |
!--Copyright 2020 The HuggingFace Team. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
the License. You may obtain a copy of the License at
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Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
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# Glossary
This glossary defines general machine learning and 🤗 Transformers terms to help you better understand the
documentation.
## A
### attention mask
The attention mask is an optional argument used when batching sequences together.
<Youtube id="M6adb1j2jPI"/>
This argument indicates to the model which tokens should be attended to, and which should not.
For example, consider these two sequences:
```python
>>> from transformers import BertTokenizer
>>> tokenizer = BertTokenizer.from_pretrained("bert-base-cased")
>>> sequence_a = "This is a short sequence."
>>> sequence_b = "This is a rather long sequence. It is at least longer than the sequence A."
>>> encoded_sequence_a = tokenizer(sequence_a)["input_ids"]
>>> encoded_sequence_b = tokenizer(sequence_b)["input_ids"]
```
The encoded versions have different lengths:
```python
>>> len(encoded_sequence_a), len(encoded_sequence_b)
(8, 19)
```
Therefore, we can't put them together in the same tensor as-is. The first sequence needs to be padded up to the length
of the second one, or the second one needs to be truncated down to the length of the first one.
In the first case, the list of IDs will be extended by the padding indices. We can pass a list to the tokenizer and ask
it to pad like this:
```python
>>> padded_sequences = tokenizer([sequence_a, sequence_b], padding=True)
```
We can see that 0s have been added on the right of the first sentence to make it the same length as the second one:
```python
>>> padded_sequences["input_ids"]
[[101, 1188, 1110, 170, 1603, 4954, 119, 102, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [101, 1188, 1110, 170, 1897, 1263, 4954, 119, 1135, 1110, 1120, 1655, 2039, 1190, 1103, 4954, 138, 119, 102]]
```
This can then be converted into a tensor in PyTorch or TensorFlow. The attention mask is a binary tensor indicating the
position of the padded indices so that the model does not attend to them. For the [`BertTokenizer`], `1` indicates a
value that should be attended to, while `0` indicates a padded value. This attention mask is in the dictionary returned
by the tokenizer under the key "attention_mask":
```python
>>> padded_sequences["attention_mask"]
[[1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]
```
### autoencoding models
See [encoder models](#encoder-models) and [masked language modeling](#masked-language-modeling-mlm)
### autoregressive models
See [causal language modeling](#causal-language-modeling) and [decoder models](#decoder-models)
## B
### backbone
The backbone is the network (embeddings and layers) that outputs the raw hidden states or features. It is usually connected to a [head](#head) which accepts the features as its input to make a prediction. For example, [`ViTModel`] is a backbone without a specific head on top. Other models can also use [`VitModel`] as a backbone such as [DPT](model_doc/dpt).
## C
### causal language modeling
A pretraining task where the model reads the texts in order and has to predict the next word. It's usually done by
reading the whole sentence but using a mask inside the model to hide the future tokens at a certain timestep.
### channel
Color images are made up of some combination of values in three channels: red, green, and blue (RGB) and grayscale images only have one channel. In 🤗 Transformers, the channel can be the first or last dimension of an image's tensor: [`n_channels`, `height`, `width`] or [`height`, `width`, `n_channels`].
### connectionist temporal classification (CTC)
An algorithm which allows a model to learn without knowing exactly how the input and output are aligned; CTC calculates the distribution of all possible outputs for a given input and chooses the most likely output from it. CTC is commonly used in speech recognition tasks because speech doesn't always cleanly align with the transcript for a variety of reasons such as a speaker's different speech rates.
### convolution
A type of layer in a neural network where the input matrix is multiplied element-wise by a smaller matrix (kernel or filter) and the values are summed up in a new matrix. This is known as a convolutional operation which is repeated over the entire input matrix. Each operation is applied to a different segment of the input matrix. Convolutional neural networks (CNNs) are commonly used in computer vision.
## D
### DataParallel (DP)
Parallelism technique for training on multiple GPUs where the same setup is replicated multiple times, with each instance
receiving a distinct data slice. The processing is done in parallel and all setups are synchronized at the end of each training step.
Learn more about how DataParallel works [here](perf_train_gpu_many#dataparallel-vs-distributeddataparallel).
### decoder input IDs
This input is specific to encoder-decoder models, and contains the input IDs that will be fed to the decoder. These
inputs should be used for sequence to sequence tasks, such as translation or summarization, and are usually built in a
way specific to each model.
Most encoder-decoder models (BART, T5) create their `decoder_input_ids` on their own from the `labels`. In such models,
passing the `labels` is the preferred way to handle training.
Please check each model's docs to see how they handle these input IDs for sequence to sequence training.
### decoder models
Also referred to as autoregressive models, decoder models involve a pretraining task (called causal language modeling) where the model reads the texts in order and has to predict the next word. It's usually done by
reading the whole sentence with a mask to hide future tokens at a certain timestep.
<Youtube id="d_ixlCubqQw"/>
### deep learning (DL)
Machine learning algorithms which uses neural networks with several layers.
## E
### encoder models
Also known as autoencoding models, encoder models take an input (such as text or images) and transform them into a condensed numerical representation called an embedding. Oftentimes, encoder models are pretrained using techniques like [masked language modeling](#masked-language-modeling-mlm), which masks parts of the input sequence and forces the model to create more meaningful representations.
<Youtube id="H39Z_720T5s"/>
## F
### feature extraction
The process of selecting and transforming raw data into a set of features that are more informative and useful for machine learning algorithms. Some examples of feature extraction include transforming raw text into word embeddings and extracting important features such as edges or shapes from image/video data.
### feed forward chunking
In each residual attention block in transformers the self-attention layer is usually followed by 2 feed forward layers.
The intermediate embedding size of the feed forward layers is often bigger than the hidden size of the model (e.g., for
`bert-base-uncased`).
For an input of size `[batch_size, sequence_length]`, the memory required to store the intermediate feed forward
embeddings `[batch_size, sequence_length, config.intermediate_size]` can account for a large fraction of the memory
use. The authors of [Reformer: The Efficient Transformer](https://arxiv.org/abs/2001.04451) noticed that since the
computation is independent of the `sequence_length` dimension, it is mathematically equivalent to compute the output
embeddings of both feed forward layers `[batch_size, config.hidden_size]_0, ..., [batch_size, config.hidden_size]_n`
individually and concat them afterward to `[batch_size, sequence_length, config.hidden_size]` with `n = sequence_length`, which trades increased computation time against reduced memory use, but yields a mathematically
**equivalent** result.
For models employing the function [`apply_chunking_to_forward`], the `chunk_size` defines the number of output
embeddings that are computed in parallel and thus defines the trade-off between memory and time complexity. If
`chunk_size` is set to 0, no feed forward chunking is done.
### finetuned models
Finetuning is a form of transfer learning which involves taking a pretrained model, freezing its weights, and replacing the output layer with a newly added [model head](#head). The model head is trained on your target dataset.
See the [Fine-tune a pretrained model](https://huggingface.co/docs/transformers/training) tutorial for more details, and learn how to fine-tune models with 🤗 Transformers.
## H
### head
The model head refers to the last layer of a neural network that accepts the raw hidden states and projects them onto a different dimension. There is a different model head for each task. For example:
* [`GPT2ForSequenceClassification`] is a sequence classification head - a linear layer - on top of the base [`GPT2Model`].
* [`ViTForImageClassification`] is an image classification head - a linear layer on top of the final hidden state of the `CLS` token - on top of the base [`ViTModel`].
* [`Wav2Vec2ForCTC`] is a language modeling head with [CTC](#connectionist-temporal-classification-(CTC)) on top of the base [`Wav2Vec2Model`].
## I
### image patch
Vision-based Transformers models split an image into smaller patches which are linearly embedded, and then passed as a sequence to the model. You can find the `patch_size` - or resolution - of the model in its configuration.
### inference
Inference is the process of evaluating a model on new data after training is complete. See the [Pipeline for inference](https://huggingface.co/docs/transformers/pipeline_tutorial) tutorial to learn how to perform inference with 🤗 Transformers.
### input IDs
The input ids are often the only required parameters to be passed to the model as input. They are token indices,
numerical representations of tokens building the sequences that will be used as input by the model.
<Youtube id="VFp38yj8h3A"/>
Each tokenizer works differently but the underlying mechanism remains the same. Here's an example using the BERT
tokenizer, which is a [WordPiece](https://arxiv.org/pdf/1609.08144.pdf) tokenizer:
```python
>>> from transformers import BertTokenizer
>>> tokenizer = BertTokenizer.from_pretrained("bert-base-cased")
>>> sequence = "A Titan RTX has 24GB of VRAM"
```
The tokenizer takes care of splitting the sequence into tokens available in the tokenizer vocabulary.
```python
>>> tokenized_sequence = tokenizer.tokenize(sequence)
```
The tokens are either words or subwords. Here for instance, "VRAM" wasn't in the model vocabulary, so it's been split
in "V", "RA" and "M". To indicate those tokens are not separate words but parts of the same word, a double-hash prefix
is added for "RA" and "M":
```python
>>> print(tokenized_sequence)
['A', 'Titan', 'R', '##T', '##X', 'has', '24', '##GB', 'of', 'V', '##RA', '##M']
```
These tokens can then be converted into IDs which are understandable by the model. This can be done by directly feeding the sentence to the tokenizer, which leverages the Rust implementation of [🤗 Tokenizers](https://github.com/huggingface/tokenizers) for peak performance.
```python
>>> inputs = tokenizer(sequence)
```
The tokenizer returns a dictionary with all the arguments necessary for its corresponding model to work properly. The
token indices are under the key `input_ids`:
```python
>>> encoded_sequence = inputs["input_ids"]
>>> print(encoded_sequence)
[101, 138, 18696, 155, 1942, 3190, 1144, 1572, 13745, 1104, 159, 9664, 2107, 102]
```
Note that the tokenizer automatically adds "special tokens" (if the associated model relies on them) which are special
IDs the model sometimes uses.
If we decode the previous sequence of ids,
```python
>>> decoded_sequence = tokenizer.decode(encoded_sequence)
```
we will see
```python
>>> print(decoded_sequence)
[CLS] A Titan RTX has 24GB of VRAM [SEP]
```
because this is the way a [`BertModel`] is going to expect its inputs.
## L
### labels
The labels are an optional argument which can be passed in order for the model to compute the loss itself. These labels
should be the expected prediction of the model: it will use the standard loss in order to compute the loss between its
predictions and the expected value (the label).
These labels are different according to the model head, for example:
- For sequence classification models, ([`BertForSequenceClassification`]), the model expects a tensor of dimension
`(batch_size)` with each value of the batch corresponding to the expected label of the entire sequence.
- For token classification models, ([`BertForTokenClassification`]), the model expects a tensor of dimension
`(batch_size, seq_length)` with each value corresponding to the expected label of each individual token.
- For masked language modeling, ([`BertForMaskedLM`]), the model expects a tensor of dimension `(batch_size,
seq_length)` with each value corresponding to the expected label of each individual token: the labels being the token
ID for the masked token, and values to be ignored for the rest (usually -100).
- For sequence to sequence tasks, ([`BartForConditionalGeneration`], [`MBartForConditionalGeneration`]), the model
expects a tensor of dimension `(batch_size, tgt_seq_length)` with each value corresponding to the target sequences
associated with each input sequence. During training, both BART and T5 will make the appropriate
`decoder_input_ids` and decoder attention masks internally. They usually do not need to be supplied. This does not
apply to models leveraging the Encoder-Decoder framework.
- For image classification models, ([`ViTForImageClassification`]), the model expects a tensor of dimension
`(batch_size)` with each value of the batch corresponding to the expected label of each individual image.
- For semantic segmentation models, ([`SegformerForSemanticSegmentation`]), the model expects a tensor of dimension
`(batch_size, height, width)` with each value of the batch corresponding to the expected label of each individual pixel.
- For object detection models, ([`DetrForObjectDetection`]), the model expects a list of dictionaries with a
`class_labels` and `boxes` key where each value of the batch corresponds to the expected label and number of bounding boxes of each individual image.
- For automatic speech recognition models, ([`Wav2Vec2ForCTC`]), the model expects a tensor of dimension `(batch_size,
target_length)` with each value corresponding to the expected label of each individual token.
<Tip>
Each model's labels may be different, so be sure to always check the documentation of each model for more information
about their specific labels!
</Tip>
The base models ([`BertModel`]) do not accept labels, as these are the base transformer models, simply outputting
features.
### large language models (LLM)
A generic term that refers to transformer language models (GPT-3, BLOOM, OPT) that were trained on a large quantity of data. These models also tend to have a large number of learnable parameters (e.g. 175 billion for GPT-3).
## M
### masked language modeling (MLM)
A pretraining task where the model sees a corrupted version of the texts, usually done by
masking some tokens randomly, and has to predict the original text.
### multimodal
A task that combines texts with another kind of inputs (for instance images).
## N
### Natural language generation (NLG)
All tasks related to generating text (for instance, [Write With Transformers](https://transformer.huggingface.co/), translation).
### Natural language processing (NLP)
A generic way to say "deal with texts".
### Natural language understanding (NLU)
All tasks related to understanding what is in a text (for instance classifying the
whole text, individual words).
## P
### pipeline
A pipeline in 🤗 Transformers is an abstraction referring to a series of steps that are executed in a specific order to preprocess and transform data and return a prediction from a model. Some example stages found in a pipeline might be data preprocessing, feature extraction, and normalization.
For more details, see [Pipelines for inference](https://huggingface.co/docs/transformers/pipeline_tutorial).
### PipelineParallel (PP)
Parallelism technique in which the model is split up vertically (layer-level) across multiple GPUs, so that only one or
several layers of the model are placed on a single GPU. Each GPU processes in parallel different stages of the pipeline
and working on a small chunk of the batch. Learn more about how PipelineParallel works [here](perf_train_gpu_many#from-naive-model-parallelism-to-pipeline-parallelism).
### pixel values
A tensor of the numerical representations of an image that is passed to a model. The pixel values have a shape of [`batch_size`, `num_channels`, `height`, `width`], and are generated from an image processor.
### pooling
An operation that reduces a matrix into a smaller matrix, either by taking the maximum or average of the pooled dimension(s). Pooling layers are commonly found between convolutional layers to downsample the feature representation.
### position IDs
Contrary to RNNs that have the position of each token embedded within them, transformers are unaware of the position of
each token. Therefore, the position IDs (`position_ids`) are used by the model to identify each token's position in the
list of tokens.
They are an optional parameter. If no `position_ids` are passed to the model, the IDs are automatically created as
absolute positional embeddings.
Absolute positional embeddings are selected in the range `[0, config.max_position_embeddings - 1]`. Some models use
other types of positional embeddings, such as sinusoidal position embeddings or relative position embeddings.
### preprocessing
The task of preparing raw data into a format that can be easily consumed by machine learning models. For example, text is typically preprocessed by tokenization. To gain a better idea of what preprocessing looks like for other input types, check out the [Preprocess](https://huggingface.co/docs/transformers/preprocessing) tutorial.
### pretrained model
A model that has been pretrained on some data (for instance all of Wikipedia). Pretraining methods involve a
self-supervised objective, which can be reading the text and trying to predict the next word (see [causal language
modeling](#causal-language-modeling)) or masking some words and trying to predict them (see [masked language
modeling](#masked-language-modeling-mlm)).
Speech and vision models have their own pretraining objectives. For example, Wav2Vec2 is a speech model pretrained on a contrastive task which requires the model to identify the "true" speech representation from a set of "false" speech representations. On the other hand, BEiT is a vision model pretrained on a masked image modeling task which masks some of the image patches and requires the model to predict the masked patches (similar to the masked language modeling objective).
## R
### recurrent neural network (RNN)
A type of model that uses a loop over a layer to process texts.
### representation learning
A subfield of machine learning which focuses on learning meaningful representations of raw data. Some examples of representation learning techniques include word embeddings, autoencoders, and Generative Adversarial Networks (GANs).
## S
### sampling rate
A measurement in hertz of the number of samples (the audio signal) taken per second. The sampling rate is a result of discretizing a continuous signal such as speech.
### self-attention
Each element of the input finds out which other elements of the input they should attend to.
### self-supervised learning
A category of machine learning techniques in which a model creates its own learning objective from unlabeled data. It differs from [unsupervised learning](#unsupervised-learning) and [supervised learning](#supervised-learning) in that the learning process is supervised, but not explicitly from the user.
One example of self-supervised learning is [masked language modeling](#masked-language-modeling-mlm), where a model is passed sentences with a proportion of its tokens removed and learns to predict the missing tokens.
### semi-supervised learning
A broad category of machine learning training techniques that leverages a small amount of labeled data with a larger quantity of unlabeled data to improve the accuracy of a model, unlike [supervised learning](#supervised-learning) and [unsupervised learning](#unsupervised-learning).
An example of a semi-supervised learning approach is "self-training", in which a model is trained on labeled data, and then used to make predictions on the unlabeled data. The portion of the unlabeled data that the model predicts with the most confidence gets added to the labeled dataset and used to retrain the model.
### sequence-to-sequence (seq2seq)
Models that generate a new sequence from an input, like translation models, or summarization models (such as
[Bart](model_doc/bart) or [T5](model_doc/t5)).
### Sharded DDP
Another name for the foundational [ZeRO](#zero-redundancy-optimizer--zero-) concept as used by various other implementations of ZeRO.
### stride
In [convolution](#convolution) or [pooling](#pooling), the stride refers to the distance the kernel is moved over a matrix. A stride of 1 means the kernel is moved one pixel over at a time, and a stride of 2 means the kernel is moved two pixels over at a time.
### supervised learning
A form of model training that directly uses labeled data to correct and instruct model performance. Data is fed into the model being trained, and its predictions are compared to the known labels. The model updates its weights based on how incorrect its predictions were, and the process is repeated to optimize model performance.
## T
### Tensor Parallelism (TP)
Parallelism technique for training on multiple GPUs in which each tensor is split up into multiple chunks, so instead of
having the whole tensor reside on a single GPU, each shard of the tensor resides on its designated GPU. Shards gets
processed separately and in parallel on different GPUs and the results are synced at the end of the processing step.
This is what is sometimes called horizontal parallelism, as the splitting happens on horizontal level.
Learn more about Tensor Parallelism [here](perf_train_gpu_many#tensor-parallelism).
### token
A part of a sentence, usually a word, but can also be a subword (non-common words are often split in subwords) or a
punctuation symbol.
### token Type IDs
Some models' purpose is to do classification on pairs of sentences or question answering.
<Youtube id="0u3ioSwev3s"/>
These require two different sequences to be joined in a single "input_ids" entry, which usually is performed with the
help of special tokens, such as the classifier (`[CLS]`) and separator (`[SEP]`) tokens. For example, the BERT model
builds its two sequence input as such:
```python
>>> # [CLS] SEQUENCE_A [SEP] SEQUENCE_B [SEP]
```
We can use our tokenizer to automatically generate such a sentence by passing the two sequences to `tokenizer` as two
arguments (and not a list, like before) like this:
```python
>>> from transformers import BertTokenizer
>>> tokenizer = BertTokenizer.from_pretrained("bert-base-cased")
>>> sequence_a = "HuggingFace is based in NYC"
>>> sequence_b = "Where is HuggingFace based?"
>>> encoded_dict = tokenizer(sequence_a, sequence_b)
>>> decoded = tokenizer.decode(encoded_dict["input_ids"])
```
which will return:
```python
>>> print(decoded)
[CLS] HuggingFace is based in NYC [SEP] Where is HuggingFace based? [SEP]
```
This is enough for some models to understand where one sequence ends and where another begins. However, other models,
such as BERT, also deploy token type IDs (also called segment IDs). They are represented as a binary mask identifying
the two types of sequence in the model.
The tokenizer returns this mask as the "token_type_ids" entry:
```python
>>> encoded_dict["token_type_ids"]
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1]
```
The first sequence, the "context" used for the question, has all its tokens represented by a `0`, whereas the second
sequence, corresponding to the "question", has all its tokens represented by a `1`.
Some models, like [`XLNetModel`] use an additional token represented by a `2`.
### transfer learning
A technique that involves taking a pretrained model and adapting it to a dataset specific to your task. Instead of training a model from scratch, you can leverage knowledge obtained from an existing model as a starting point. This speeds up the learning process and reduces the amount of training data needed.
### transformer
Self-attention based deep learning model architecture.
## U
### unsupervised learning
A form of model training in which data provided to the model is not labeled. Unsupervised learning techniques leverage statistical information of the data distribution to find patterns useful for the task at hand.
## Z
### Zero Redundancy Optimizer (ZeRO)
Parallelism technique which performs sharding of the tensors somewhat similar to [TensorParallel](#tensor-parallelism-tp),
except the whole tensor gets reconstructed in time for a forward or backward computation, therefore the model doesn't need
to be modified. This method also supports various offloading techniques to compensate for limited GPU memory.
Learn more about ZeRO [here](perf_train_gpu_many#zero-data-parallelism). | huggingface/transformers/blob/main/docs/source/en/glossary.md |
!---
Copyright 2021 The HuggingFace Team. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
-->
# Image classification examples
This directory contains 2 scripts that showcase how to fine-tune any model supported by the [`AutoModelForImageClassification` API](https://huggingface.co/docs/transformers/main/en/model_doc/auto#transformers.AutoModelForImageClassification) (such as [ViT](https://huggingface.co/docs/transformers/main/en/model_doc/vit), [ConvNeXT](https://huggingface.co/docs/transformers/main/en/model_doc/convnext), [ResNet](https://huggingface.co/docs/transformers/main/en/model_doc/resnet), [Swin Transformer](https://huggingface.co/docs/transformers/main/en/model_doc/swin)...) using PyTorch. They can be used to fine-tune models on both [datasets from the hub](#using-datasets-from-hub) as well as on [your own custom data](#using-your-own-data).
<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/image_classification_inference_widget.png" height="400" />
Try out the inference widget here: https://huggingface.co/google/vit-base-patch16-224
Content:
- [PyTorch version, Trainer](#pytorch-version-trainer)
- [PyTorch version, no Trainer](#pytorch-version-no-trainer)
## PyTorch version, Trainer
Based on the script [`run_image_classification.py`](https://github.com/huggingface/transformers/blob/main/examples/pytorch/image-classification/run_image_classification.py).
The script leverages the 🤗 [Trainer API](https://huggingface.co/docs/transformers/main_classes/trainer) to automatically take care of the training for you, running on distributed environments right away.
### Using datasets from Hub
Here we show how to fine-tune a Vision Transformer (`ViT`) on the [beans](https://huggingface.co/datasets/beans) dataset, to classify the disease type of bean leaves.
```bash
python run_image_classification.py \
--dataset_name beans \
--output_dir ./beans_outputs/ \
--remove_unused_columns False \
--do_train \
--do_eval \
--push_to_hub \
--push_to_hub_model_id vit-base-beans \
--learning_rate 2e-5 \
--num_train_epochs 5 \
--per_device_train_batch_size 8 \
--per_device_eval_batch_size 8 \
--logging_strategy steps \
--logging_steps 10 \
--evaluation_strategy epoch \
--save_strategy epoch \
--load_best_model_at_end True \
--save_total_limit 3 \
--seed 1337
```
👀 See the results here: [nateraw/vit-base-beans](https://huggingface.co/nateraw/vit-base-beans).
Note that you can replace the model and dataset by simply setting the `model_name_or_path` and `dataset_name` arguments respectively, with any model or dataset from the [hub](https://huggingface.co/). For an overview of all possible arguments, we refer to the [docs](https://huggingface.co/docs/transformers/main_classes/trainer#transformers.TrainingArguments) of the `TrainingArguments`, which can be passed as flags.
> If your model classification head dimensions do not fit the number of labels in the dataset, you can specify `--ignore_mismatched_sizes` to adapt it.
### Using your own data
To use your own dataset, there are 2 ways:
- you can either provide your own folders as `--train_dir` and/or `--validation_dir` arguments
- you can upload your dataset to the hub (possibly as a private repo, if you prefer so), and simply pass the `--dataset_name` argument.
Below, we explain both in more detail.
#### Provide them as folders
If you provide your own folders with images, the script expects the following directory structure:
```bash
root/dog/xxx.png
root/dog/xxy.png
root/dog/[...]/xxz.png
root/cat/123.png
root/cat/nsdf3.png
root/cat/[...]/asd932_.png
```
In other words, you need to organize your images in subfolders, based on their class. You can then run the script like this:
```bash
python run_image_classification.py \
--train_dir <path-to-train-root> \
--output_dir ./outputs/ \
--remove_unused_columns False \
--do_train \
--do_eval
```
Internally, the script will use the [`ImageFolder`](https://huggingface.co/docs/datasets/v2.0.0/en/image_process#imagefolder) feature which will automatically turn the folders into 🤗 Dataset objects.
##### 💡 The above will split the train dir into training and evaluation sets
- To control the split amount, use the `--train_val_split` flag.
- To provide your own validation split in its own directory, you can pass the `--validation_dir <path-to-val-root>` flag.
#### Upload your data to the hub, as a (possibly private) repo
It's very easy (and convenient) to upload your image dataset to the hub using the [`ImageFolder`](https://huggingface.co/docs/datasets/v2.0.0/en/image_process#imagefolder) feature available in 🤗 Datasets. Simply do the following:
```python
from datasets import load_dataset
# example 1: local folder
dataset = load_dataset("imagefolder", data_dir="path_to_your_folder")
# example 2: local files (suppoted formats are tar, gzip, zip, xz, rar, zstd)
dataset = load_dataset("imagefolder", data_files="path_to_zip_file")
# example 3: remote files (suppoted formats are tar, gzip, zip, xz, rar, zstd)
dataset = load_dataset("imagefolder", data_files="https://download.microsoft.com/download/3/E/1/3E1C3F21-ECDB-4869-8368-6DEBA77B919F/kagglecatsanddogs_3367a.zip")
# example 4: providing several splits
dataset = load_dataset("imagefolder", data_files={"train": ["path/to/file1", "path/to/file2"], "test": ["path/to/file3", "path/to/file4"]})
```
`ImageFolder` will create a `label` column, and the label name is based on the directory name.
Next, push it to the hub!
```python
# assuming you have ran the huggingface-cli login command in a terminal
dataset.push_to_hub("name_of_your_dataset")
# if you want to push to a private repo, simply pass private=True:
dataset.push_to_hub("name_of_your_dataset", private=True)
```
and that's it! You can now train your model by simply setting the `--dataset_name` argument to the name of your dataset on the hub (as explained in [Using datasets from the 🤗 hub](#using-datasets-from-hub)).
More on this can also be found in [this blog post](https://huggingface.co/blog/image-search-datasets).
### Sharing your model on 🤗 Hub
0. If you haven't already, [sign up](https://huggingface.co/join) for a 🤗 account
1. Make sure you have `git-lfs` installed and git set up.
```bash
$ apt install git-lfs
$ git config --global user.email "[email protected]"
$ git config --global user.name "Your Name"
```
2. Log in with your HuggingFace account credentials using `huggingface-cli`:
```bash
$ huggingface-cli login
# ...follow the prompts
```
3. When running the script, pass the following arguments:
```bash
python run_image_classification.py \
--push_to_hub \
--push_to_hub_model_id <name-your-model> \
...
```
## PyTorch version, no Trainer
Based on the script [`run_image_classification_no_trainer.py`](https://github.com/huggingface/transformers/blob/main/examples/pytorch/image-classification/run_image_classification_no_trainer.py).
Like `run_image_classification.py`, this script allows you to fine-tune any of the models on the [hub](https://huggingface.co/models) on an image classification task. The main difference is that this script exposes the bare training loop, to allow you to quickly experiment and add any customization you would like.
It offers less options than the script with `Trainer` (for instance you can easily change the options for the optimizer
or the dataloaders directly in the script) but still run in a distributed setup, and supports mixed precision by
the means of the [🤗 `Accelerate`](https://github.com/huggingface/accelerate) library. You can use the script normally
after installing it:
```bash
pip install git+https://github.com/huggingface/accelerate
```
You can then use your usual launchers to run in it in a distributed environment, but the easiest way is to run
```bash
accelerate config
```
and reply to the questions asked. Then
```bash
accelerate test
```
that will check everything is ready for training. Finally, you can launch training with
```bash
accelerate launch run_image_classification_trainer.py
```
This command is the same and will work for:
- single/multiple CPUs
- single/multiple GPUs
- TPUs
Note that this library is in alpha release so your feedback is more than welcome if you encounter any problem using it.
Regarding using custom data with this script, we refer to [using your own data](#using-your-own-data).
| huggingface/transformers/blob/main/examples/pytorch/image-classification/README.md |
!--Copyright 2023 The HuggingFace Team. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
the License. You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
specific language governing permissions and limitations under the License.
⚠️ Note that this file is in Markdown but contain specific syntax for our doc-builder (similar to MDX) that may not be
rendered properly in your Markdown viewer.
-->
# Text to speech
[[open-in-colab]]
Text-to-speech (TTS) is the task of creating natural-sounding speech from text, where the speech can be generated in multiple
languages and for multiple speakers. Several text-to-speech models are currently available in 🤗 Transformers, such as
[Bark](../model_doc/bark), [MMS](../model_doc/mms), [VITS](../model_doc/vits) and [SpeechT5](../model_doc/speecht5).
You can easily generate audio using the `"text-to-audio"` pipeline (or its alias - `"text-to-speech"`). Some models, like Bark,
can also be conditioned to generate non-verbal communications such as laughing, sighing and crying, or even add music.
Here's an example of how you would use the `"text-to-speech"` pipeline with Bark:
```py
>>> from transformers import pipeline
>>> pipe = pipeline("text-to-speech", model="suno/bark-small")
>>> text = "[clears throat] This is a test ... and I just took a long pause."
>>> output = pipe(text)
```
Here's a code snippet you can use to listen to the resulting audio in a notebook:
```python
>>> from IPython.display import Audio
>>> Audio(output["audio"], rate=output["sampling_rate"])
```
For more examples on what Bark and other pretrained TTS models can do, refer to our
[Audio course](https://huggingface.co/learn/audio-course/chapter6/pre-trained_models).
If you are looking to fine-tune a TTS model, you can currently fine-tune SpeechT5 only. SpeechT5 is pre-trained on a combination of
speech-to-text and text-to-speech data, allowing it to learn a unified space of hidden representations shared by both text
and speech. This means that the same pre-trained model can be fine-tuned for different tasks. Furthermore, SpeechT5
supports multiple speakers through x-vector speaker embeddings.
The remainder of this guide illustrates how to:
1. Fine-tune [SpeechT5](../model_doc/speecht5) that was originally trained on English speech on the Dutch (`nl`) language subset of the [VoxPopuli](https://huggingface.co/datasets/facebook/voxpopuli) dataset.
2. Use your refined model for inference in one of two ways: using a pipeline or directly.
Before you begin, make sure you have all the necessary libraries installed:
```bash
pip install datasets soundfile speechbrain accelerate
```
Install 🤗Transformers from source as not all the SpeechT5 features have been merged into an official release yet:
```bash
pip install git+https://github.com/huggingface/transformers.git
```
<Tip>
To follow this guide you will need a GPU. If you're working in a notebook, run the following line to check if a GPU is available:
```bash
!nvidia-smi
```
or alternatively for AMD GPUs:
```bash
!rocm-smi
```
</Tip>
We encourage you to log in to your Hugging Face account to upload and share your model with the community. When prompted, enter your token to log in:
```py
>>> from huggingface_hub import notebook_login
>>> notebook_login()
```
## Load the dataset
[VoxPopuli](https://huggingface.co/datasets/facebook/voxpopuli) is a large-scale multilingual speech corpus consisting of
data sourced from 2009-2020 European Parliament event recordings. It contains labelled audio-transcription data for 15
European languages. In this guide, we are using the Dutch language subset, feel free to pick another subset.
Note that VoxPopuli or any other automated speech recognition (ASR) dataset may not be the most suitable
option for training TTS models. The features that make it beneficial for ASR, such as excessive background noise, are
typically undesirable in TTS. However, finding top-quality, multilingual, and multi-speaker TTS datasets can be quite
challenging.
Let's load the data:
```py
>>> from datasets import load_dataset, Audio
>>> dataset = load_dataset("facebook/voxpopuli", "nl", split="train")
>>> len(dataset)
20968
```
20968 examples should be sufficient for fine-tuning. SpeechT5 expects audio data to have a sampling rate of 16 kHz, so
make sure the examples in the dataset meet this requirement:
```py
dataset = dataset.cast_column("audio", Audio(sampling_rate=16000))
```
## Preprocess the data
Let's begin by defining the model checkpoint to use and loading the appropriate processor:
```py
>>> from transformers import SpeechT5Processor
>>> checkpoint = "microsoft/speecht5_tts"
>>> processor = SpeechT5Processor.from_pretrained(checkpoint)
```
### Text cleanup for SpeechT5 tokenization
Start by cleaning up the text data. You'll need the tokenizer part of the processor to process the text:
```py
>>> tokenizer = processor.tokenizer
```
The dataset examples contain `raw_text` and `normalized_text` features. When deciding which feature to use as the text input,
consider that the SpeechT5 tokenizer doesn't have any tokens for numbers. In `normalized_text` the numbers are written
out as text. Thus, it is a better fit, and we recommend using `normalized_text` as input text.
Because SpeechT5 was trained on the English language, it may not recognize certain characters in the Dutch dataset. If
left as is, these characters will be converted to `<unk>` tokens. However, in Dutch, certain characters like `à` are
used to stress syllables. In order to preserve the meaning of the text, we can replace this character with a regular `a`.
To identify unsupported tokens, extract all unique characters in the dataset using the `SpeechT5Tokenizer` which
works with characters as tokens. To do this, write the `extract_all_chars` mapping function that concatenates
the transcriptions from all examples into one string and converts it to a set of characters.
Make sure to set `batched=True` and `batch_size=-1` in `dataset.map()` so that all transcriptions are available at once for
the mapping function.
```py
>>> def extract_all_chars(batch):
... all_text = " ".join(batch["normalized_text"])
... vocab = list(set(all_text))
... return {"vocab": [vocab], "all_text": [all_text]}
>>> vocabs = dataset.map(
... extract_all_chars,
... batched=True,
... batch_size=-1,
... keep_in_memory=True,
... remove_columns=dataset.column_names,
... )
>>> dataset_vocab = set(vocabs["vocab"][0])
>>> tokenizer_vocab = {k for k, _ in tokenizer.get_vocab().items()}
```
Now you have two sets of characters: one with the vocabulary from the dataset and one with the vocabulary from the tokenizer.
To identify any unsupported characters in the dataset, you can take the difference between these two sets. The resulting
set will contain the characters that are in the dataset but not in the tokenizer.
```py
>>> dataset_vocab - tokenizer_vocab
{' ', 'à', 'ç', 'è', 'ë', 'í', 'ï', 'ö', 'ü'}
```
To handle the unsupported characters identified in the previous step, define a function that maps these characters to
valid tokens. Note that spaces are already replaced by `▁` in the tokenizer and don't need to be handled separately.
```py
>>> replacements = [
... ("à", "a"),
... ("ç", "c"),
... ("è", "e"),
... ("ë", "e"),
... ("í", "i"),
... ("ï", "i"),
... ("ö", "o"),
... ("ü", "u"),
... ]
>>> def cleanup_text(inputs):
... for src, dst in replacements:
... inputs["normalized_text"] = inputs["normalized_text"].replace(src, dst)
... return inputs
>>> dataset = dataset.map(cleanup_text)
```
Now that you have dealt with special characters in the text, it's time to shift focus to the audio data.
### Speakers
The VoxPopuli dataset includes speech from multiple speakers, but how many speakers are represented in the dataset? To
determine this, we can count the number of unique speakers and the number of examples each speaker contributes to the dataset.
With a total of 20,968 examples in the dataset, this information will give us a better understanding of the distribution of
speakers and examples in the data.
```py
>>> from collections import defaultdict
>>> speaker_counts = defaultdict(int)
>>> for speaker_id in dataset["speaker_id"]:
... speaker_counts[speaker_id] += 1
```
By plotting a histogram you can get a sense of how much data there is for each speaker.
```py
>>> import matplotlib.pyplot as plt
>>> plt.figure()
>>> plt.hist(speaker_counts.values(), bins=20)
>>> plt.ylabel("Speakers")
>>> plt.xlabel("Examples")
>>> plt.show()
```
<div class="flex justify-center">
<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/tts_speakers_histogram.png" alt="Speakers histogram"/>
</div>
The histogram reveals that approximately one-third of the speakers in the dataset have fewer than 100 examples, while
around ten speakers have more than 500 examples. To improve training efficiency and balance the dataset, we can limit
the data to speakers with between 100 and 400 examples.
```py
>>> def select_speaker(speaker_id):
... return 100 <= speaker_counts[speaker_id] <= 400
>>> dataset = dataset.filter(select_speaker, input_columns=["speaker_id"])
```
Let's check how many speakers remain:
```py
>>> len(set(dataset["speaker_id"]))
42
```
Let's see how many examples are left:
```py
>>> len(dataset)
9973
```
You are left with just under 10,000 examples from approximately 40 unique speakers, which should be sufficient.
Note that some speakers with few examples may actually have more audio available if the examples are long. However,
determining the total amount of audio for each speaker requires scanning through the entire dataset, which is a
time-consuming process that involves loading and decoding each audio file. As such, we have chosen to skip this step here.
### Speaker embeddings
To enable the TTS model to differentiate between multiple speakers, you'll need to create a speaker embedding for each example.
The speaker embedding is an additional input into the model that captures a particular speaker's voice characteristics.
To generate these speaker embeddings, use the pre-trained [spkrec-xvect-voxceleb](https://huggingface.co/speechbrain/spkrec-xvect-voxceleb)
model from SpeechBrain.
Create a function `create_speaker_embedding()` that takes an input audio waveform and outputs a 512-element vector
containing the corresponding speaker embedding.
```py
>>> import os
>>> import torch
>>> from speechbrain.pretrained import EncoderClassifier
>>> spk_model_name = "speechbrain/spkrec-xvect-voxceleb"
>>> device = "cuda" if torch.cuda.is_available() else "cpu"
>>> speaker_model = EncoderClassifier.from_hparams(
... source=spk_model_name,
... run_opts={"device": device},
... savedir=os.path.join("/tmp", spk_model_name),
... )
>>> def create_speaker_embedding(waveform):
... with torch.no_grad():
... speaker_embeddings = speaker_model.encode_batch(torch.tensor(waveform))
... speaker_embeddings = torch.nn.functional.normalize(speaker_embeddings, dim=2)
... speaker_embeddings = speaker_embeddings.squeeze().cpu().numpy()
... return speaker_embeddings
```
It's important to note that the `speechbrain/spkrec-xvect-voxceleb` model was trained on English speech from the VoxCeleb
dataset, whereas the training examples in this guide are in Dutch. While we believe that this model will still generate
reasonable speaker embeddings for our Dutch dataset, this assumption may not hold true in all cases.
For optimal results, we recommend training an X-vector model on the target speech first. This will ensure that the model
is better able to capture the unique voice characteristics present in the Dutch language.
### Processing the dataset
Finally, let's process the data into the format the model expects. Create a `prepare_dataset` function that takes in a
single example and uses the `SpeechT5Processor` object to tokenize the input text and load the target audio into a log-mel spectrogram.
It should also add the speaker embeddings as an additional input.
```py
>>> def prepare_dataset(example):
... audio = example["audio"]
... example = processor(
... text=example["normalized_text"],
... audio_target=audio["array"],
... sampling_rate=audio["sampling_rate"],
... return_attention_mask=False,
... )
... # strip off the batch dimension
... example["labels"] = example["labels"][0]
... # use SpeechBrain to obtain x-vector
... example["speaker_embeddings"] = create_speaker_embedding(audio["array"])
... return example
```
Verify the processing is correct by looking at a single example:
```py
>>> processed_example = prepare_dataset(dataset[0])
>>> list(processed_example.keys())
['input_ids', 'labels', 'stop_labels', 'speaker_embeddings']
```
Speaker embeddings should be a 512-element vector:
```py
>>> processed_example["speaker_embeddings"].shape
(512,)
```
The labels should be a log-mel spectrogram with 80 mel bins.
```py
>>> import matplotlib.pyplot as plt
>>> plt.figure()
>>> plt.imshow(processed_example["labels"].T)
>>> plt.show()
```
<div class="flex justify-center">
<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/tts_logmelspectrogram_1.png" alt="Log-mel spectrogram with 80 mel bins"/>
</div>
Side note: If you find this spectrogram confusing, it may be due to your familiarity with the convention of placing low frequencies
at the bottom and high frequencies at the top of a plot. However, when plotting spectrograms as an image using the matplotlib library,
the y-axis is flipped and the spectrograms appear upside down.
Now apply the processing function to the entire dataset. This will take between 5 and 10 minutes.
```py
>>> dataset = dataset.map(prepare_dataset, remove_columns=dataset.column_names)
```
You'll see a warning saying that some examples in the dataset are longer than the maximum input length the model can handle (600 tokens).
Remove those examples from the dataset. Here we go even further and to allow for larger batch sizes we remove anything over 200 tokens.
```py
>>> def is_not_too_long(input_ids):
... input_length = len(input_ids)
... return input_length < 200
>>> dataset = dataset.filter(is_not_too_long, input_columns=["input_ids"])
>>> len(dataset)
8259
```
Next, create a basic train/test split:
```py
>>> dataset = dataset.train_test_split(test_size=0.1)
```
### Data collator
In order to combine multiple examples into a batch, you need to define a custom data collator. This collator will pad shorter sequences with padding
tokens, ensuring that all examples have the same length. For the spectrogram labels, the padded portions are replaced with the special value `-100`. This special value
instructs the model to ignore that part of the spectrogram when calculating the spectrogram loss.
```py
>>> from dataclasses import dataclass
>>> from typing import Any, Dict, List, Union
>>> @dataclass
... class TTSDataCollatorWithPadding:
... processor: Any
... def __call__(self, features: List[Dict[str, Union[List[int], torch.Tensor]]]) -> Dict[str, torch.Tensor]:
... input_ids = [{"input_ids": feature["input_ids"]} for feature in features]
... label_features = [{"input_values": feature["labels"]} for feature in features]
... speaker_features = [feature["speaker_embeddings"] for feature in features]
... # collate the inputs and targets into a batch
... batch = processor.pad(input_ids=input_ids, labels=label_features, return_tensors="pt")
... # replace padding with -100 to ignore loss correctly
... batch["labels"] = batch["labels"].masked_fill(batch.decoder_attention_mask.unsqueeze(-1).ne(1), -100)
... # not used during fine-tuning
... del batch["decoder_attention_mask"]
... # round down target lengths to multiple of reduction factor
... if model.config.reduction_factor > 1:
... target_lengths = torch.tensor([len(feature["input_values"]) for feature in label_features])
... target_lengths = target_lengths.new(
... [length - length % model.config.reduction_factor for length in target_lengths]
... )
... max_length = max(target_lengths)
... batch["labels"] = batch["labels"][:, :max_length]
... # also add in the speaker embeddings
... batch["speaker_embeddings"] = torch.tensor(speaker_features)
... return batch
```
In SpeechT5, the input to the decoder part of the model is reduced by a factor 2. In other words, it throws away every
other timestep from the target sequence. The decoder then predicts a sequence that is twice as long. Since the original
target sequence length may be odd, the data collator makes sure to round the maximum length of the batch down to be a
multiple of 2.
```py
>>> data_collator = TTSDataCollatorWithPadding(processor=processor)
```
## Train the model
Load the pre-trained model from the same checkpoint as you used for loading the processor:
```py
>>> from transformers import SpeechT5ForTextToSpeech
>>> model = SpeechT5ForTextToSpeech.from_pretrained(checkpoint)
```
The `use_cache=True` option is incompatible with gradient checkpointing. Disable it for training.
```py
>>> model.config.use_cache = False
```
Define the training arguments. Here we are not computing any evaluation metrics during the training process. Instead, we'll
only look at the loss:
```python
>>> from transformers import Seq2SeqTrainingArguments
>>> training_args = Seq2SeqTrainingArguments(
... output_dir="speecht5_finetuned_voxpopuli_nl", # change to a repo name of your choice
... per_device_train_batch_size=4,
... gradient_accumulation_steps=8,
... learning_rate=1e-5,
... warmup_steps=500,
... max_steps=4000,
... gradient_checkpointing=True,
... fp16=True,
... evaluation_strategy="steps",
... per_device_eval_batch_size=2,
... save_steps=1000,
... eval_steps=1000,
... logging_steps=25,
... report_to=["tensorboard"],
... load_best_model_at_end=True,
... greater_is_better=False,
... label_names=["labels"],
... push_to_hub=True,
... )
```
Instantiate the `Trainer` object and pass the model, dataset, and data collator to it.
```py
>>> from transformers import Seq2SeqTrainer
>>> trainer = Seq2SeqTrainer(
... args=training_args,
... model=model,
... train_dataset=dataset["train"],
... eval_dataset=dataset["test"],
... data_collator=data_collator,
... tokenizer=processor,
... )
```
And with that, you're ready to start training! Training will take several hours. Depending on your GPU,
it is possible that you will encounter a CUDA "out-of-memory" error when you start training. In this case, you can reduce
the `per_device_train_batch_size` incrementally by factors of 2 and increase `gradient_accumulation_steps` by 2x to compensate.
```py
>>> trainer.train()
```
To be able to use your checkpoint with a pipeline, make sure to save the processor with the checkpoint:
```py
>>> processor.save_pretrained("YOUR_ACCOUNT_NAME/speecht5_finetuned_voxpopuli_nl")
```
Push the final model to the 🤗 Hub:
```py
>>> trainer.push_to_hub()
```
## Inference
### Inference with a pipeline
Great, now that you've fine-tuned a model, you can use it for inference!
First, let's see how you can use it with a corresponding pipeline. Let's create a `"text-to-speech"` pipeline with your
checkpoint:
```py
>>> from transformers import pipeline
>>> pipe = pipeline("text-to-speech", model="YOUR_ACCOUNT_NAME/speecht5_finetuned_voxpopuli_nl")
```
Pick a piece of text in Dutch you'd like narrated, e.g.:
```py
>>> text = "hallo allemaal, ik praat nederlands. groetjes aan iedereen!"
```
To use SpeechT5 with the pipeline, you'll need a speaker embedding. Let's get it from an example in the test dataset:
```py
>>> example = dataset["test"][304]
>>> speaker_embeddings = torch.tensor(example["speaker_embeddings"]).unsqueeze(0)
```
Now you can pass the text and speaker embeddings to the pipeline, and it will take care of the rest:
```py
>>> forward_params = {"speaker_embeddings": speaker_embeddings}
>>> output = pipe(text, forward_params=forward_params)
>>> output
{'audio': array([-6.82714235e-05, -4.26525949e-04, 1.06134125e-04, ...,
-1.22392643e-03, -7.76011671e-04, 3.29112721e-04], dtype=float32),
'sampling_rate': 16000}
```
You can then listen to the result:
```py
>>> from IPython.display import Audio
>>> Audio(output['audio'], rate=output['sampling_rate'])
```
### Run inference manually
You can achieve the same inference results without using the pipeline, however, more steps will be required.
Load the model from the 🤗 Hub:
```py
>>> model = SpeechT5ForTextToSpeech.from_pretrained("YOUR_ACCOUNT/speecht5_finetuned_voxpopuli_nl")
```
Pick an example from the test dataset obtain a speaker embedding.
```py
>>> example = dataset["test"][304]
>>> speaker_embeddings = torch.tensor(example["speaker_embeddings"]).unsqueeze(0)
```
Define the input text and tokenize it.
```py
>>> text = "hallo allemaal, ik praat nederlands. groetjes aan iedereen!"
>>> inputs = processor(text=text, return_tensors="pt")
```
Create a spectrogram with your model:
```py
>>> spectrogram = model.generate_speech(inputs["input_ids"], speaker_embeddings)
```
Visualize the spectrogram, if you'd like to:
```py
>>> plt.figure()
>>> plt.imshow(spectrogram.T)
>>> plt.show()
```
<div class="flex justify-center">
<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/tts_logmelspectrogram_2.png" alt="Generated log-mel spectrogram"/>
</div>
Finally, use the vocoder to turn the spectrogram into sound.
```py
>>> with torch.no_grad():
... speech = vocoder(spectrogram)
>>> from IPython.display import Audio
>>> Audio(speech.numpy(), rate=16000)
```
In our experience, obtaining satisfactory results from this model can be challenging. The quality of the speaker
embeddings appears to be a significant factor. Since SpeechT5 was pre-trained with English x-vectors, it performs best
when using English speaker embeddings. If the synthesized speech sounds poor, try using a different speaker embedding.
Increasing the training duration is also likely to enhance the quality of the results. Even so, the speech clearly is Dutch instead of English, and it does
capture the voice characteristics of the speaker (compare to the original audio in the example).
Another thing to experiment with is the model's configuration. For example, try using `config.reduction_factor = 1` to
see if this improves the results.
Finally, it is essential to consider ethical considerations. Although TTS technology has numerous useful applications, it
may also be used for malicious purposes, such as impersonating someone's voice without their knowledge or consent. Please
use TTS judiciously and responsibly.
| huggingface/transformers/blob/main/docs/source/en/tasks/text-to-speech.md |
!--Copyright 2021 The HuggingFace Team. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
the License. You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
specific language governing permissions and limitations under the License.
⚠️ Note that this file is in Markdown but contain specific syntax for our doc-builder (similar to MDX) that may not be
rendered properly in your Markdown viewer.
-->
# UniSpeech-SAT
## Overview
The UniSpeech-SAT model was proposed in [UniSpeech-SAT: Universal Speech Representation Learning with Speaker Aware
Pre-Training](https://arxiv.org/abs/2110.05752) by Sanyuan Chen, Yu Wu, Chengyi Wang, Zhengyang Chen, Zhuo Chen,
Shujie Liu, Jian Wu, Yao Qian, Furu Wei, Jinyu Li, Xiangzhan Yu .
The abstract from the paper is the following:
*Self-supervised learning (SSL) is a long-standing goal for speech processing, since it utilizes large-scale unlabeled
data and avoids extensive human labeling. Recent years witness great successes in applying self-supervised learning in
speech recognition, while limited exploration was attempted in applying SSL for modeling speaker characteristics. In
this paper, we aim to improve the existing SSL framework for speaker representation learning. Two methods are
introduced for enhancing the unsupervised speaker information extraction. First, we apply the multi-task learning to
the current SSL framework, where we integrate the utterance-wise contrastive loss with the SSL objective function.
Second, for better speaker discrimination, we propose an utterance mixing strategy for data augmentation, where
additional overlapped utterances are created unsupervisely and incorporate during training. We integrate the proposed
methods into the HuBERT framework. Experiment results on SUPERB benchmark show that the proposed system achieves
state-of-the-art performance in universal representation learning, especially for speaker identification oriented
tasks. An ablation study is performed verifying the efficacy of each proposed method. Finally, we scale up training
dataset to 94 thousand hours public audio data and achieve further performance improvement in all SUPERB tasks.*
This model was contributed by [patrickvonplaten](https://huggingface.co/patrickvonplaten). The Authors' code can be
found [here](https://github.com/microsoft/UniSpeech/tree/main/UniSpeech-SAT).
## Usage tips
- UniSpeechSat is a speech model that accepts a float array corresponding to the raw waveform of the speech signal.
Please use [`Wav2Vec2Processor`] for the feature extraction.
- UniSpeechSat model can be fine-tuned using connectionist temporal classification (CTC) so the model output has to be
decoded using [`Wav2Vec2CTCTokenizer`].
- UniSpeechSat performs especially well on speaker verification, speaker identification, and speaker diarization tasks.
## Resources
- [Audio classification task guide](../tasks/audio_classification)
- [Automatic speech recognition task guide](../tasks/asr)
## UniSpeechSatConfig
[[autodoc]] UniSpeechSatConfig
## UniSpeechSat specific outputs
[[autodoc]] models.unispeech_sat.modeling_unispeech_sat.UniSpeechSatForPreTrainingOutput
## UniSpeechSatModel
[[autodoc]] UniSpeechSatModel
- forward
## UniSpeechSatForCTC
[[autodoc]] UniSpeechSatForCTC
- forward
## UniSpeechSatForSequenceClassification
[[autodoc]] UniSpeechSatForSequenceClassification
- forward
## UniSpeechSatForAudioFrameClassification
[[autodoc]] UniSpeechSatForAudioFrameClassification
- forward
## UniSpeechSatForXVector
[[autodoc]] UniSpeechSatForXVector
- forward
## UniSpeechSatForPreTraining
[[autodoc]] UniSpeechSatForPreTraining
- forward
| huggingface/transformers/blob/main/docs/source/en/model_doc/unispeech-sat.md |
!--Copyright 2020 The HuggingFace Team. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
the License. You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
specific language governing permissions and limitations under the License.
⚠️ Note that this file is in Markdown but contain specific syntax for our doc-builder (similar to MDX) that may not be
rendered properly in your Markdown viewer.
-->
# TAPAS
## Overview
The TAPAS model was proposed in [TAPAS: Weakly Supervised Table Parsing via Pre-training](https://www.aclweb.org/anthology/2020.acl-main.398)
by Jonathan Herzig, Paweł Krzysztof Nowak, Thomas Müller, Francesco Piccinno and Julian Martin Eisenschlos. It's a BERT-based model specifically
designed (and pre-trained) for answering questions about tabular data. Compared to BERT, TAPAS uses relative position embeddings and has 7
token types that encode tabular structure. TAPAS is pre-trained on the masked language modeling (MLM) objective on a large dataset comprising
millions of tables from English Wikipedia and corresponding texts.
For question answering, TAPAS has 2 heads on top: a cell selection head and an aggregation head, for (optionally) performing aggregations (such as counting or summing) among selected cells. TAPAS has been fine-tuned on several datasets:
- [SQA](https://www.microsoft.com/en-us/download/details.aspx?id=54253) (Sequential Question Answering by Microsoft)
- [WTQ](https://github.com/ppasupat/WikiTableQuestions) (Wiki Table Questions by Stanford University)
- [WikiSQL](https://github.com/salesforce/WikiSQL) (by Salesforce).
It achieves state-of-the-art on both SQA and WTQ, while having comparable performance to SOTA on WikiSQL, with a much simpler architecture.
The abstract from the paper is the following:
*Answering natural language questions over tables is usually seen as a semantic parsing task. To alleviate the collection cost of full logical forms, one popular approach focuses on weak supervision consisting of denotations instead of logical forms. However, training semantic parsers from weak supervision poses difficulties, and in addition, the generated logical forms are only used as an intermediate step prior to retrieving the denotation. In this paper, we present TAPAS, an approach to question answering over tables without generating logical forms. TAPAS trains from weak supervision, and predicts the denotation by selecting table cells and optionally applying a corresponding aggregation operator to such selection. TAPAS extends BERT's architecture to encode tables as input, initializes from an effective joint pre-training of text segments and tables crawled from Wikipedia, and is trained end-to-end. We experiment with three different semantic parsing datasets, and find that TAPAS outperforms or rivals semantic parsing models by improving state-of-the-art accuracy on SQA from 55.1 to 67.2 and performing on par with the state-of-the-art on WIKISQL and WIKITQ, but with a simpler model architecture. We additionally find that transfer learning, which is trivial in our setting, from WIKISQL to WIKITQ, yields 48.7 accuracy, 4.2 points above the state-of-the-art.*
In addition, the authors have further pre-trained TAPAS to recognize **table entailment**, by creating a balanced dataset of millions of automatically created training examples which are learned in an intermediate step prior to fine-tuning. The authors of TAPAS call this further pre-training intermediate pre-training (since TAPAS is first pre-trained on MLM, and then on another dataset). They found that intermediate pre-training further improves performance on SQA, achieving a new state-of-the-art as well as state-of-the-art on [TabFact](https://github.com/wenhuchen/Table-Fact-Checking), a large-scale dataset with 16k Wikipedia tables for table entailment (a binary classification task). For more details, see their follow-up paper: [Understanding tables with intermediate pre-training](https://www.aclweb.org/anthology/2020.findings-emnlp.27/) by Julian Martin Eisenschlos, Syrine Krichene and Thomas Müller.
<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/tapas_architecture.png"
alt="drawing" width="600"/>
<small> TAPAS architecture. Taken from the <a href="https://ai.googleblog.com/2020/04/using-neural-networks-to-find-answers.html">original blog post</a>.</small>
This model was contributed by [nielsr](https://huggingface.co/nielsr). The Tensorflow version of this model was contributed by [kamalkraj](https://huggingface.co/kamalkraj). The original code can be found [here](https://github.com/google-research/tapas).
## Usage tips
- TAPAS is a model that uses relative position embeddings by default (restarting the position embeddings at every cell of the table). Note that this is something that was added after the publication of the original TAPAS paper. According to the authors, this usually results in a slightly better performance, and allows you to encode longer sequences without running out of embeddings. This is reflected in the `reset_position_index_per_cell` parameter of [`TapasConfig`], which is set to `True` by default. The default versions of the models available on the [hub](https://huggingface.co/models?search=tapas) all use relative position embeddings. You can still use the ones with absolute position embeddings by passing in an additional argument `revision="no_reset"` when calling the `from_pretrained()` method. Note that it's usually advised to pad the inputs on the right rather than the left.
- TAPAS is based on BERT, so `TAPAS-base` for example corresponds to a `BERT-base` architecture. Of course, `TAPAS-large` will result in the best performance (the results reported in the paper are from `TAPAS-large`). Results of the various sized models are shown on the [original GitHub repository](https://github.com/google-research/tapas).
- TAPAS has checkpoints fine-tuned on SQA, which are capable of answering questions related to a table in a conversational set-up. This means that you can ask follow-up questions such as "what is his age?" related to the previous question. Note that the forward pass of TAPAS is a bit different in case of a conversational set-up: in that case, you have to feed every table-question pair one by one to the model, such that the `prev_labels` token type ids can be overwritten by the predicted `labels` of the model to the previous question. See "Usage" section for more info.
- TAPAS is similar to BERT and therefore relies on the masked language modeling (MLM) objective. It is therefore efficient at predicting masked tokens and at NLU in general, but is not optimal for text generation. Models trained with a causal language modeling (CLM) objective are better in that regard. Note that TAPAS can be used as an encoder in the EncoderDecoderModel framework, to combine it with an autoregressive text decoder such as GPT-2.
## Usage: fine-tuning
Here we explain how you can fine-tune [`TapasForQuestionAnswering`] on your own dataset.
**STEP 1: Choose one of the 3 ways in which you can use TAPAS - or experiment**
Basically, there are 3 different ways in which one can fine-tune [`TapasForQuestionAnswering`], corresponding to the different datasets on which Tapas was fine-tuned:
1. SQA: if you're interested in asking follow-up questions related to a table, in a conversational set-up. For example if you first ask "what's the name of the first actor?" then you can ask a follow-up question such as "how old is he?". Here, questions do not involve any aggregation (all questions are cell selection questions).
2. WTQ: if you're not interested in asking questions in a conversational set-up, but rather just asking questions related to a table, which might involve aggregation, such as counting a number of rows, summing up cell values or averaging cell values. You can then for example ask "what's the total number of goals Cristiano Ronaldo made in his career?". This case is also called **weak supervision**, since the model itself must learn the appropriate aggregation operator (SUM/COUNT/AVERAGE/NONE) given only the answer to the question as supervision.
3. WikiSQL-supervised: this dataset is based on WikiSQL with the model being given the ground truth aggregation operator during training. This is also called **strong supervision**. Here, learning the appropriate aggregation operator is much easier.
To summarize:
| **Task** | **Example dataset** | **Description** |
|-------------------------------------|---------------------|---------------------------------------------------------------------------------------------------------|
| Conversational | SQA | Conversational, only cell selection questions |
| Weak supervision for aggregation | WTQ | Questions might involve aggregation, and the model must learn this given only the answer as supervision |
| Strong supervision for aggregation | WikiSQL-supervised | Questions might involve aggregation, and the model must learn this given the gold aggregation operator |
<frameworkcontent>
<pt>
Initializing a model with a pre-trained base and randomly initialized classification heads from the hub can be done as shown below.
```py
>>> from transformers import TapasConfig, TapasForQuestionAnswering
>>> # for example, the base sized model with default SQA configuration
>>> model = TapasForQuestionAnswering.from_pretrained("google/tapas-base")
>>> # or, the base sized model with WTQ configuration
>>> config = TapasConfig.from_pretrained("google/tapas-base-finetuned-wtq")
>>> model = TapasForQuestionAnswering.from_pretrained("google/tapas-base", config=config)
>>> # or, the base sized model with WikiSQL configuration
>>> config = TapasConfig("google-base-finetuned-wikisql-supervised")
>>> model = TapasForQuestionAnswering.from_pretrained("google/tapas-base", config=config)
```
Of course, you don't necessarily have to follow one of these three ways in which TAPAS was fine-tuned. You can also experiment by defining any hyperparameters you want when initializing [`TapasConfig`], and then create a [`TapasForQuestionAnswering`] based on that configuration. For example, if you have a dataset that has both conversational questions and questions that might involve aggregation, then you can do it this way. Here's an example:
```py
>>> from transformers import TapasConfig, TapasForQuestionAnswering
>>> # you can initialize the classification heads any way you want (see docs of TapasConfig)
>>> config = TapasConfig(num_aggregation_labels=3, average_logits_per_cell=True)
>>> # initializing the pre-trained base sized model with our custom classification heads
>>> model = TapasForQuestionAnswering.from_pretrained("google/tapas-base", config=config)
```
</pt>
<tf>
Initializing a model with a pre-trained base and randomly initialized classification heads from the hub can be done as shown below. Be sure to have installed the [tensorflow_probability](https://github.com/tensorflow/probability) dependency:
```py
>>> from transformers import TapasConfig, TFTapasForQuestionAnswering
>>> # for example, the base sized model with default SQA configuration
>>> model = TFTapasForQuestionAnswering.from_pretrained("google/tapas-base")
>>> # or, the base sized model with WTQ configuration
>>> config = TapasConfig.from_pretrained("google/tapas-base-finetuned-wtq")
>>> model = TFTapasForQuestionAnswering.from_pretrained("google/tapas-base", config=config)
>>> # or, the base sized model with WikiSQL configuration
>>> config = TapasConfig("google-base-finetuned-wikisql-supervised")
>>> model = TFTapasForQuestionAnswering.from_pretrained("google/tapas-base", config=config)
```
Of course, you don't necessarily have to follow one of these three ways in which TAPAS was fine-tuned. You can also experiment by defining any hyperparameters you want when initializing [`TapasConfig`], and then create a [`TFTapasForQuestionAnswering`] based on that configuration. For example, if you have a dataset that has both conversational questions and questions that might involve aggregation, then you can do it this way. Here's an example:
```py
>>> from transformers import TapasConfig, TFTapasForQuestionAnswering
>>> # you can initialize the classification heads any way you want (see docs of TapasConfig)
>>> config = TapasConfig(num_aggregation_labels=3, average_logits_per_cell=True)
>>> # initializing the pre-trained base sized model with our custom classification heads
>>> model = TFTapasForQuestionAnswering.from_pretrained("google/tapas-base", config=config)
```
</tf>
</frameworkcontent>
What you can also do is start from an already fine-tuned checkpoint. A note here is that the already fine-tuned checkpoint on WTQ has some issues due to the L2-loss which is somewhat brittle. See [here](https://github.com/google-research/tapas/issues/91#issuecomment-735719340) for more info.
For a list of all pre-trained and fine-tuned TAPAS checkpoints available on HuggingFace's hub, see [here](https://huggingface.co/models?search=tapas).
**STEP 2: Prepare your data in the SQA format**
Second, no matter what you picked above, you should prepare your dataset in the [SQA](https://www.microsoft.com/en-us/download/details.aspx?id=54253) format. This format is a TSV/CSV file with the following columns:
- `id`: optional, id of the table-question pair, for bookkeeping purposes.
- `annotator`: optional, id of the person who annotated the table-question pair, for bookkeeping purposes.
- `position`: integer indicating if the question is the first, second, third,... related to the table. Only required in case of conversational setup (SQA). You don't need this column in case you're going for WTQ/WikiSQL-supervised.
- `question`: string
- `table_file`: string, name of a csv file containing the tabular data
- `answer_coordinates`: list of one or more tuples (each tuple being a cell coordinate, i.e. row, column pair that is part of the answer)
- `answer_text`: list of one or more strings (each string being a cell value that is part of the answer)
- `aggregation_label`: index of the aggregation operator. Only required in case of strong supervision for aggregation (the WikiSQL-supervised case)
- `float_answer`: the float answer to the question, if there is one (np.nan if there isn't). Only required in case of weak supervision for aggregation (such as WTQ and WikiSQL)
The tables themselves should be present in a folder, each table being a separate csv file. Note that the authors of the TAPAS algorithm used conversion scripts with some automated logic to convert the other datasets (WTQ, WikiSQL) into the SQA format. The author explains this [here](https://github.com/google-research/tapas/issues/50#issuecomment-705465960). A conversion of this script that works with HuggingFace's implementation can be found [here](https://github.com/NielsRogge/tapas_utils). Interestingly, these conversion scripts are not perfect (the `answer_coordinates` and `float_answer` fields are populated based on the `answer_text`), meaning that WTQ and WikiSQL results could actually be improved.
**STEP 3: Convert your data into tensors using TapasTokenizer**
<frameworkcontent>
<pt>
Third, given that you've prepared your data in this TSV/CSV format (and corresponding CSV files containing the tabular data), you can then use [`TapasTokenizer`] to convert table-question pairs into `input_ids`, `attention_mask`, `token_type_ids` and so on. Again, based on which of the three cases you picked above, [`TapasForQuestionAnswering`] requires different
inputs to be fine-tuned:
| **Task** | **Required inputs** |
|------------------------------------|---------------------------------------------------------------------------------------------------------------------|
| Conversational | `input_ids`, `attention_mask`, `token_type_ids`, `labels` |
| Weak supervision for aggregation | `input_ids`, `attention_mask`, `token_type_ids`, `labels`, `numeric_values`, `numeric_values_scale`, `float_answer` |
| Strong supervision for aggregation | `input ids`, `attention mask`, `token type ids`, `labels`, `aggregation_labels` |
[`TapasTokenizer`] creates the `labels`, `numeric_values` and `numeric_values_scale` based on the `answer_coordinates` and `answer_text` columns of the TSV file. The `float_answer` and `aggregation_labels` are already in the TSV file of step 2. Here's an example:
```py
>>> from transformers import TapasTokenizer
>>> import pandas as pd
>>> model_name = "google/tapas-base"
>>> tokenizer = TapasTokenizer.from_pretrained(model_name)
>>> data = {"Actors": ["Brad Pitt", "Leonardo Di Caprio", "George Clooney"], "Number of movies": ["87", "53", "69"]}
>>> queries = [
... "What is the name of the first actor?",
... "How many movies has George Clooney played in?",
... "What is the total number of movies?",
... ]
>>> answer_coordinates = [[(0, 0)], [(2, 1)], [(0, 1), (1, 1), (2, 1)]]
>>> answer_text = [["Brad Pitt"], ["69"], ["209"]]
>>> table = pd.DataFrame.from_dict(data)
>>> inputs = tokenizer(
... table=table,
... queries=queries,
... answer_coordinates=answer_coordinates,
... answer_text=answer_text,
... padding="max_length",
... return_tensors="pt",
... )
>>> inputs
{'input_ids': tensor([[ ... ]]), 'attention_mask': tensor([[...]]), 'token_type_ids': tensor([[[...]]]),
'numeric_values': tensor([[ ... ]]), 'numeric_values_scale: tensor([[ ... ]]), labels: tensor([[ ... ]])}
```
Note that [`TapasTokenizer`] expects the data of the table to be **text-only**. You can use `.astype(str)` on a dataframe to turn it into text-only data.
Of course, this only shows how to encode a single training example. It is advised to create a dataloader to iterate over batches:
```py
>>> import torch
>>> import pandas as pd
>>> tsv_path = "your_path_to_the_tsv_file"
>>> table_csv_path = "your_path_to_a_directory_containing_all_csv_files"
>>> class TableDataset(torch.utils.data.Dataset):
... def __init__(self, data, tokenizer):
... self.data = data
... self.tokenizer = tokenizer
... def __getitem__(self, idx):
... item = data.iloc[idx]
... table = pd.read_csv(table_csv_path + item.table_file).astype(
... str
... ) # be sure to make your table data text only
... encoding = self.tokenizer(
... table=table,
... queries=item.question,
... answer_coordinates=item.answer_coordinates,
... answer_text=item.answer_text,
... truncation=True,
... padding="max_length",
... return_tensors="pt",
... )
... # remove the batch dimension which the tokenizer adds by default
... encoding = {key: val.squeeze(0) for key, val in encoding.items()}
... # add the float_answer which is also required (weak supervision for aggregation case)
... encoding["float_answer"] = torch.tensor(item.float_answer)
... return encoding
... def __len__(self):
... return len(self.data)
>>> data = pd.read_csv(tsv_path, sep="\t")
>>> train_dataset = TableDataset(data, tokenizer)
>>> train_dataloader = torch.utils.data.DataLoader(train_dataset, batch_size=32)
```
</pt>
<tf>
Third, given that you've prepared your data in this TSV/CSV format (and corresponding CSV files containing the tabular data), you can then use [`TapasTokenizer`] to convert table-question pairs into `input_ids`, `attention_mask`, `token_type_ids` and so on. Again, based on which of the three cases you picked above, [`TFTapasForQuestionAnswering`] requires different
inputs to be fine-tuned:
| **Task** | **Required inputs** |
|------------------------------------|---------------------------------------------------------------------------------------------------------------------|
| Conversational | `input_ids`, `attention_mask`, `token_type_ids`, `labels` |
| Weak supervision for aggregation | `input_ids`, `attention_mask`, `token_type_ids`, `labels`, `numeric_values`, `numeric_values_scale`, `float_answer` |
| Strong supervision for aggregation | `input ids`, `attention mask`, `token type ids`, `labels`, `aggregation_labels` |
[`TapasTokenizer`] creates the `labels`, `numeric_values` and `numeric_values_scale` based on the `answer_coordinates` and `answer_text` columns of the TSV file. The `float_answer` and `aggregation_labels` are already in the TSV file of step 2. Here's an example:
```py
>>> from transformers import TapasTokenizer
>>> import pandas as pd
>>> model_name = "google/tapas-base"
>>> tokenizer = TapasTokenizer.from_pretrained(model_name)
>>> data = {"Actors": ["Brad Pitt", "Leonardo Di Caprio", "George Clooney"], "Number of movies": ["87", "53", "69"]}
>>> queries = [
... "What is the name of the first actor?",
... "How many movies has George Clooney played in?",
... "What is the total number of movies?",
... ]
>>> answer_coordinates = [[(0, 0)], [(2, 1)], [(0, 1), (1, 1), (2, 1)]]
>>> answer_text = [["Brad Pitt"], ["69"], ["209"]]
>>> table = pd.DataFrame.from_dict(data)
>>> inputs = tokenizer(
... table=table,
... queries=queries,
... answer_coordinates=answer_coordinates,
... answer_text=answer_text,
... padding="max_length",
... return_tensors="tf",
... )
>>> inputs
{'input_ids': tensor([[ ... ]]), 'attention_mask': tensor([[...]]), 'token_type_ids': tensor([[[...]]]),
'numeric_values': tensor([[ ... ]]), 'numeric_values_scale: tensor([[ ... ]]), labels: tensor([[ ... ]])}
```
Note that [`TapasTokenizer`] expects the data of the table to be **text-only**. You can use `.astype(str)` on a dataframe to turn it into text-only data.
Of course, this only shows how to encode a single training example. It is advised to create a dataloader to iterate over batches:
```py
>>> import tensorflow as tf
>>> import pandas as pd
>>> tsv_path = "your_path_to_the_tsv_file"
>>> table_csv_path = "your_path_to_a_directory_containing_all_csv_files"
>>> class TableDataset:
... def __init__(self, data, tokenizer):
... self.data = data
... self.tokenizer = tokenizer
... def __iter__(self):
... for idx in range(self.__len__()):
... item = self.data.iloc[idx]
... table = pd.read_csv(table_csv_path + item.table_file).astype(
... str
... ) # be sure to make your table data text only
... encoding = self.tokenizer(
... table=table,
... queries=item.question,
... answer_coordinates=item.answer_coordinates,
... answer_text=item.answer_text,
... truncation=True,
... padding="max_length",
... return_tensors="tf",
... )
... # remove the batch dimension which the tokenizer adds by default
... encoding = {key: tf.squeeze(val, 0) for key, val in encoding.items()}
... # add the float_answer which is also required (weak supervision for aggregation case)
... encoding["float_answer"] = tf.convert_to_tensor(item.float_answer, dtype=tf.float32)
... yield encoding["input_ids"], encoding["attention_mask"], encoding["numeric_values"], encoding[
... "numeric_values_scale"
... ], encoding["token_type_ids"], encoding["labels"], encoding["float_answer"]
... def __len__(self):
... return len(self.data)
>>> data = pd.read_csv(tsv_path, sep="\t")
>>> train_dataset = TableDataset(data, tokenizer)
>>> output_signature = (
... tf.TensorSpec(shape=(512,), dtype=tf.int32),
... tf.TensorSpec(shape=(512,), dtype=tf.int32),
... tf.TensorSpec(shape=(512,), dtype=tf.float32),
... tf.TensorSpec(shape=(512,), dtype=tf.float32),
... tf.TensorSpec(shape=(512, 7), dtype=tf.int32),
... tf.TensorSpec(shape=(512,), dtype=tf.int32),
... tf.TensorSpec(shape=(512,), dtype=tf.float32),
... )
>>> train_dataloader = tf.data.Dataset.from_generator(train_dataset, output_signature=output_signature).batch(32)
```
</tf>
</frameworkcontent>
Note that here, we encode each table-question pair independently. This is fine as long as your dataset is **not conversational**. In case your dataset involves conversational questions (such as in SQA), then you should first group together the `queries`, `answer_coordinates` and `answer_text` per table (in the order of their `position`
index) and batch encode each table with its questions. This will make sure that the `prev_labels` token types (see docs of [`TapasTokenizer`]) are set correctly. See [this notebook](https://github.com/NielsRogge/Transformers-Tutorials/blob/master/TAPAS/Fine_tuning_TapasForQuestionAnswering_on_SQA.ipynb) for more info. See [this notebook](https://github.com/kamalkraj/Tapas-Tutorial/blob/master/TAPAS/Fine_tuning_TapasForQuestionAnswering_on_SQA.ipynb) for more info regarding using the TensorFlow model.
**STEP 4: Train (fine-tune) the model
<frameworkcontent>
<pt>
You can then fine-tune [`TapasForQuestionAnswering`] as follows (shown here for the weak supervision for aggregation case):
```py
>>> from transformers import TapasConfig, TapasForQuestionAnswering, AdamW
>>> # this is the default WTQ configuration
>>> config = TapasConfig(
... num_aggregation_labels=4,
... use_answer_as_supervision=True,
... answer_loss_cutoff=0.664694,
... cell_selection_preference=0.207951,
... huber_loss_delta=0.121194,
... init_cell_selection_weights_to_zero=True,
... select_one_column=True,
... allow_empty_column_selection=False,
... temperature=0.0352513,
... )
>>> model = TapasForQuestionAnswering.from_pretrained("google/tapas-base", config=config)
>>> optimizer = AdamW(model.parameters(), lr=5e-5)
>>> model.train()
>>> for epoch in range(2): # loop over the dataset multiple times
... for batch in train_dataloader:
... # get the inputs;
... input_ids = batch["input_ids"]
... attention_mask = batch["attention_mask"]
... token_type_ids = batch["token_type_ids"]
... labels = batch["labels"]
... numeric_values = batch["numeric_values"]
... numeric_values_scale = batch["numeric_values_scale"]
... float_answer = batch["float_answer"]
... # zero the parameter gradients
... optimizer.zero_grad()
... # forward + backward + optimize
... outputs = model(
... input_ids=input_ids,
... attention_mask=attention_mask,
... token_type_ids=token_type_ids,
... labels=labels,
... numeric_values=numeric_values,
... numeric_values_scale=numeric_values_scale,
... float_answer=float_answer,
... )
... loss = outputs.loss
... loss.backward()
... optimizer.step()
```
</pt>
<tf>
You can then fine-tune [`TFTapasForQuestionAnswering`] as follows (shown here for the weak supervision for aggregation case):
```py
>>> import tensorflow as tf
>>> from transformers import TapasConfig, TFTapasForQuestionAnswering
>>> # this is the default WTQ configuration
>>> config = TapasConfig(
... num_aggregation_labels=4,
... use_answer_as_supervision=True,
... answer_loss_cutoff=0.664694,
... cell_selection_preference=0.207951,
... huber_loss_delta=0.121194,
... init_cell_selection_weights_to_zero=True,
... select_one_column=True,
... allow_empty_column_selection=False,
... temperature=0.0352513,
... )
>>> model = TFTapasForQuestionAnswering.from_pretrained("google/tapas-base", config=config)
>>> optimizer = tf.keras.optimizers.Adam(learning_rate=5e-5)
>>> for epoch in range(2): # loop over the dataset multiple times
... for batch in train_dataloader:
... # get the inputs;
... input_ids = batch[0]
... attention_mask = batch[1]
... token_type_ids = batch[4]
... labels = batch[-1]
... numeric_values = batch[2]
... numeric_values_scale = batch[3]
... float_answer = batch[6]
... # forward + backward + optimize
... with tf.GradientTape() as tape:
... outputs = model(
... input_ids=input_ids,
... attention_mask=attention_mask,
... token_type_ids=token_type_ids,
... labels=labels,
... numeric_values=numeric_values,
... numeric_values_scale=numeric_values_scale,
... float_answer=float_answer,
... )
... grads = tape.gradient(outputs.loss, model.trainable_weights)
... optimizer.apply_gradients(zip(grads, model.trainable_weights))
```
</tf>
</frameworkcontent>
## Usage: inference
<frameworkcontent>
<pt>
Here we explain how you can use [`TapasForQuestionAnswering`] or [`TFTapasForQuestionAnswering`] for inference (i.e. making predictions on new data). For inference, only `input_ids`, `attention_mask` and `token_type_ids` (which you can obtain using [`TapasTokenizer`]) have to be provided to the model to obtain the logits. Next, you can use the handy [`~models.tapas.tokenization_tapas.convert_logits_to_predictions`] method to convert these into predicted coordinates and optional aggregation indices.
However, note that inference is **different** depending on whether or not the setup is conversational. In a non-conversational set-up, inference can be done in parallel on all table-question pairs of a batch. Here's an example of that:
```py
>>> from transformers import TapasTokenizer, TapasForQuestionAnswering
>>> import pandas as pd
>>> model_name = "google/tapas-base-finetuned-wtq"
>>> model = TapasForQuestionAnswering.from_pretrained(model_name)
>>> tokenizer = TapasTokenizer.from_pretrained(model_name)
>>> data = {"Actors": ["Brad Pitt", "Leonardo Di Caprio", "George Clooney"], "Number of movies": ["87", "53", "69"]}
>>> queries = [
... "What is the name of the first actor?",
... "How many movies has George Clooney played in?",
... "What is the total number of movies?",
... ]
>>> table = pd.DataFrame.from_dict(data)
>>> inputs = tokenizer(table=table, queries=queries, padding="max_length", return_tensors="pt")
>>> outputs = model(**inputs)
>>> predicted_answer_coordinates, predicted_aggregation_indices = tokenizer.convert_logits_to_predictions(
... inputs, outputs.logits.detach(), outputs.logits_aggregation.detach()
... )
>>> # let's print out the results:
>>> id2aggregation = {0: "NONE", 1: "SUM", 2: "AVERAGE", 3: "COUNT"}
>>> aggregation_predictions_string = [id2aggregation[x] for x in predicted_aggregation_indices]
>>> answers = []
>>> for coordinates in predicted_answer_coordinates:
... if len(coordinates) == 1:
... # only a single cell:
... answers.append(table.iat[coordinates[0]])
... else:
... # multiple cells
... cell_values = []
... for coordinate in coordinates:
... cell_values.append(table.iat[coordinate])
... answers.append(", ".join(cell_values))
>>> display(table)
>>> print("")
>>> for query, answer, predicted_agg in zip(queries, answers, aggregation_predictions_string):
... print(query)
... if predicted_agg == "NONE":
... print("Predicted answer: " + answer)
... else:
... print("Predicted answer: " + predicted_agg + " > " + answer)
What is the name of the first actor?
Predicted answer: Brad Pitt
How many movies has George Clooney played in?
Predicted answer: COUNT > 69
What is the total number of movies?
Predicted answer: SUM > 87, 53, 69
```
</pt>
<tf>
Here we explain how you can use [`TFTapasForQuestionAnswering`] for inference (i.e. making predictions on new data). For inference, only `input_ids`, `attention_mask` and `token_type_ids` (which you can obtain using [`TapasTokenizer`]) have to be provided to the model to obtain the logits. Next, you can use the handy [`~models.tapas.tokenization_tapas.convert_logits_to_predictions`] method to convert these into predicted coordinates and optional aggregation indices.
However, note that inference is **different** depending on whether or not the setup is conversational. In a non-conversational set-up, inference can be done in parallel on all table-question pairs of a batch. Here's an example of that:
```py
>>> from transformers import TapasTokenizer, TFTapasForQuestionAnswering
>>> import pandas as pd
>>> model_name = "google/tapas-base-finetuned-wtq"
>>> model = TFTapasForQuestionAnswering.from_pretrained(model_name)
>>> tokenizer = TapasTokenizer.from_pretrained(model_name)
>>> data = {"Actors": ["Brad Pitt", "Leonardo Di Caprio", "George Clooney"], "Number of movies": ["87", "53", "69"]}
>>> queries = [
... "What is the name of the first actor?",
... "How many movies has George Clooney played in?",
... "What is the total number of movies?",
... ]
>>> table = pd.DataFrame.from_dict(data)
>>> inputs = tokenizer(table=table, queries=queries, padding="max_length", return_tensors="tf")
>>> outputs = model(**inputs)
>>> predicted_answer_coordinates, predicted_aggregation_indices = tokenizer.convert_logits_to_predictions(
... inputs, outputs.logits, outputs.logits_aggregation
... )
>>> # let's print out the results:
>>> id2aggregation = {0: "NONE", 1: "SUM", 2: "AVERAGE", 3: "COUNT"}
>>> aggregation_predictions_string = [id2aggregation[x] for x in predicted_aggregation_indices]
>>> answers = []
>>> for coordinates in predicted_answer_coordinates:
... if len(coordinates) == 1:
... # only a single cell:
... answers.append(table.iat[coordinates[0]])
... else:
... # multiple cells
... cell_values = []
... for coordinate in coordinates:
... cell_values.append(table.iat[coordinate])
... answers.append(", ".join(cell_values))
>>> display(table)
>>> print("")
>>> for query, answer, predicted_agg in zip(queries, answers, aggregation_predictions_string):
... print(query)
... if predicted_agg == "NONE":
... print("Predicted answer: " + answer)
... else:
... print("Predicted answer: " + predicted_agg + " > " + answer)
What is the name of the first actor?
Predicted answer: Brad Pitt
How many movies has George Clooney played in?
Predicted answer: COUNT > 69
What is the total number of movies?
Predicted answer: SUM > 87, 53, 69
```
</tf>
</frameworkcontent>
In case of a conversational set-up, then each table-question pair must be provided **sequentially** to the model, such that the `prev_labels` token types can be overwritten by the predicted `labels` of the previous table-question pair. Again, more info can be found in [this notebook](https://github.com/NielsRogge/Transformers-Tutorials/blob/master/TAPAS/Fine_tuning_TapasForQuestionAnswering_on_SQA.ipynb) (for PyTorch) and [this notebook](https://github.com/kamalkraj/Tapas-Tutorial/blob/master/TAPAS/Fine_tuning_TapasForQuestionAnswering_on_SQA.ipynb) (for TensorFlow).
## Resources
- [Text classification task guide](../tasks/sequence_classification)
- [Masked language modeling task guide](../tasks/masked_language_modeling)
## TAPAS specific outputs
[[autodoc]] models.tapas.modeling_tapas.TableQuestionAnsweringOutput
## TapasConfig
[[autodoc]] TapasConfig
## TapasTokenizer
[[autodoc]] TapasTokenizer
- __call__
- convert_logits_to_predictions
- save_vocabulary
<frameworkcontent>
<pt>
## TapasModel
[[autodoc]] TapasModel
- forward
## TapasForMaskedLM
[[autodoc]] TapasForMaskedLM
- forward
## TapasForSequenceClassification
[[autodoc]] TapasForSequenceClassification
- forward
## TapasForQuestionAnswering
[[autodoc]] TapasForQuestionAnswering
- forward
</pt>
<tf>
## TFTapasModel
[[autodoc]] TFTapasModel
- call
## TFTapasForMaskedLM
[[autodoc]] TFTapasForMaskedLM
- call
## TFTapasForSequenceClassification
[[autodoc]] TFTapasForSequenceClassification
- call
## TFTapasForQuestionAnswering
[[autodoc]] TFTapasForQuestionAnswering
- call
</tf>
</frameworkcontent>
| huggingface/transformers/blob/main/docs/source/en/model_doc/tapas.md |
## Translating the Transformers documentation into your language
As part of our mission to democratize machine learning, we'd love to make the Transformers library available in many more languages! Follow the steps below if you want to help translate the documentation into your language 🙏.
**🗞️ Open an issue**
To get started, navigate to the [Issues](https://github.com/huggingface/transformers/issues) page of this repo and check if anyone else has opened an issue for your language. If not, open a new issue by selecting the "Translation template" from the "New issue" button.
Once an issue exists, post a comment to indicate which chapters you'd like to work on, and we'll add your name to the list.
**🍴 Fork the repository**
First, you'll need to [fork the Transformers repo](https://docs.github.com/en/get-started/quickstart/fork-a-repo). You can do this by clicking on the **Fork** button on the top-right corner of this repo's page.
Once you've forked the repo, you'll want to get the files on your local machine for editing. You can do that by cloning the fork with Git as follows:
```bash
git clone https://github.com/YOUR-USERNAME/transformers.git
```
**📋 Copy-paste the English version with a new language code**
The documentation files are in one leading directory:
- [`docs/source`](https://github.com/huggingface/transformers/tree/main/docs/source): All the documentation materials are organized here by language.
You'll only need to copy the files in the [`docs/source/en`](https://github.com/huggingface/transformers/tree/main/docs/source/en) directory, so first navigate to your fork of the repo and run the following:
```bash
cd ~/path/to/transformers/docs
cp -r source/en source/LANG-ID
```
Here, `LANG-ID` should be one of the ISO 639-1 or ISO 639-2 language codes -- see [here](https://www.loc.gov/standards/iso639-2/php/code_list.php) for a handy table.
**✍️ Start translating**
The fun part comes - translating the text!
The first thing we recommend is translating the part of the `_toctree.yml` file that corresponds to your doc chapter. This file is used to render the table of contents on the website.
> 🙋 If the `_toctree.yml` file doesn't yet exist for your language, you can create one by copy-pasting from the English version and deleting the sections unrelated to your chapter. Just make sure it exists in the `docs/source/LANG-ID/` directory!
The fields you should add are `local` (with the name of the file containing the translation; e.g. `autoclass_tutorial`), and `title` (with the title of the doc in your language; e.g. `Load pretrained instances with an AutoClass`) -- as a reference, here is the `_toctree.yml` for [English](https://github.com/huggingface/transformers/blob/main/docs/source/en/_toctree.yml):
```yaml
- sections:
- local: pipeline_tutorial # Do not change this! Use the same name for your .md file
title: Pipelines for inference # Translate this!
...
title: Tutorials # Translate this!
```
Once you have translated the `_toctree.yml` file, you can start translating the [MDX](https://mdxjs.com/) files associated with your docs chapter.
> 🙋 If you'd like others to help you with the translation, you should [open an issue](https://github.com/huggingface/transformers/issues) and tag @stevhliu and @MKhalusova.
| huggingface/transformers/blob/main/docs/TRANSLATING.md |
!--Copyright 2022 The HuggingFace Team. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
the License. You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
specific language governing permissions and limitations under the License.
⚠️ Note that this file is in Markdown but contain specific syntax for our doc-builder (similar to MDX) that may not be
rendered properly in your Markdown viewer.
-->
# Multiple choice
[[open-in-colab]]
A multiple choice task is similar to question answering, except several candidate answers are provided along with a context and the model is trained to select the correct answer.
This guide will show you how to:
1. Finetune [BERT](https://huggingface.co/bert-base-uncased) on the `regular` configuration of the [SWAG](https://huggingface.co/datasets/swag) dataset to select the best answer given multiple options and some context.
2. Use your finetuned model for inference.
<Tip>
The task illustrated in this tutorial is supported by the following model architectures:
<!--This tip is automatically generated by `make fix-copies`, do not fill manually!-->
[ALBERT](../model_doc/albert), [BERT](../model_doc/bert), [BigBird](../model_doc/big_bird), [CamemBERT](../model_doc/camembert), [CANINE](../model_doc/canine), [ConvBERT](../model_doc/convbert), [Data2VecText](../model_doc/data2vec-text), [DeBERTa-v2](../model_doc/deberta-v2), [DistilBERT](../model_doc/distilbert), [ELECTRA](../model_doc/electra), [ERNIE](../model_doc/ernie), [ErnieM](../model_doc/ernie_m), [FlauBERT](../model_doc/flaubert), [FNet](../model_doc/fnet), [Funnel Transformer](../model_doc/funnel), [I-BERT](../model_doc/ibert), [Longformer](../model_doc/longformer), [LUKE](../model_doc/luke), [MEGA](../model_doc/mega), [Megatron-BERT](../model_doc/megatron-bert), [MobileBERT](../model_doc/mobilebert), [MPNet](../model_doc/mpnet), [MRA](../model_doc/mra), [Nezha](../model_doc/nezha), [Nyströmformer](../model_doc/nystromformer), [QDQBert](../model_doc/qdqbert), [RemBERT](../model_doc/rembert), [RoBERTa](../model_doc/roberta), [RoBERTa-PreLayerNorm](../model_doc/roberta-prelayernorm), [RoCBert](../model_doc/roc_bert), [RoFormer](../model_doc/roformer), [SqueezeBERT](../model_doc/squeezebert), [XLM](../model_doc/xlm), [XLM-RoBERTa](../model_doc/xlm-roberta), [XLM-RoBERTa-XL](../model_doc/xlm-roberta-xl), [XLNet](../model_doc/xlnet), [X-MOD](../model_doc/xmod), [YOSO](../model_doc/yoso)
<!--End of the generated tip-->
</Tip>
Before you begin, make sure you have all the necessary libraries installed:
```bash
pip install transformers datasets evaluate
```
We encourage you to login to your Hugging Face account so you can upload and share your model with the community. When prompted, enter your token to login:
```py
>>> from huggingface_hub import notebook_login
>>> notebook_login()
```
## Load SWAG dataset
Start by loading the `regular` configuration of the SWAG dataset from the 🤗 Datasets library:
```py
>>> from datasets import load_dataset
>>> swag = load_dataset("swag", "regular")
```
Then take a look at an example:
```py
>>> swag["train"][0]
{'ending0': 'passes by walking down the street playing their instruments.',
'ending1': 'has heard approaching them.',
'ending2': "arrives and they're outside dancing and asleep.",
'ending3': 'turns the lead singer watches the performance.',
'fold-ind': '3416',
'gold-source': 'gold',
'label': 0,
'sent1': 'Members of the procession walk down the street holding small horn brass instruments.',
'sent2': 'A drum line',
'startphrase': 'Members of the procession walk down the street holding small horn brass instruments. A drum line',
'video-id': 'anetv_jkn6uvmqwh4'}
```
While it looks like there are a lot of fields here, it is actually pretty straightforward:
- `sent1` and `sent2`: these fields show how a sentence starts, and if you put the two together, you get the `startphrase` field.
- `ending`: suggests a possible ending for how a sentence can end, but only one of them is correct.
- `label`: identifies the correct sentence ending.
## Preprocess
The next step is to load a BERT tokenizer to process the sentence starts and the four possible endings:
```py
>>> from transformers import AutoTokenizer
>>> tokenizer = AutoTokenizer.from_pretrained("bert-base-uncased")
```
The preprocessing function you want to create needs to:
1. Make four copies of the `sent1` field and combine each of them with `sent2` to recreate how a sentence starts.
2. Combine `sent2` with each of the four possible sentence endings.
3. Flatten these two lists so you can tokenize them, and then unflatten them afterward so each example has a corresponding `input_ids`, `attention_mask`, and `labels` field.
```py
>>> ending_names = ["ending0", "ending1", "ending2", "ending3"]
>>> def preprocess_function(examples):
... first_sentences = [[context] * 4 for context in examples["sent1"]]
... question_headers = examples["sent2"]
... second_sentences = [
... [f"{header} {examples[end][i]}" for end in ending_names] for i, header in enumerate(question_headers)
... ]
... first_sentences = sum(first_sentences, [])
... second_sentences = sum(second_sentences, [])
... tokenized_examples = tokenizer(first_sentences, second_sentences, truncation=True)
... return {k: [v[i : i + 4] for i in range(0, len(v), 4)] for k, v in tokenized_examples.items()}
```
To apply the preprocessing function over the entire dataset, use 🤗 Datasets [`~datasets.Dataset.map`] method. You can speed up the `map` function by setting `batched=True` to process multiple elements of the dataset at once:
```py
tokenized_swag = swag.map(preprocess_function, batched=True)
```
🤗 Transformers doesn't have a data collator for multiple choice, so you'll need to adapt the [`DataCollatorWithPadding`] to create a batch of examples. It's more efficient to *dynamically pad* the sentences to the longest length in a batch during collation, instead of padding the whole dataset to the maximum length.
`DataCollatorForMultipleChoice` flattens all the model inputs, applies padding, and then unflattens the results:
<frameworkcontent>
<pt>
```py
>>> from dataclasses import dataclass
>>> from transformers.tokenization_utils_base import PreTrainedTokenizerBase, PaddingStrategy
>>> from typing import Optional, Union
>>> import torch
>>> @dataclass
... class DataCollatorForMultipleChoice:
... """
... Data collator that will dynamically pad the inputs for multiple choice received.
... """
... tokenizer: PreTrainedTokenizerBase
... padding: Union[bool, str, PaddingStrategy] = True
... max_length: Optional[int] = None
... pad_to_multiple_of: Optional[int] = None
... def __call__(self, features):
... label_name = "label" if "label" in features[0].keys() else "labels"
... labels = [feature.pop(label_name) for feature in features]
... batch_size = len(features)
... num_choices = len(features[0]["input_ids"])
... flattened_features = [
... [{k: v[i] for k, v in feature.items()} for i in range(num_choices)] for feature in features
... ]
... flattened_features = sum(flattened_features, [])
... batch = self.tokenizer.pad(
... flattened_features,
... padding=self.padding,
... max_length=self.max_length,
... pad_to_multiple_of=self.pad_to_multiple_of,
... return_tensors="pt",
... )
... batch = {k: v.view(batch_size, num_choices, -1) for k, v in batch.items()}
... batch["labels"] = torch.tensor(labels, dtype=torch.int64)
... return batch
```
</pt>
<tf>
```py
>>> from dataclasses import dataclass
>>> from transformers.tokenization_utils_base import PreTrainedTokenizerBase, PaddingStrategy
>>> from typing import Optional, Union
>>> import tensorflow as tf
>>> @dataclass
... class DataCollatorForMultipleChoice:
... """
... Data collator that will dynamically pad the inputs for multiple choice received.
... """
... tokenizer: PreTrainedTokenizerBase
... padding: Union[bool, str, PaddingStrategy] = True
... max_length: Optional[int] = None
... pad_to_multiple_of: Optional[int] = None
... def __call__(self, features):
... label_name = "label" if "label" in features[0].keys() else "labels"
... labels = [feature.pop(label_name) for feature in features]
... batch_size = len(features)
... num_choices = len(features[0]["input_ids"])
... flattened_features = [
... [{k: v[i] for k, v in feature.items()} for i in range(num_choices)] for feature in features
... ]
... flattened_features = sum(flattened_features, [])
... batch = self.tokenizer.pad(
... flattened_features,
... padding=self.padding,
... max_length=self.max_length,
... pad_to_multiple_of=self.pad_to_multiple_of,
... return_tensors="tf",
... )
... batch = {k: tf.reshape(v, (batch_size, num_choices, -1)) for k, v in batch.items()}
... batch["labels"] = tf.convert_to_tensor(labels, dtype=tf.int64)
... return batch
```
</tf>
</frameworkcontent>
## Evaluate
Including a metric during training is often helpful for evaluating your model's performance. You can quickly load a evaluation method with the 🤗 [Evaluate](https://huggingface.co/docs/evaluate/index) library. For this task, load the [accuracy](https://huggingface.co/spaces/evaluate-metric/accuracy) metric (see the 🤗 Evaluate [quick tour](https://huggingface.co/docs/evaluate/a_quick_tour) to learn more about how to load and compute a metric):
```py
>>> import evaluate
>>> accuracy = evaluate.load("accuracy")
```
Then create a function that passes your predictions and labels to [`~evaluate.EvaluationModule.compute`] to calculate the accuracy:
```py
>>> import numpy as np
>>> def compute_metrics(eval_pred):
... predictions, labels = eval_pred
... predictions = np.argmax(predictions, axis=1)
... return accuracy.compute(predictions=predictions, references=labels)
```
Your `compute_metrics` function is ready to go now, and you'll return to it when you setup your training.
## Train
<frameworkcontent>
<pt>
<Tip>
If you aren't familiar with finetuning a model with the [`Trainer`], take a look at the basic tutorial [here](../training#train-with-pytorch-trainer)!
</Tip>
You're ready to start training your model now! Load BERT with [`AutoModelForMultipleChoice`]:
```py
>>> from transformers import AutoModelForMultipleChoice, TrainingArguments, Trainer
>>> model = AutoModelForMultipleChoice.from_pretrained("bert-base-uncased")
```
At this point, only three steps remain:
1. Define your training hyperparameters in [`TrainingArguments`]. The only required parameter is `output_dir` which specifies where to save your model. You'll push this model to the Hub by setting `push_to_hub=True` (you need to be signed in to Hugging Face to upload your model). At the end of each epoch, the [`Trainer`] will evaluate the accuracy and save the training checkpoint.
2. Pass the training arguments to [`Trainer`] along with the model, dataset, tokenizer, data collator, and `compute_metrics` function.
3. Call [`~Trainer.train`] to finetune your model.
```py
>>> training_args = TrainingArguments(
... output_dir="my_awesome_swag_model",
... evaluation_strategy="epoch",
... save_strategy="epoch",
... load_best_model_at_end=True,
... learning_rate=5e-5,
... per_device_train_batch_size=16,
... per_device_eval_batch_size=16,
... num_train_epochs=3,
... weight_decay=0.01,
... push_to_hub=True,
... )
>>> trainer = Trainer(
... model=model,
... args=training_args,
... train_dataset=tokenized_swag["train"],
... eval_dataset=tokenized_swag["validation"],
... tokenizer=tokenizer,
... data_collator=DataCollatorForMultipleChoice(tokenizer=tokenizer),
... compute_metrics=compute_metrics,
... )
>>> trainer.train()
```
Once training is completed, share your model to the Hub with the [`~transformers.Trainer.push_to_hub`] method so everyone can use your model:
```py
>>> trainer.push_to_hub()
```
</pt>
<tf>
<Tip>
If you aren't familiar with finetuning a model with Keras, take a look at the basic tutorial [here](../training#train-a-tensorflow-model-with-keras)!
</Tip>
To finetune a model in TensorFlow, start by setting up an optimizer function, learning rate schedule, and some training hyperparameters:
```py
>>> from transformers import create_optimizer
>>> batch_size = 16
>>> num_train_epochs = 2
>>> total_train_steps = (len(tokenized_swag["train"]) // batch_size) * num_train_epochs
>>> optimizer, schedule = create_optimizer(init_lr=5e-5, num_warmup_steps=0, num_train_steps=total_train_steps)
```
Then you can load BERT with [`TFAutoModelForMultipleChoice`]:
```py
>>> from transformers import TFAutoModelForMultipleChoice
>>> model = TFAutoModelForMultipleChoice.from_pretrained("bert-base-uncased")
```
Convert your datasets to the `tf.data.Dataset` format with [`~transformers.TFPreTrainedModel.prepare_tf_dataset`]:
```py
>>> data_collator = DataCollatorForMultipleChoice(tokenizer=tokenizer)
>>> tf_train_set = model.prepare_tf_dataset(
... tokenized_swag["train"],
... shuffle=True,
... batch_size=batch_size,
... collate_fn=data_collator,
... )
>>> tf_validation_set = model.prepare_tf_dataset(
... tokenized_swag["validation"],
... shuffle=False,
... batch_size=batch_size,
... collate_fn=data_collator,
... )
```
Configure the model for training with [`compile`](https://keras.io/api/models/model_training_apis/#compile-method). Note that Transformers models all have a default task-relevant loss function, so you don't need to specify one unless you want to:
```py
>>> model.compile(optimizer=optimizer) # No loss argument!
```
The last two things to setup before you start training is to compute the accuracy from the predictions, and provide a way to push your model to the Hub. Both are done by using [Keras callbacks](../main_classes/keras_callbacks).
Pass your `compute_metrics` function to [`~transformers.KerasMetricCallback`]:
```py
>>> from transformers.keras_callbacks import KerasMetricCallback
>>> metric_callback = KerasMetricCallback(metric_fn=compute_metrics, eval_dataset=tf_validation_set)
```
Specify where to push your model and tokenizer in the [`~transformers.PushToHubCallback`]:
```py
>>> from transformers.keras_callbacks import PushToHubCallback
>>> push_to_hub_callback = PushToHubCallback(
... output_dir="my_awesome_model",
... tokenizer=tokenizer,
... )
```
Then bundle your callbacks together:
```py
>>> callbacks = [metric_callback, push_to_hub_callback]
```
Finally, you're ready to start training your model! Call [`fit`](https://keras.io/api/models/model_training_apis/#fit-method) with your training and validation datasets, the number of epochs, and your callbacks to finetune the model:
```py
>>> model.fit(x=tf_train_set, validation_data=tf_validation_set, epochs=2, callbacks=callbacks)
```
Once training is completed, your model is automatically uploaded to the Hub so everyone can use it!
</tf>
</frameworkcontent>
<Tip>
For a more in-depth example of how to finetune a model for multiple choice, take a look at the corresponding
[PyTorch notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/multiple_choice.ipynb)
or [TensorFlow notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/multiple_choice-tf.ipynb).
</Tip>
## Inference
Great, now that you've finetuned a model, you can use it for inference!
Come up with some text and two candidate answers:
```py
>>> prompt = "France has a bread law, Le Décret Pain, with strict rules on what is allowed in a traditional baguette."
>>> candidate1 = "The law does not apply to croissants and brioche."
>>> candidate2 = "The law applies to baguettes."
```
<frameworkcontent>
<pt>
Tokenize each prompt and candidate answer pair and return PyTorch tensors. You should also create some `labels`:
```py
>>> from transformers import AutoTokenizer
>>> tokenizer = AutoTokenizer.from_pretrained("my_awesome_swag_model")
>>> inputs = tokenizer([[prompt, candidate1], [prompt, candidate2]], return_tensors="pt", padding=True)
>>> labels = torch.tensor(0).unsqueeze(0)
```
Pass your inputs and labels to the model and return the `logits`:
```py
>>> from transformers import AutoModelForMultipleChoice
>>> model = AutoModelForMultipleChoice.from_pretrained("my_awesome_swag_model")
>>> outputs = model(**{k: v.unsqueeze(0) for k, v in inputs.items()}, labels=labels)
>>> logits = outputs.logits
```
Get the class with the highest probability:
```py
>>> predicted_class = logits.argmax().item()
>>> predicted_class
'0'
```
</pt>
<tf>
Tokenize each prompt and candidate answer pair and return TensorFlow tensors:
```py
>>> from transformers import AutoTokenizer
>>> tokenizer = AutoTokenizer.from_pretrained("my_awesome_swag_model")
>>> inputs = tokenizer([[prompt, candidate1], [prompt, candidate2]], return_tensors="tf", padding=True)
```
Pass your inputs to the model and return the `logits`:
```py
>>> from transformers import TFAutoModelForMultipleChoice
>>> model = TFAutoModelForMultipleChoice.from_pretrained("my_awesome_swag_model")
>>> inputs = {k: tf.expand_dims(v, 0) for k, v in inputs.items()}
>>> outputs = model(inputs)
>>> logits = outputs.logits
```
Get the class with the highest probability:
```py
>>> predicted_class = int(tf.math.argmax(logits, axis=-1)[0])
>>> predicted_class
'0'
```
</tf>
</frameworkcontent>
| huggingface/transformers/blob/main/docs/source/en/tasks/multiple_choice.md |
!---
Copyright 2021 The HuggingFace Team. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
-->
# Question answering
This folder contains several scripts that showcase how to fine-tune a 🤗 Transformers model on a question answering dataset,
like SQuAD.
## Trainer-based scripts
The [`run_qa.py`](https://github.com/huggingface/transformers/blob/main/examples/pytorch/question-answering/run_qa.py),
[`run_qa_beam_search.py`](https://github.com/huggingface/transformers/blob/main/examples/pytorch/question-answering/run_qa_beam_search.py) and [`run_seq2seq_qa.py`](https://github.com/huggingface/transformers/blob/main/examples/pytorch/question-answering/run_seq2seq_qa.py) leverage the 🤗 [Trainer](https://huggingface.co/transformers/main_classes/trainer.html) for fine-tuning.
### Fine-tuning BERT on SQuAD1.0
The [`run_qa.py`](https://github.com/huggingface/transformers/blob/main/examples/pytorch/question-answering/run_qa.py) script
allows to fine-tune any model from our [hub](https://huggingface.co/models) (as long as its architecture has a `ForQuestionAnswering` version in the library) on a question-answering dataset (such as SQuAD, or any other QA dataset available in the `datasets` library, or your own csv/jsonlines files) as long as they are structured the same way as SQuAD. You might need to tweak the data processing inside the script if your data is structured differently.
**Note:** This script only works with models that have a fast tokenizer (backed by the 🤗 Tokenizers library) as it
uses special features of those tokenizers. You can check if your favorite model has a fast tokenizer in
[this table](https://huggingface.co/transformers/index.html#supported-frameworks), if it doesn't you can still use the old version of the script which can be found [here](https://github.com/huggingface/transformers/tree/main/examples/legacy/question-answering).
Note that if your dataset contains samples with no possible answers (like SQuAD version 2), you need to pass along the flag `--version_2_with_negative`.
This example code fine-tunes BERT on the SQuAD1.0 dataset. It runs in 24 min (with BERT-base) or 68 min (with BERT-large)
on a single tesla V100 16GB.
```bash
python run_qa.py \
--model_name_or_path bert-base-uncased \
--dataset_name squad \
--do_train \
--do_eval \
--per_device_train_batch_size 12 \
--learning_rate 3e-5 \
--num_train_epochs 2 \
--max_seq_length 384 \
--doc_stride 128 \
--output_dir /tmp/debug_squad/
```
Training with the previously defined hyper-parameters yields the following results:
```bash
f1 = 88.52
exact_match = 81.22
```
### Fine-tuning XLNet with beam search on SQuAD
The [`run_qa_beam_search.py`](https://github.com/huggingface/transformers/blob/main/examples/pytorch/question-answering/run_qa_beam_search.py) script is only meant to fine-tune XLNet, which is a special encoder-only Transformer model. The example code below fine-tunes XLNet on the SQuAD1.0 and SQuAD2.0 datasets.
#### Command for SQuAD1.0:
```bash
python run_qa_beam_search.py \
--model_name_or_path xlnet-large-cased \
--dataset_name squad \
--do_train \
--do_eval \
--learning_rate 3e-5 \
--num_train_epochs 2 \
--max_seq_length 384 \
--doc_stride 128 \
--output_dir ./wwm_cased_finetuned_squad/ \
--per_device_eval_batch_size=4 \
--per_device_train_batch_size=4 \
--save_steps 5000
```
#### Command for SQuAD2.0:
```bash
export SQUAD_DIR=/path/to/SQUAD
python run_qa_beam_search.py \
--model_name_or_path xlnet-large-cased \
--dataset_name squad_v2 \
--do_train \
--do_eval \
--version_2_with_negative \
--learning_rate 3e-5 \
--num_train_epochs 4 \
--max_seq_length 384 \
--doc_stride 128 \
--output_dir ./wwm_cased_finetuned_squad/ \
--per_device_eval_batch_size=2 \
--per_device_train_batch_size=2 \
--save_steps 5000
```
### Fine-tuning T5 on SQuAD2.0
The [`run_seq2seq_qa.py`](https://github.com/huggingface/transformers/blob/main/examples/pytorch/question-answering/run_seq2seq_qa.py) script is meant for encoder-decoder (also called seq2seq) Transformer models, such as T5 or BART. These
models are generative, rather than discriminative. This means that they learn to generate the correct answer, rather than predicting the start and end position of the tokens of the answer.
This example code fine-tunes T5 on the SQuAD2.0 dataset.
```bash
python run_seq2seq_qa.py \
--model_name_or_path t5-small \
--dataset_name squad_v2 \
--context_column context \
--question_column question \
--answer_column answers \
--do_train \
--do_eval \
--per_device_train_batch_size 12 \
--learning_rate 3e-5 \
--num_train_epochs 2 \
--max_seq_length 384 \
--doc_stride 128 \
--output_dir /tmp/debug_seq2seq_squad/
```
## Accelerate-based scripts
Based on the scripts `run_qa_no_trainer.py` and `run_qa_beam_search_no_trainer.py`.
Like `run_qa.py` and `run_qa_beam_search.py`, these scripts allow you to fine-tune any of the models supported on a
SQuAD or a similar dataset, the main difference is that this script exposes the bare training loop, to allow you to quickly experiment and add any customization you would like. It offers less options than the script with `Trainer` (for instance you can easily change the options for the optimizer or the dataloaders directly in the script), but still run in a distributed setup, on TPU and supports mixed precision by leveraging the [🤗 `Accelerate`](https://github.com/huggingface/accelerate) library.
You can use the script normally after installing it:
```bash
pip install git+https://github.com/huggingface/accelerate
```
then
```bash
python run_qa_no_trainer.py \
--model_name_or_path bert-base-uncased \
--dataset_name squad \
--max_seq_length 384 \
--doc_stride 128 \
--output_dir ~/tmp/debug_squad
```
You can then use your usual launchers to run in it in a distributed environment, but the easiest way is to run
```bash
accelerate config
```
and reply to the questions asked. Then
```bash
accelerate test
```
that will check everything is ready for training. Finally, you can launch training with
```bash
accelerate launch run_qa_no_trainer.py \
--model_name_or_path bert-base-uncased \
--dataset_name squad \
--max_seq_length 384 \
--doc_stride 128 \
--output_dir ~/tmp/debug_squad
```
This command is the same and will work for:
- a CPU-only setup
- a setup with one GPU
- a distributed training with several GPUs (single or multi node)
- a training on TPUs
Note that this library is in alpha release so your feedback is more than welcome if you encounter any problem using it.
| huggingface/transformers/blob/main/examples/pytorch/question-answering/README.md |
!--Copyright 2022 The HuggingFace Team. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
the License. You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
specific language governing permissions and limitations under the License.
⚠️ Note that this file is in Markdown but contain specific syntax for our doc-builder (similar to MDX) that may not be
rendered properly in your Markdown viewer.
-->
# RoCBert
## Overview
The RoCBert model was proposed in [RoCBert: Robust Chinese Bert with Multimodal Contrastive Pretraining](https://aclanthology.org/2022.acl-long.65.pdf) by HuiSu, WeiweiShi, XiaoyuShen, XiaoZhou, TuoJi, JiaruiFang, JieZhou.
It's a pretrained Chinese language model that is robust under various forms of adversarial attacks.
The abstract from the paper is the following:
*Large-scale pretrained language models have achieved SOTA results on NLP tasks. However, they have been shown
vulnerable to adversarial attacks especially for logographic languages like Chinese. In this work, we propose
ROCBERT: a pretrained Chinese Bert that is robust to various forms of adversarial attacks like word perturbation,
synonyms, typos, etc. It is pretrained with the contrastive learning objective which maximizes the label consistency
under different synthesized adversarial examples. The model takes as input multimodal information including the
semantic, phonetic and visual features. We show all these features are important to the model robustness since the
attack can be performed in all the three forms. Across 5 Chinese NLU tasks, ROCBERT outperforms strong baselines under
three blackbox adversarial algorithms without sacrificing the performance on clean testset. It also performs the best
in the toxic content detection task under human-made attacks.*
This model was contributed by [weiweishi](https://huggingface.co/weiweishi).
## Resources
- [Text classification task guide](../tasks/sequence_classification)
- [Token classification task guide](../tasks/token_classification)
- [Question answering task guide](../tasks/question_answering)
- [Causal language modeling task guide](../tasks/language_modeling)
- [Masked language modeling task guide](../tasks/masked_language_modeling)
- [Multiple choice task guide](../tasks/multiple_choice)
## RoCBertConfig
[[autodoc]] RoCBertConfig
- all
## RoCBertTokenizer
[[autodoc]] RoCBertTokenizer
- build_inputs_with_special_tokens
- get_special_tokens_mask
- create_token_type_ids_from_sequences
- save_vocabulary
## RoCBertModel
[[autodoc]] RoCBertModel
- forward
## RoCBertForPreTraining
[[autodoc]] RoCBertForPreTraining
- forward
## RoCBertForCausalLM
[[autodoc]] RoCBertForCausalLM
- forward
## RoCBertForMaskedLM
[[autodoc]] RoCBertForMaskedLM
- forward
## RoCBertForSequenceClassification
[[autodoc]] transformers.RoCBertForSequenceClassification
- forward
## RoCBertForMultipleChoice
[[autodoc]] transformers.RoCBertForMultipleChoice
- forward
## RoCBertForTokenClassification
[[autodoc]] transformers.RoCBertForTokenClassification
- forward
## RoCBertForQuestionAnswering
[[autodoc]] RoCBertForQuestionAnswering
- forward
| huggingface/transformers/blob/main/docs/source/en/model_doc/roc_bert.md |
!--Copyright 2022 The HuggingFace Team. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
the License. You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
specific language governing permissions and limitations under the License.
⚠️ Note that this file is in Markdown but contain specific syntax for our doc-builder (similar to MDX) that may not be
rendered properly in your Markdown viewer.
-->
# Big Transfer (BiT)
## Overview
The BiT model was proposed in [Big Transfer (BiT): General Visual Representation Learning](https://arxiv.org/abs/1912.11370) by Alexander Kolesnikov, Lucas Beyer, Xiaohua Zhai, Joan Puigcerver, Jessica Yung, Sylvain Gelly, Neil Houlsby.
BiT is a simple recipe for scaling up pre-training of [ResNet](resnet)-like architectures (specifically, ResNetv2). The method results in significant improvements for transfer learning.
The abstract from the paper is the following:
*Transfer of pre-trained representations improves sample efficiency and simplifies hyperparameter tuning when training deep neural networks for vision. We revisit the paradigm of pre-training on large supervised datasets and fine-tuning the model on a target task. We scale up pre-training, and propose a simple recipe that we call Big Transfer (BiT). By combining a few carefully selected components, and transferring using a simple heuristic, we achieve strong performance on over 20 datasets. BiT performs well across a surprisingly wide range of data regimes -- from 1 example per class to 1M total examples. BiT achieves 87.5% top-1 accuracy on ILSVRC-2012, 99.4% on CIFAR-10, and 76.3% on the 19 task Visual Task Adaptation Benchmark (VTAB). On small datasets, BiT attains 76.8% on ILSVRC-2012 with 10 examples per class, and 97.0% on CIFAR-10 with 10 examples per class. We conduct detailed analysis of the main components that lead to high transfer performance.*
This model was contributed by [nielsr](https://huggingface.co/nielsr).
The original code can be found [here](https://github.com/google-research/big_transfer).
## Usage tips
- BiT models are equivalent to ResNetv2 in terms of architecture, except that: 1) all batch normalization layers are replaced by [group normalization](https://arxiv.org/abs/1803.08494),
2) [weight standardization](https://arxiv.org/abs/1903.10520) is used for convolutional layers. The authors show that the combination of both is useful for training with large batch sizes, and has a significant
impact on transfer learning.
## Resources
A list of official Hugging Face and community (indicated by 🌎) resources to help you get started with BiT.
<PipelineTag pipeline="image-classification"/>
- [`BitForImageClassification`] is supported by this [example script](https://github.com/huggingface/transformers/tree/main/examples/pytorch/image-classification) and [notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/image_classification.ipynb).
- See also: [Image classification task guide](../tasks/image_classification)
If you're interested in submitting a resource to be included here, please feel free to open a Pull Request and we'll review it! The resource should ideally demonstrate something new instead of duplicating an existing resource.
## BitConfig
[[autodoc]] BitConfig
## BitImageProcessor
[[autodoc]] BitImageProcessor
- preprocess
## BitModel
[[autodoc]] BitModel
- forward
## BitForImageClassification
[[autodoc]] BitForImageClassification
- forward | huggingface/transformers/blob/main/docs/source/en/model_doc/bit.md |
!--Copyright 2022 The HuggingFace Team. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
the License. You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
specific language governing permissions and limitations under the License.
⚠️ Note that this file is in Markdown but contain specific syntax for our doc-builder (similar to MDX) that may not be
rendered properly in your Markdown viewer.
-->
# Image Processor
An image processor is in charge of preparing input features for vision models and post processing their outputs. This includes transformations such as resizing, normalization, and conversion to PyTorch, TensorFlow, Flax and Numpy tensors. It may also include model specific post-processing such as converting logits to segmentation masks.
## ImageProcessingMixin
[[autodoc]] image_processing_utils.ImageProcessingMixin
- from_pretrained
- save_pretrained
## BatchFeature
[[autodoc]] BatchFeature
## BaseImageProcessor
[[autodoc]] image_processing_utils.BaseImageProcessor
| huggingface/transformers/blob/main/docs/source/en/main_classes/image_processor.md |
!--Copyright 2023 The HuggingFace Team. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
the License. You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
specific language governing permissions and limitations under the License.
⚠️ Note that this file is in Markdown but contain specific syntax for our doc-builder (similar to MDX) that may not be
rendered properly in your Markdown viewer.
-->
# Attention mechanisms
Most transformer models use full attention in the sense that the attention matrix is square. It can be a big
computational bottleneck when you have long texts. Longformer and reformer are models that try to be more efficient and
use a sparse version of the attention matrix to speed up training.
## LSH attention
[Reformer](#reformer) uses LSH attention. In the softmax(QK^t), only the biggest elements (in the softmax
dimension) of the matrix QK^t are going to give useful contributions. So for each query q in Q, we can consider only
the keys k in K that are close to q. A hash function is used to determine if q and k are close. The attention mask is
modified to mask the current token (except at the first position), because it will give a query and a key equal (so
very similar to each other). Since the hash can be a bit random, several hash functions are used in practice
(determined by a n_rounds parameter) and then are averaged together.
## Local attention
[Longformer](#longformer) uses local attention: often, the local context (e.g., what are the two tokens to the
left and right?) is enough to take action for a given token. Also, by stacking attention layers that have a small
window, the last layer will have a receptive field of more than just the tokens in the window, allowing them to build a
representation of the whole sentence.
Some preselected input tokens are also given global attention: for those few tokens, the attention matrix can access
all tokens and this process is symmetric: all other tokens have access to those specific tokens (on top of the ones in
their local window). This is shown in Figure 2d of the paper, see below for a sample attention mask:
<div class="flex justify-center">
<img scale="50 %" align="center" src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/local_attention_mask.png"/>
</div>
Using those attention matrices with less parameters then allows the model to have inputs having a bigger sequence
length.
## Other tricks
### Axial positional encodings
[Reformer](#reformer) uses axial positional encodings: in traditional transformer models, the positional encoding
E is a matrix of size \\(l\\) by \\(d\\), \\(l\\) being the sequence length and \\(d\\) the dimension of the
hidden state. If you have very long texts, this matrix can be huge and take way too much space on the GPU. To alleviate
that, axial positional encodings consist of factorizing that big matrix E in two smaller matrices E1 and E2, with
dimensions \\(l_{1} \times d_{1}\\) and \\(l_{2} \times d_{2}\\), such that \\(l_{1} \times l_{2} = l\\) and
\\(d_{1} + d_{2} = d\\) (with the product for the lengths, this ends up being way smaller). The embedding for time
step \\(j\\) in E is obtained by concatenating the embeddings for timestep \\(j \% l1\\) in E1 and \\(j // l1\\)
in E2.
| huggingface/transformers/blob/main/docs/source/en/attention.md |
!--Copyright 2022 The HuggingFace Team. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
the License. You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
specific language governing permissions and limitations under the License.
⚠️ Note that this file is in Markdown but contain specific syntax for our doc-builder (similar to MDX) that may not be
rendered properly in your Markdown viewer.
-->
# Image Segmentation
[[open-in-colab]]
<Youtube id="dKE8SIt9C-w"/>
Image segmentation models separate areas corresponding to different areas of interest in an image. These models work by assigning a label to each pixel. There are several types of segmentation: semantic segmentation, instance segmentation, and panoptic segmentation.
In this guide, we will:
1. [Take a look at different types of segmentation](#types-of-segmentation).
2. [Have an end-to-end fine-tuning example for semantic segmentation](#fine-tuning-a-model-for-segmentation).
Before you begin, make sure you have all the necessary libraries installed:
```bash
pip install -q datasets transformers evaluate
```
We encourage you to log in to your Hugging Face account so you can upload and share your model with the community. When prompted, enter your token to log in:
```py
>>> from huggingface_hub import notebook_login
>>> notebook_login()
```
## Types of Segmentation
Semantic segmentation assigns a label or class to every single pixel in an image. Let's take a look at a semantic segmentation model output. It will assign the same class to every instance of an object it comes across in an image, for example, all cats will be labeled as "cat" instead of "cat-1", "cat-2".
We can use transformers' image segmentation pipeline to quickly infer a semantic segmentation model. Let's take a look at the example image.
```python
from transformers import pipeline
from PIL import Image
import requests
url = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/segmentation_input.jpg"
image = Image.open(requests.get(url, stream=True).raw)
image
```
<div class="flex justify-center">
<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/segmentation_input.jpg" alt="Segmentation Input"/>
</div>
We will use [nvidia/segformer-b1-finetuned-cityscapes-1024-1024](https://huggingface.co/nvidia/segformer-b1-finetuned-cityscapes-1024-1024).
```python
semantic_segmentation = pipeline("image-segmentation", "nvidia/segformer-b1-finetuned-cityscapes-1024-1024")
results = semantic_segmentation(image)
results
```
The segmentation pipeline output includes a mask for every predicted class.
```bash
[{'score': None,
'label': 'road',
'mask': <PIL.Image.Image image mode=L size=612x415>},
{'score': None,
'label': 'sidewalk',
'mask': <PIL.Image.Image image mode=L size=612x415>},
{'score': None,
'label': 'building',
'mask': <PIL.Image.Image image mode=L size=612x415>},
{'score': None,
'label': 'wall',
'mask': <PIL.Image.Image image mode=L size=612x415>},
{'score': None,
'label': 'pole',
'mask': <PIL.Image.Image image mode=L size=612x415>},
{'score': None,
'label': 'traffic sign',
'mask': <PIL.Image.Image image mode=L size=612x415>},
{'score': None,
'label': 'vegetation',
'mask': <PIL.Image.Image image mode=L size=612x415>},
{'score': None,
'label': 'terrain',
'mask': <PIL.Image.Image image mode=L size=612x415>},
{'score': None,
'label': 'sky',
'mask': <PIL.Image.Image image mode=L size=612x415>},
{'score': None,
'label': 'car',
'mask': <PIL.Image.Image image mode=L size=612x415>}]
```
Taking a look at the mask for the car class, we can see every car is classified with the same mask.
```python
results[-1]["mask"]
```
<div class="flex justify-center">
<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/semantic_segmentation_output.png" alt="Semantic Segmentation Output"/>
</div>
In instance segmentation, the goal is not to classify every pixel, but to predict a mask for **every instance of an object** in a given image. It works very similar to object detection, where there is a bounding box for every instance, there's a segmentation mask instead. We will use [facebook/mask2former-swin-large-cityscapes-instance](https://huggingface.co/facebook/mask2former-swin-large-cityscapes-instance) for this.
```python
instance_segmentation = pipeline("image-segmentation", "facebook/mask2former-swin-large-cityscapes-instance")
results = instance_segmentation(Image.open(image))
results
```
As you can see below, there are multiple cars classified, and there's no classification for pixels other than pixels that belong to car and person instances.
```bash
[{'score': 0.999944,
'label': 'car',
'mask': <PIL.Image.Image image mode=L size=612x415>},
{'score': 0.999945,
'label': 'car',
'mask': <PIL.Image.Image image mode=L size=612x415>},
{'score': 0.999652,
'label': 'car',
'mask': <PIL.Image.Image image mode=L size=612x415>},
{'score': 0.903529,
'label': 'person',
'mask': <PIL.Image.Image image mode=L size=612x415>}]
```
Checking out one of the car masks below.
```python
results[2]["mask"]
```
<div class="flex justify-center">
<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/instance_segmentation_output.png" alt="Semantic Segmentation Output"/>
</div>
Panoptic segmentation combines semantic segmentation and instance segmentation, where every pixel is classified into a class and an instance of that class, and there are multiple masks for each instance of a class. We can use [facebook/mask2former-swin-large-cityscapes-panoptic](https://huggingface.co/facebook/mask2former-swin-large-cityscapes-panoptic) for this.
```python
panoptic_segmentation = pipeline("image-segmentation", "facebook/mask2former-swin-large-cityscapes-panoptic")
results = panoptic_segmentation(Image.open(image))
results
```
As you can see below, we have more classes. We will later illustrate to see that every pixel is classified into one of the classes.
```bash
[{'score': 0.999981,
'label': 'car',
'mask': <PIL.Image.Image image mode=L size=612x415>},
{'score': 0.999958,
'label': 'car',
'mask': <PIL.Image.Image image mode=L size=612x415>},
{'score': 0.99997,
'label': 'vegetation',
'mask': <PIL.Image.Image image mode=L size=612x415>},
{'score': 0.999575,
'label': 'pole',
'mask': <PIL.Image.Image image mode=L size=612x415>},
{'score': 0.999958,
'label': 'building',
'mask': <PIL.Image.Image image mode=L size=612x415>},
{'score': 0.999634,
'label': 'road',
'mask': <PIL.Image.Image image mode=L size=612x415>},
{'score': 0.996092,
'label': 'sidewalk',
'mask': <PIL.Image.Image image mode=L size=612x415>},
{'score': 0.999221,
'label': 'car',
'mask': <PIL.Image.Image image mode=L size=612x415>},
{'score': 0.99987,
'label': 'sky',
'mask': <PIL.Image.Image image mode=L size=612x415>}]
```
Let's have a side by side comparison for all types of segmentation.
<div class="flex justify-center">
<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/segmentation-comparison.png" alt="Segmentation Maps Compared"/>
</div>
Seeing all types of segmentation, let's have a deep dive on fine-tuning a model for semantic segmentation.
Common real-world applications of semantic segmentation include training self-driving cars to identify pedestrians and important traffic information, identifying cells and abnormalities in medical imagery, and monitoring environmental changes from satellite imagery.
## Fine-tuning a Model for Segmentation
We will now:
1. Finetune [SegFormer](https://huggingface.co/docs/transformers/main/en/model_doc/segformer#segformer) on the [SceneParse150](https://huggingface.co/datasets/scene_parse_150) dataset.
2. Use your fine-tuned model for inference.
<Tip>
The task illustrated in this tutorial is supported by the following model architectures:
<!--This tip is automatically generated by `make fix-copies`, do not fill manually!-->
[BEiT](../model_doc/beit), [Data2VecVision](../model_doc/data2vec-vision), [DPT](../model_doc/dpt), [MobileNetV2](../model_doc/mobilenet_v2), [MobileViT](../model_doc/mobilevit), [MobileViTV2](../model_doc/mobilevitv2), [SegFormer](../model_doc/segformer), [UPerNet](../model_doc/upernet)
<!--End of the generated tip-->
</Tip>
### Load SceneParse150 dataset
Start by loading a smaller subset of the SceneParse150 dataset from the 🤗 Datasets library. This'll give you a chance to experiment and make sure everything works before spending more time training on the full dataset.
```py
>>> from datasets import load_dataset
>>> ds = load_dataset("scene_parse_150", split="train[:50]")
```
Split the dataset's `train` split into a train and test set with the [`~datasets.Dataset.train_test_split`] method:
```py
>>> ds = ds.train_test_split(test_size=0.2)
>>> train_ds = ds["train"]
>>> test_ds = ds["test"]
```
Then take a look at an example:
```py
>>> train_ds[0]
{'image': <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=512x683 at 0x7F9B0C201F90>,
'annotation': <PIL.PngImagePlugin.PngImageFile image mode=L size=512x683 at 0x7F9B0C201DD0>,
'scene_category': 368}
```
- `image`: a PIL image of the scene.
- `annotation`: a PIL image of the segmentation map, which is also the model's target.
- `scene_category`: a category id that describes the image scene like "kitchen" or "office". In this guide, you'll only need `image` and `annotation`, both of which are PIL images.
You'll also want to create a dictionary that maps a label id to a label class which will be useful when you set up the model later. Download the mappings from the Hub and create the `id2label` and `label2id` dictionaries:
```py
>>> import json
>>> from huggingface_hub import cached_download, hf_hub_url
>>> repo_id = "huggingface/label-files"
>>> filename = "ade20k-id2label.json"
>>> id2label = json.load(open(cached_download(hf_hub_url(repo_id, filename, repo_type="dataset")), "r"))
>>> id2label = {int(k): v for k, v in id2label.items()}
>>> label2id = {v: k for k, v in id2label.items()}
>>> num_labels = len(id2label)
```
#### Custom dataset
You could also create and use your own dataset if you prefer to train with the [run_semantic_segmentation.py](https://github.com/huggingface/transformers/blob/main/examples/pytorch/semantic-segmentation/run_semantic_segmentation.py) script instead of a notebook instance. The script requires:
1. a [`~datasets.DatasetDict`] with two [`~datasets.Image`] columns, "image" and "label"
```py
from datasets import Dataset, DatasetDict, Image
image_paths_train = ["path/to/image_1.jpg/jpg", "path/to/image_2.jpg/jpg", ..., "path/to/image_n.jpg/jpg"]
label_paths_train = ["path/to/annotation_1.png", "path/to/annotation_2.png", ..., "path/to/annotation_n.png"]
image_paths_validation = [...]
label_paths_validation = [...]
def create_dataset(image_paths, label_paths):
dataset = Dataset.from_dict({"image": sorted(image_paths),
"label": sorted(label_paths)})
dataset = dataset.cast_column("image", Image())
dataset = dataset.cast_column("label", Image())
return dataset
# step 1: create Dataset objects
train_dataset = create_dataset(image_paths_train, label_paths_train)
validation_dataset = create_dataset(image_paths_validation, label_paths_validation)
# step 2: create DatasetDict
dataset = DatasetDict({
"train": train_dataset,
"validation": validation_dataset,
}
)
# step 3: push to Hub (assumes you have ran the huggingface-cli login command in a terminal/notebook)
dataset.push_to_hub("your-name/dataset-repo")
# optionally, you can push to a private repo on the Hub
# dataset.push_to_hub("name of repo on the hub", private=True)
```
2. an id2label dictionary mapping the class integers to their class names
```py
import json
# simple example
id2label = {0: 'cat', 1: 'dog'}
with open('id2label.json', 'w') as fp:
json.dump(id2label, fp)
```
As an example, take a look at this [example dataset](https://huggingface.co/datasets/nielsr/ade20k-demo) which was created with the steps shown above.
### Preprocess
The next step is to load a SegFormer image processor to prepare the images and annotations for the model. Some datasets, like this one, use the zero-index as the background class. However, the background class isn't actually included in the 150 classes, so you'll need to set `reduce_labels=True` to subtract one from all the labels. The zero-index is replaced by `255` so it's ignored by SegFormer's loss function:
```py
>>> from transformers import AutoImageProcessor
>>> checkpoint = "nvidia/mit-b0"
>>> image_processor = AutoImageProcessor.from_pretrained(checkpoint, reduce_labels=True)
```
<frameworkcontent>
<pt>
It is common to apply some data augmentations to an image dataset to make a model more robust against overfitting. In this guide, you'll use the [`ColorJitter`](https://pytorch.org/vision/stable/generated/torchvision.transforms.ColorJitter.html) function from [torchvision](https://pytorch.org/vision/stable/index.html) to randomly change the color properties of an image, but you can also use any image library you like.
```py
>>> from torchvision.transforms import ColorJitter
>>> jitter = ColorJitter(brightness=0.25, contrast=0.25, saturation=0.25, hue=0.1)
```
Now create two preprocessing functions to prepare the images and annotations for the model. These functions convert the images into `pixel_values` and annotations to `labels`. For the training set, `jitter` is applied before providing the images to the image processor. For the test set, the image processor crops and normalizes the `images`, and only crops the `labels` because no data augmentation is applied during testing.
```py
>>> def train_transforms(example_batch):
... images = [jitter(x) for x in example_batch["image"]]
... labels = [x for x in example_batch["annotation"]]
... inputs = image_processor(images, labels)
... return inputs
>>> def val_transforms(example_batch):
... images = [x for x in example_batch["image"]]
... labels = [x for x in example_batch["annotation"]]
... inputs = image_processor(images, labels)
... return inputs
```
To apply the `jitter` over the entire dataset, use the 🤗 Datasets [`~datasets.Dataset.set_transform`] function. The transform is applied on the fly which is faster and consumes less disk space:
```py
>>> train_ds.set_transform(train_transforms)
>>> test_ds.set_transform(val_transforms)
```
</pt>
</frameworkcontent>
<frameworkcontent>
<tf>
It is common to apply some data augmentations to an image dataset to make a model more robust against overfitting.
In this guide, you'll use [`tf.image`](https://www.tensorflow.org/api_docs/python/tf/image) to randomly change the color properties of an image, but you can also use any image
library you like.
Define two separate transformation functions:
- training data transformations that include image augmentation
- validation data transformations that only transpose the images, since computer vision models in 🤗 Transformers expect channels-first layout
```py
>>> import tensorflow as tf
>>> def aug_transforms(image):
... image = tf.keras.utils.img_to_array(image)
... image = tf.image.random_brightness(image, 0.25)
... image = tf.image.random_contrast(image, 0.5, 2.0)
... image = tf.image.random_saturation(image, 0.75, 1.25)
... image = tf.image.random_hue(image, 0.1)
... image = tf.transpose(image, (2, 0, 1))
... return image
>>> def transforms(image):
... image = tf.keras.utils.img_to_array(image)
... image = tf.transpose(image, (2, 0, 1))
... return image
```
Next, create two preprocessing functions to prepare batches of images and annotations for the model. These functions apply
the image transformations and use the earlier loaded `image_processor` to convert the images into `pixel_values` and
annotations to `labels`. `ImageProcessor` also takes care of resizing and normalizing the images.
```py
>>> def train_transforms(example_batch):
... images = [aug_transforms(x.convert("RGB")) for x in example_batch["image"]]
... labels = [x for x in example_batch["annotation"]]
... inputs = image_processor(images, labels)
... return inputs
>>> def val_transforms(example_batch):
... images = [transforms(x.convert("RGB")) for x in example_batch["image"]]
... labels = [x for x in example_batch["annotation"]]
... inputs = image_processor(images, labels)
... return inputs
```
To apply the preprocessing transformations over the entire dataset, use the 🤗 Datasets [`~datasets.Dataset.set_transform`] function.
The transform is applied on the fly which is faster and consumes less disk space:
```py
>>> train_ds.set_transform(train_transforms)
>>> test_ds.set_transform(val_transforms)
```
</tf>
</frameworkcontent>
### Evaluate
Including a metric during training is often helpful for evaluating your model's performance. You can quickly load an evaluation method with the 🤗 [Evaluate](https://huggingface.co/docs/evaluate/index) library. For this task, load the [mean Intersection over Union](https://huggingface.co/spaces/evaluate-metric/accuracy) (IoU) metric (see the 🤗 Evaluate [quick tour](https://huggingface.co/docs/evaluate/a_quick_tour) to learn more about how to load and compute a metric):
```py
>>> import evaluate
>>> metric = evaluate.load("mean_iou")
```
Then create a function to [`~evaluate.EvaluationModule.compute`] the metrics. Your predictions need to be converted to
logits first, and then reshaped to match the size of the labels before you can call [`~evaluate.EvaluationModule.compute`]:
<frameworkcontent>
<pt>
```py
>>> import numpy as np
>>> import torch
>>> from torch import nn
>>> def compute_metrics(eval_pred):
... with torch.no_grad():
... logits, labels = eval_pred
... logits_tensor = torch.from_numpy(logits)
... logits_tensor = nn.functional.interpolate(
... logits_tensor,
... size=labels.shape[-2:],
... mode="bilinear",
... align_corners=False,
... ).argmax(dim=1)
... pred_labels = logits_tensor.detach().cpu().numpy()
... metrics = metric.compute(
... predictions=pred_labels,
... references=labels,
... num_labels=num_labels,
... ignore_index=255,
... reduce_labels=False,
... )
... for key, value in metrics.items():
... if isinstance(value, np.ndarray):
... metrics[key] = value.tolist()
... return metrics
```
</pt>
</frameworkcontent>
<frameworkcontent>
<tf>
```py
>>> def compute_metrics(eval_pred):
... logits, labels = eval_pred
... logits = tf.transpose(logits, perm=[0, 2, 3, 1])
... logits_resized = tf.image.resize(
... logits,
... size=tf.shape(labels)[1:],
... method="bilinear",
... )
... pred_labels = tf.argmax(logits_resized, axis=-1)
... metrics = metric.compute(
... predictions=pred_labels,
... references=labels,
... num_labels=num_labels,
... ignore_index=-1,
... reduce_labels=image_processor.do_reduce_labels,
... )
... per_category_accuracy = metrics.pop("per_category_accuracy").tolist()
... per_category_iou = metrics.pop("per_category_iou").tolist()
... metrics.update({f"accuracy_{id2label[i]}": v for i, v in enumerate(per_category_accuracy)})
... metrics.update({f"iou_{id2label[i]}": v for i, v in enumerate(per_category_iou)})
... return {"val_" + k: v for k, v in metrics.items()}
```
</tf>
</frameworkcontent>
Your `compute_metrics` function is ready to go now, and you'll return to it when you setup your training.
### Train
<frameworkcontent>
<pt>
<Tip>
If you aren't familiar with finetuning a model with the [`Trainer`], take a look at the basic tutorial [here](../training#finetune-with-trainer)!
</Tip>
You're ready to start training your model now! Load SegFormer with [`AutoModelForSemanticSegmentation`], and pass the model the mapping between label ids and label classes:
```py
>>> from transformers import AutoModelForSemanticSegmentation, TrainingArguments, Trainer
>>> model = AutoModelForSemanticSegmentation.from_pretrained(checkpoint, id2label=id2label, label2id=label2id)
```
At this point, only three steps remain:
1. Define your training hyperparameters in [`TrainingArguments`]. It is important you don't remove unused columns because this'll drop the `image` column. Without the `image` column, you can't create `pixel_values`. Set `remove_unused_columns=False` to prevent this behavior! The only other required parameter is `output_dir` which specifies where to save your model. You'll push this model to the Hub by setting `push_to_hub=True` (you need to be signed in to Hugging Face to upload your model). At the end of each epoch, the [`Trainer`] will evaluate the IoU metric and save the training checkpoint.
2. Pass the training arguments to [`Trainer`] along with the model, dataset, tokenizer, data collator, and `compute_metrics` function.
3. Call [`~Trainer.train`] to finetune your model.
```py
>>> training_args = TrainingArguments(
... output_dir="segformer-b0-scene-parse-150",
... learning_rate=6e-5,
... num_train_epochs=50,
... per_device_train_batch_size=2,
... per_device_eval_batch_size=2,
... save_total_limit=3,
... evaluation_strategy="steps",
... save_strategy="steps",
... save_steps=20,
... eval_steps=20,
... logging_steps=1,
... eval_accumulation_steps=5,
... remove_unused_columns=False,
... push_to_hub=True,
... )
>>> trainer = Trainer(
... model=model,
... args=training_args,
... train_dataset=train_ds,
... eval_dataset=test_ds,
... compute_metrics=compute_metrics,
... )
>>> trainer.train()
```
Once training is completed, share your model to the Hub with the [`~transformers.Trainer.push_to_hub`] method so everyone can use your model:
```py
>>> trainer.push_to_hub()
```
</pt>
</frameworkcontent>
<frameworkcontent>
<tf>
<Tip>
If you are unfamiliar with fine-tuning a model with Keras, check out the [basic tutorial](./training#train-a-tensorflow-model-with-keras) first!
</Tip>
To fine-tune a model in TensorFlow, follow these steps:
1. Define the training hyperparameters, and set up an optimizer and a learning rate schedule.
2. Instantiate a pretrained model.
3. Convert a 🤗 Dataset to a `tf.data.Dataset`.
4. Compile your model.
5. Add callbacks to calculate metrics and upload your model to 🤗 Hub
6. Use the `fit()` method to run the training.
Start by defining the hyperparameters, optimizer and learning rate schedule:
```py
>>> from transformers import create_optimizer
>>> batch_size = 2
>>> num_epochs = 50
>>> num_train_steps = len(train_ds) * num_epochs
>>> learning_rate = 6e-5
>>> weight_decay_rate = 0.01
>>> optimizer, lr_schedule = create_optimizer(
... init_lr=learning_rate,
... num_train_steps=num_train_steps,
... weight_decay_rate=weight_decay_rate,
... num_warmup_steps=0,
... )
```
Then, load SegFormer with [`TFAutoModelForSemanticSegmentation`] along with the label mappings, and compile it with the
optimizer. Note that Transformers models all have a default task-relevant loss function, so you don't need to specify one unless you want to:
```py
>>> from transformers import TFAutoModelForSemanticSegmentation
>>> model = TFAutoModelForSemanticSegmentation.from_pretrained(
... checkpoint,
... id2label=id2label,
... label2id=label2id,
... )
>>> model.compile(optimizer=optimizer) # No loss argument!
```
Convert your datasets to the `tf.data.Dataset` format using the [`~datasets.Dataset.to_tf_dataset`] and the [`DefaultDataCollator`]:
```py
>>> from transformers import DefaultDataCollator
>>> data_collator = DefaultDataCollator(return_tensors="tf")
>>> tf_train_dataset = train_ds.to_tf_dataset(
... columns=["pixel_values", "label"],
... shuffle=True,
... batch_size=batch_size,
... collate_fn=data_collator,
... )
>>> tf_eval_dataset = test_ds.to_tf_dataset(
... columns=["pixel_values", "label"],
... shuffle=True,
... batch_size=batch_size,
... collate_fn=data_collator,
... )
```
To compute the accuracy from the predictions and push your model to the 🤗 Hub, use [Keras callbacks](../main_classes/keras_callbacks).
Pass your `compute_metrics` function to [`KerasMetricCallback`],
and use the [`PushToHubCallback`] to upload the model:
```py
>>> from transformers.keras_callbacks import KerasMetricCallback, PushToHubCallback
>>> metric_callback = KerasMetricCallback(
... metric_fn=compute_metrics, eval_dataset=tf_eval_dataset, batch_size=batch_size, label_cols=["labels"]
... )
>>> push_to_hub_callback = PushToHubCallback(output_dir="scene_segmentation", tokenizer=image_processor)
>>> callbacks = [metric_callback, push_to_hub_callback]
```
Finally, you are ready to train your model! Call `fit()` with your training and validation datasets, the number of epochs,
and your callbacks to fine-tune the model:
```py
>>> model.fit(
... tf_train_dataset,
... validation_data=tf_eval_dataset,
... callbacks=callbacks,
... epochs=num_epochs,
... )
```
Congratulations! You have fine-tuned your model and shared it on the 🤗 Hub. You can now use it for inference!
</tf>
</frameworkcontent>
### Inference
Great, now that you've finetuned a model, you can use it for inference!
Load an image for inference:
```py
>>> image = ds[0]["image"]
>>> image
```
<div class="flex justify-center">
<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/semantic-seg-image.png" alt="Image of bedroom"/>
</div>
<frameworkcontent>
<pt>
We will now see how to infer without a pipeline. Process the image with an image processor and place the `pixel_values` on a GPU:
```py
>>> device = torch.device("cuda" if torch.cuda.is_available() else "cpu") # use GPU if available, otherwise use a CPU
>>> encoding = image_processor(image, return_tensors="pt")
>>> pixel_values = encoding.pixel_values.to(device)
```
Pass your input to the model and return the `logits`:
```py
>>> outputs = model(pixel_values=pixel_values)
>>> logits = outputs.logits.cpu()
```
Next, rescale the logits to the original image size:
```py
>>> upsampled_logits = nn.functional.interpolate(
... logits,
... size=image.size[::-1],
... mode="bilinear",
... align_corners=False,
... )
>>> pred_seg = upsampled_logits.argmax(dim=1)[0]
```
</pt>
</frameworkcontent>
<frameworkcontent>
<tf>
Load an image processor to preprocess the image and return the input as TensorFlow tensors:
```py
>>> from transformers import AutoImageProcessor
>>> image_processor = AutoImageProcessor.from_pretrained("MariaK/scene_segmentation")
>>> inputs = image_processor(image, return_tensors="tf")
```
Pass your input to the model and return the `logits`:
```py
>>> from transformers import TFAutoModelForSemanticSegmentation
>>> model = TFAutoModelForSemanticSegmentation.from_pretrained("MariaK/scene_segmentation")
>>> logits = model(**inputs).logits
```
Next, rescale the logits to the original image size and apply argmax on the class dimension:
```py
>>> logits = tf.transpose(logits, [0, 2, 3, 1])
>>> upsampled_logits = tf.image.resize(
... logits,
... # We reverse the shape of `image` because `image.size` returns width and height.
... image.size[::-1],
... )
>>> pred_seg = tf.math.argmax(upsampled_logits, axis=-1)[0]
```
</tf>
</frameworkcontent>
To visualize the results, load the [dataset color palette](https://github.com/tensorflow/models/blob/3f1ca33afe3c1631b733ea7e40c294273b9e406d/research/deeplab/utils/get_dataset_colormap.py#L51) as `ade_palette()` that maps each class to their RGB values. Then you can combine and plot your image and the predicted segmentation map:
```py
>>> import matplotlib.pyplot as plt
>>> import numpy as np
>>> color_seg = np.zeros((pred_seg.shape[0], pred_seg.shape[1], 3), dtype=np.uint8)
>>> palette = np.array(ade_palette())
>>> for label, color in enumerate(palette):
... color_seg[pred_seg == label, :] = color
>>> color_seg = color_seg[..., ::-1] # convert to BGR
>>> img = np.array(image) * 0.5 + color_seg * 0.5 # plot the image with the segmentation map
>>> img = img.astype(np.uint8)
>>> plt.figure(figsize=(15, 10))
>>> plt.imshow(img)
>>> plt.show()
```
<div class="flex justify-center">
<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/semantic-seg-preds.png" alt="Image of bedroom overlaid with segmentation map"/>
</div>
| huggingface/transformers/blob/main/docs/source/en/tasks/semantic_segmentation.md |
!--Copyright 2023 The HuggingFace Team. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
the License. You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
specific language governing permissions and limitations under the License.
⚠️ Note that this file is in Markdown but contain specific syntax for our doc-builder (similar to MDX) that may not be
rendered properly in your Markdown viewer.
-->
# MGP-STR
## Overview
The MGP-STR model was proposed in [Multi-Granularity Prediction for Scene Text Recognition](https://arxiv.org/abs/2209.03592) by Peng Wang, Cheng Da, and Cong Yao. MGP-STR is a conceptually **simple** yet **powerful** vision Scene Text Recognition (STR) model, which is built upon the [Vision Transformer (ViT)](vit). To integrate linguistic knowledge, Multi-Granularity Prediction (MGP) strategy is proposed to inject information from the language modality into the model in an implicit way.
The abstract from the paper is the following:
*Scene text recognition (STR) has been an active research topic in computer vision for years. To tackle this challenging problem, numerous innovative methods have been successively proposed and incorporating linguistic knowledge into STR models has recently become a prominent trend. In this work, we first draw inspiration from the recent progress in Vision Transformer (ViT) to construct a conceptually simple yet powerful vision STR model, which is built upon ViT and outperforms previous state-of-the-art models for scene text recognition, including both pure vision models and language-augmented methods. To integrate linguistic knowledge, we further propose a Multi-Granularity Prediction strategy to inject information from the language modality into the model in an implicit way, i.e. , subword representations (BPE and WordPiece) widely-used in NLP are introduced into the output space, in addition to the conventional character level representation, while no independent language model (LM) is adopted. The resultant algorithm (termed MGP-STR) is able to push the performance envelop of STR to an even higher level. Specifically, it achieves an average recognition accuracy of 93.35% on standard benchmarks.*
<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/model_doc/mgp_str_architecture.png"
alt="drawing" width="600"/>
<small> MGP-STR architecture. Taken from the <a href="https://arxiv.org/abs/2209.03592">original paper</a>. </small>
MGP-STR is trained on two synthetic datasets [MJSynth]((http://www.robots.ox.ac.uk/~vgg/data/text/)) (MJ) and SynthText(http://www.robots.ox.ac.uk/~vgg/data/scenetext/) (ST) without fine-tuning on other datasets. It achieves state-of-the-art results on six standard Latin scene text benchmarks, including 3 regular text datasets (IC13, SVT, IIIT) and 3 irregular ones (IC15, SVTP, CUTE).
This model was contributed by [yuekun](https://huggingface.co/yuekun). The original code can be found [here](https://github.com/AlibabaResearch/AdvancedLiterateMachinery/tree/main/OCR/MGP-STR).
## Inference example
[`MgpstrModel`] accepts images as input and generates three types of predictions, which represent textual information at different granularities.
The three types of predictions are fused to give the final prediction result.
The [`ViTImageProcessor`] class is responsible for preprocessing the input image and
[`MgpstrTokenizer`] decodes the generated character tokens to the target string. The
[`MgpstrProcessor`] wraps [`ViTImageProcessor`] and [`MgpstrTokenizer`]
into a single instance to both extract the input features and decode the predicted token ids.
- Step-by-step Optical Character Recognition (OCR)
```py
>>> from transformers import MgpstrProcessor, MgpstrForSceneTextRecognition
>>> import requests
>>> from PIL import Image
>>> processor = MgpstrProcessor.from_pretrained('alibaba-damo/mgp-str-base')
>>> model = MgpstrForSceneTextRecognition.from_pretrained('alibaba-damo/mgp-str-base')
>>> # load image from the IIIT-5k dataset
>>> url = "https://i.postimg.cc/ZKwLg2Gw/367-14.png"
>>> image = Image.open(requests.get(url, stream=True).raw).convert("RGB")
>>> pixel_values = processor(images=image, return_tensors="pt").pixel_values
>>> outputs = model(pixel_values)
>>> generated_text = processor.batch_decode(outputs.logits)['generated_text']
```
## MgpstrConfig
[[autodoc]] MgpstrConfig
## MgpstrTokenizer
[[autodoc]] MgpstrTokenizer
- save_vocabulary
## MgpstrProcessor
[[autodoc]] MgpstrProcessor
- __call__
- batch_decode
## MgpstrModel
[[autodoc]] MgpstrModel
- forward
## MgpstrForSceneTextRecognition
[[autodoc]] MgpstrForSceneTextRecognition
- forward
| huggingface/transformers/blob/main/docs/source/en/model_doc/mgp-str.md |
Zero-shot classifier distillation
Author: @joeddav
This script provides a way to improve the speed and memory performance of a zero-shot classifier by training a more
efficient student model from the zero-shot teacher's predictions over an unlabeled dataset.
The zero-shot classification pipeline uses a model pre-trained on natural language inference (NLI) to determine the
compatibility of a set of candidate class names with a given sequence. This serves as a convenient out-of-the-box
classifier without the need for labeled training data. However, for a given sequence, the method requires each
possible label to be fed through the large NLI model separately. Thus for `N` sequences and `K` classes, a total of
`N*K` forward passes through the model are required. This requirement slows inference considerably, particularly as
`K` grows.
Given (1) an unlabeled corpus and (2) a set of candidate class names, the provided script trains a student model
with a standard classification head with `K` output dimensions. The resulting student model can then be used for
classifying novel text instances with a significant boost in speed and memory performance while retaining similar
classification performance to the original zero-shot model
### Usage
A teacher NLI model can be distilled to a more efficient student model by running [`distill_classifier.py`](https://github.com/huggingface/transformers/blob/main/examples/research_projects/zero-shot-distillation/distill_classifier.py):
```
python distill_classifier.py \
--data_file <unlabeled_data.txt> \
--class_names_file <class_names.txt> \
--output_dir <output_dir>
```
`<unlabeled_data.txt>` should be a text file with a single unlabeled example per line. `<class_names.txt>` is a text file with one class name per line.
Other optional arguments include:
- `--teacher_name_or_path` (default: `roberta-large-mnli`): The name or path of the NLI teacher model.
- `--student_name_or_path` (default: `distillbert-base-uncased`): The name or path of the student model which will
be fine-tuned to copy the teacher predictions.
- `--hypothesis_template` (default `"This example is {}."`): The template used to turn each label into an NLI-style
hypothesis when generating teacher predictions. This template must include a `{}` or similar syntax for the
candidate label to be inserted into the template. For example, the default template is `"This example is {}."` With
the candidate label `sports`, this would be fed into the model like `[CLS] sequence to classify [SEP] This example
is sports . [SEP]`.
- `--multi_class`: Whether or not multiple candidate labels can be true. By default, the scores are normalized such
that the sum of the label likelihoods for each sequence is 1. If `--multi_class` is passed, the labels are
considered independent and probabilities are normalized for each candidate by doing a softmax of the entailment
score vs. the contradiction score. This is sometimes called "multi-class multi-label" classification.
- `--temperature` (default: `1.0`): The temperature applied to the softmax of the teacher model predictions. A
higher temperature results in a student with smoother (lower confidence) predictions than the teacher while a value
`<1` resultings in a higher-confidence, peaked distribution. The default `1.0` is equivalent to no smoothing.
- `--teacher_batch_size` (default: `32`): The batch size used for generating a single set of teacher predictions.
Does not affect training. Use `--per_device_train_batch_size` to change the training batch size.
Any of the arguments in the 🤗 Trainer's
[`TrainingArguments`](https://huggingface.co/transformers/main_classes/trainer.html?#trainingarguments) can also be
modified, such as `--learning_rate`, `--fp16`, `--no_cuda`, `--warmup_steps`, etc. Run `python distill_classifier.py
-h` for a full list of available arguments or consult the [Trainer
documentation](https://huggingface.co/transformers/main_classes/trainer.html#trainingarguments).
> **Note**: Distributed and TPU training are not currently supported. Single-node multi-GPU is supported, however,
and will run automatically if multiple GPUs are available.
### Example: Topic classification
> A full colab demo notebook of this example can be found [here](https://colab.research.google.com/drive/1mjBjd0cR8G57ZpsnFCS3ngGyo5nCa9ya?usp=sharing).
Let's say we're interested in classifying news articles into one of four topic categories: "the world", "sports",
"business", or "science/tech". We have an unlabeled dataset, [AG's News](https://huggingface.co/datasets/ag_news),
which corresponds to this problem (in reality AG's News is annotated, but we will pretend it is not for the sake of
example).
We can use an NLI model like `roberta-large-mnli` for zero-shot classification like so:
```python
>>> class_names = ["the world", "sports", "business", "science/tech"]
>>> hypothesis_template = "This text is about {}."
>>> sequence = "A new moon has been discovered in Jupiter's orbit"
>>> zero_shot_classifier = pipeline("zero-shot-classification", model="roberta-large-mnli")
>>> zero_shot_classifier(sequence, class_names, hypothesis_template=hypothesis_template)
{'sequence': "A new moon has been discovered in Jupiter's orbit",
'labels': ['science/tech', 'the world', 'business', 'sports'],
'scores': [0.7035840153694153, 0.18744826316833496, 0.06027870625257492, 0.04868902638554573]}
```
Unfortunately, inference is slow since each of our 4 class names must be fed through the large model for every
sequence to be classified. But with our unlabeled data we can distill the model to a small distilbert classifier to
make future inference much faster.
To run the script, we will need to put each training example (text only) from AG's News on its own line in
`agnews/train_unlabeled.txt`, and each of the four class names in the newline-separated `agnews/class_names.txt`.
Then we can run distillation with the following command:
```bash
python distill_classifier.py \
--data_file ./agnews/unlabeled.txt \
--class_names_files ./agnews/class_names.txt \
--teacher_name_or_path roberta-large-mnli \
--hypothesis_template "This text is about {}." \
--output_dir ./agnews/distilled
```
The script will generate a set of soft zero-shot predictions from `roberta-large-mnli` for each example in
`agnews/unlabeled.txt`. It will then train a student distilbert classifier on the teacher predictions and
save the resulting model in `./agnews/distilled`.
The resulting model can then be loaded and used like any other pre-trained classifier:
```python
from transformers import AutoModelForSequenceClassification, AutoTokenizer
model = AutoModelForSequenceClassification.from_pretrained("./agnews/distilled")
tokenizer = AutoTokenizer.from_pretrained("./agnews/distilled")
```
and even used trivially with a `TextClassificationPipeline`:
```python
>>> distilled_classifier = TextClassificationPipeline(model=model, tokenizer=tokenizer, return_all_scores=True)
>>> distilled_classifier(sequence)
[[{'label': 'the world', 'score': 0.14899294078350067},
{'label': 'sports', 'score': 0.03205857425928116},
{'label': 'business', 'score': 0.05943061783909798},
{'label': 'science/tech', 'score': 0.7595179080963135}]]
```
> Tip: pass `device=0` when constructing a pipeline to run on a GPU
As we can see, the results of the student closely resemble that of the trainer despite never having seen this
example during training. Now let's do a quick & dirty speed comparison simulating 16K examples with a batch size of
16:
```python
for _ in range(1000):
zero_shot_classifier([sequence] * 16, class_names)
# runs in 1m 23s on a single V100 GPU
```
```python
%%time
for _ in range(1000):
distilled_classifier([sequence] * 16)
# runs in 10.3s on a single V100 GPU
```
As we can see, the distilled student model runs an order of magnitude faster than its teacher NLI model. This is
also a seeting where we only have `K=4` possible labels. The higher the number of classes for a given task, the more
drastic the speedup will be, since the zero-shot teacher's complexity scales linearly with the number of classes.
Since we secretly have access to ground truth labels for AG's news, we can evaluate the accuracy of each model. The
original zero-shot model `roberta-large-mnli` gets an accuracy of 69.3% on the held-out test set. After training a
student on the unlabeled training set, the distilled model gets a similar score of 70.4%.
Lastly, you can share the distilled model with the community and/or use it with our inference API by [uploading it
to the 🤗 Hub](https://huggingface.co/transformers/model_sharing.html). We've uploaded the distilled model from this
example at
[joeddav/distilbert-base-uncased-agnews-student](https://huggingface.co/joeddav/distilbert-base-uncased-agnews-student).
| huggingface/transformers/blob/main/examples/research_projects/zero-shot-distillation/README.md |
!--Copyright 2023 Mistral AI and The HuggingFace Team. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
the License. You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
specific language governing permissions and limitations under the License.
⚠️ Note that this file is in Markdown but contain specific syntax for our doc-builder (similar to MDX) that may not be
rendered properly in your Markdown viewer.
-->
# Mixtral
## Overview
Mixtral-8x7B is Mistral AI's second Large Language Model (LLM).
The Mixtral model was proposed by the [Mistral AI](https://mistral.ai/) team.
It was introduced in the [Mixtral of Experts blogpost](https://mistral.ai/news/mixtral-of-experts/) with the following introduction:
*Today, the team is proud to release Mixtral 8x7B, a high-quality sparse mixture of experts models (SMoE) with open weights. Licensed under Apache 2.0. Mixtral outperforms Llama 2 70B on most benchmarks with 6x faster inference. It is the strongest open-weight model with a permissive license and the best model overall regarding cost/performance trade-offs. In particular, it matches or outperforms GPT3.5 on most standard benchmarks.*
Tips:
- The model needs to be converted using the [conversion script](https://github.com/huggingface/transformers/blob/main/src/transformers/models/mixtral/convert_mixtral_weights_to_hf.py).
- If the model is quantized to 4bits, a single A100 is enough to fit the entire 45B model.
This model was contributed by [Younes Belkada](https://huggingface.co/ybelkada) and [Arthur Zucker](https://huggingface.co/ArthurZ) .
The original code can be found [here](https://github.com/mistralai/mistral-src).
### Model Details
Mixtral-45B is a decoder-based LM with the following architectural choices:
* Mixtral is a Mixture of Expert (MOE) model with 8 experts per MLP, with a total of 45B paramateres but the compute required is the same as a 14B model. This is because even though each experts have to be loaded in RAM (70B like ram requirement) each token from the hidden states are dipatched twice (top 2 routing) and thus the compute (the operation required at each foward computation) is just 2 X sequence_length.
The following implementation details are shared with Mistral AI's first model [mistral](mistral):
* Sliding Window Attention - Trained with 8k context length and fixed cache size, with a theoretical attention span of 128K tokens
* GQA (Grouped Query Attention) - allowing faster inference and lower cache size.
* Byte-fallback BPE tokenizer - ensures that characters are never mapped to out of vocabulary tokens.
They also provide an instruction fine-tuned model: `mistralai/Mixtral-8x7B-v0.1` which can be used for chat-based inference.
For more details please read our [release blog post](https://mistral.ai/news/mixtral-of-experts/)
### License
`Mixtral-8x7B` is released under the Apache 2.0 license.
## Usage tips
`Mixtral-8x7B` can be found on the [Huggingface Hub](https://huggingface.co/mistralai)
These ready-to-use checkpoints can be downloaded and used via the HuggingFace Hub:
```python
>>> from transformers import AutoModelForCausalLM, AutoTokenizer
>>> device = "cuda" # the device to load the model onto
>>> model = AutoModelForCausalLM.from_pretrained("mistralai/Mixtral-8x7B-v0.1")
>>> tokenizer = AutoTokenizer.from_pretrained("mistralai/Mistral-8x7B")
>>> prompt = "My favourite condiment is"
>>> model_inputs = tokenizer([prompt], return_tensors="pt").to(device)
>>> model.to(device)
>>> generated_ids = model.generate(**model_inputs, max_new_tokens=100, do_sample=True)
>>> tokenizer.batch_decode(generated_ids)[0]
"The expected output"
```
To use the raw checkpoints with HuggingFace you can use the `convert_mixtral_weights_to_hf.py` script to convert them to the HuggingFace format:
```bash
python src/transformers/models/mixtral/convert_mixtral_weights_to_hf.py \
--input_dir /path/to/downloaded/mistral/weights --output_dir /output/path
```
You can then load the converted model from the `output/path`:
```python
from transformers import MixtralForCausalLM, LlamaTokenizer
tokenizer = LlamaTokenizer.from_pretrained("/output/path")
model = MixtralForCausalLM.from_pretrained("/output/path")
```
## Combining Mixtral and Flash Attention 2
First, make sure to install the latest version of Flash Attention 2 to include the sliding window attention feature.
```bash
pip install -U flash-attn --no-build-isolation
```
Make also sure that you have a hardware that is compatible with Flash-Attention 2. Read more about it in the official documentation of [`flash-attn`](https://github.com/Dao-AILab/flash-attention) repository. Make also sure to load your model in half-precision (e.g. `torch.float16`)
To load and run a model using Flash Attention 2, refer to the snippet below:
```python
>>> import torch
>>> from transformers import AutoModelForCausalLM, AutoTokenizer
>>> device = "cuda" # the device to load the model onto
>>> model = AutoModelForCausalLM.from_pretrained("mistralai/Mixtral-8x7B-v0.1", torch_dtype=torch.float16, attn_implementation="flash_attention_2")
>>> tokenizer = AutoTokenizer.from_pretrained("mistralai/Mixtral-8x7B-v0.1")
>>> prompt = "My favourite condiment is"
>>> model_inputs = tokenizer([prompt], return_tensors="pt").to(device)
>>> model.to(device)
>>> generated_ids = model.generate(**model_inputs, max_new_tokens=100, do_sample=True)
>>> tokenizer.batch_decode(generated_ids)[0]
"The expected output"
```
### Expected speedups
Below is a expected speedup diagram that compares pure inference time between the native implementation in transformers using `mistralai/Mixtral-8x7B-v0.1` checkpoint and the Flash Attention 2 version of the model.
<div style="text-align: center">
<img src="https://huggingface.co/datasets/ybelkada/documentation-images/resolve/main/mixtral-7b-inference-large-seqlen.png">
</div>
### Sliding window Attention
The current implementation supports the sliding window attention mechanism and memory efficient cache management.
To enable sliding window attention, just make sure to have a `flash-attn` version that is compatible with sliding window attention (`>=2.3.0`).
The Flash Attention-2 model uses also a more memory efficient cache slicing mechanism - as recommended per the official implementation of Mistral model that use rolling cache mechanism we keep the cache size fixed (`self.config.sliding_window`), support batched generation only for `padding_side="left"` and use the absolute position of the current token to compute the positional embedding.
## The Mistral Team
Albert Jiang, Alexandre Sablayrolles, Arthur Mensch, Chris Bamford, Devendra Singh Chaplot, Diego de las Casas, Florian Bressand, Gianna Lengyel, Guillaume Lample, Lélio Renard Lavaud, Lucile Saulnier, Marie-Anne Lachaux, Pierre Stock, Teven Le Scao, Thibaut Lavril, Thomas Wang, Timothée Lacroix, William El Sayed.
## MixtralConfig
[[autodoc]] MixtralConfig
## MixtralModel
[[autodoc]] MixtralModel
- forward
## MixtralForCausalLM
[[autodoc]] MixtralForCausalLM
- forward
## MixtralForSequenceClassification
[[autodoc]] MixtralForSequenceClassification
- forward
| huggingface/transformers/blob/main/docs/source/en/model_doc/mixtral.md |
!--Copyright 2023 The HuggingFace Team. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
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Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
specific language governing permissions and limitations under the License.
⚠️ Note that this file is in Markdown but contain specific syntax for our doc-builder (similar to MDX) that may not be
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-->
# Custom Tools and Prompts
<Tip>
If you are not aware of what tools and agents are in the context of transformers, we recommend you read the
[Transformers Agents](transformers_agents) page first.
</Tip>
<Tip warning={true}>
Transformers Agents is an experimental API that is subject to change at any time. Results returned by the agents
can vary as the APIs or underlying models are prone to change.
</Tip>
Creating and using custom tools and prompts is paramount to empowering the agent and having it perform new tasks.
In this guide we'll take a look at:
- How to customize the prompt
- How to use custom tools
- How to create custom tools
## Customizing the prompt
As explained in [Transformers Agents](transformers_agents) agents can run in [`~Agent.run`] and [`~Agent.chat`] mode.
Both the `run` and `chat` modes underlie the same logic. The language model powering the agent is conditioned on a long
prompt and completes the prompt by generating the next tokens until the stop token is reached.
The only difference between the two modes is that during the `chat` mode the prompt is extended with
previous user inputs and model generations. This allows the agent to have access to past interactions,
seemingly giving the agent some kind of memory.
### Structure of the prompt
Let's take a closer look at how the prompt is structured to understand how it can be best customized.
The prompt is structured broadly into four parts.
- 1. Introduction: how the agent should behave, explanation of the concept of tools.
- 2. Description of all the tools. This is defined by a `<<all_tools>>` token that is dynamically replaced at runtime with the tools defined/chosen by the user.
- 3. A set of examples of tasks and their solution
- 4. Current example, and request for solution.
To better understand each part, let's look at a shortened version of how the `run` prompt can look like:
````text
I will ask you to perform a task, your job is to come up with a series of simple commands in Python that will perform the task.
[...]
You can print intermediate results if it makes sense to do so.
Tools:
- document_qa: This is a tool that answers a question about a document (pdf). It takes an input named `document` which should be the document containing the information, as well as a `question` that is the question about the document. It returns a text that contains the answer to the question.
- image_captioner: This is a tool that generates a description of an image. It takes an input named `image` which should be the image to the caption and returns a text that contains the description in English.
[...]
Task: "Answer the question in the variable `question` about the image stored in the variable `image`. The question is in French."
I will use the following tools: `translator` to translate the question into English and then `image_qa` to answer the question on the input image.
Answer:
```py
translated_question = translator(question=question, src_lang="French", tgt_lang="English")
print(f"The translated question is {translated_question}.")
answer = image_qa(image=image, question=translated_question)
print(f"The answer is {answer}")
```
Task: "Identify the oldest person in the `document` and create an image showcasing the result as a banner."
I will use the following tools: `document_qa` to find the oldest person in the document, then `image_generator` to generate an image according to the answer.
Answer:
```py
answer = document_qa(document, question="What is the oldest person?")
print(f"The answer is {answer}.")
image = image_generator("A banner showing " + answer)
```
[...]
Task: "Draw me a picture of rivers and lakes"
I will use the following
````
The introduction (the text before *"Tools:"*) explains precisely how the model shall behave and what it should do.
This part most likely does not need to be customized as the agent shall always behave the same way.
The second part (the bullet points below *"Tools"*) is dynamically added upon calling `run` or `chat`. There are
exactly as many bullet points as there are tools in `agent.toolbox` and each bullet point consists of the name
and description of the tool:
```text
- <tool.name>: <tool.description>
```
Let's verify this quickly by loading the document_qa tool and printing out the name and description.
```py
from transformers import load_tool
document_qa = load_tool("document-question-answering")
print(f"- {document_qa.name}: {document_qa.description}")
```
which gives:
```text
- document_qa: This is a tool that answers a question about a document (pdf). It takes an input named `document` which should be the document containing the information, as well as a `question` that is the question about the document. It returns a text that contains the answer to the question.
```
We can see that the tool name is short and precise. The description includes two parts, the first explaining
what the tool does and the second states what input arguments and return values are expected.
A good tool name and tool description are very important for the agent to correctly use it. Note that the only
information the agent has about the tool is its name and description, so one should make sure that both
are precisely written and match the style of the existing tools in the toolbox. In particular make sure the description
mentions all the arguments expected by name in code-style, along with the expected type and a description of what they
are.
<Tip>
Check the naming and description of the curated Transformers tools to better understand what name and
description a tool is expected to have. You can see all tools with the [`Agent.toolbox`] property.
</Tip>
The third part includes a set of curated examples that show the agent exactly what code it should produce
for what kind of user request. The large language models empowering the agent are extremely good at
recognizing patterns in a prompt and repeating the pattern with new data. Therefore, it is very important
that the examples are written in a way that maximizes the likelihood of the agent to generating correct,
executable code in practice.
Let's have a look at one example:
````text
Task: "Identify the oldest person in the `document` and create an image showcasing the result as a banner."
I will use the following tools: `document_qa` to find the oldest person in the document, then `image_generator` to generate an image according to the answer.
Answer:
```py
answer = document_qa(document, question="What is the oldest person?")
print(f"The answer is {answer}.")
image = image_generator("A banner showing " + answer)
```
````
The pattern the model is prompted to repeat has three parts: The task statement, the agent's explanation of
what it intends to do, and finally the generated code. Every example that is part of the prompt has this exact
pattern, thus making sure that the agent will reproduce exactly the same pattern when generating new tokens.
The prompt examples are curated by the Transformers team and rigorously evaluated on a set of
[problem statements](https://github.com/huggingface/transformers/blob/main/src/transformers/tools/evaluate_agent.py)
to ensure that the agent's prompt is as good as possible to solve real use cases of the agent.
The final part of the prompt corresponds to:
```text
Task: "Draw me a picture of rivers and lakes"
I will use the following
```
is a final and unfinished example that the agent is tasked to complete. The unfinished example
is dynamically created based on the actual user input. For the above example, the user ran:
```py
agent.run("Draw me a picture of rivers and lakes")
```
The user input - *a.k.a* the task: *"Draw me a picture of rivers and lakes"* is cast into the
prompt template: "Task: <task> \n\n I will use the following". This sentence makes up the final lines of the
prompt the agent is conditioned on, therefore strongly influencing the agent to finish the example
exactly in the same way it was previously done in the examples.
Without going into too much detail, the chat template has the same prompt structure with the
examples having a slightly different style, *e.g.*:
````text
[...]
=====
Human: Answer the question in the variable `question` about the image stored in the variable `image`.
Assistant: I will use the tool `image_qa` to answer the question on the input image.
```py
answer = image_qa(text=question, image=image)
print(f"The answer is {answer}")
```
Human: I tried this code, it worked but didn't give me a good result. The question is in French
Assistant: In this case, the question needs to be translated first. I will use the tool `translator` to do this.
```py
translated_question = translator(question=question, src_lang="French", tgt_lang="English")
print(f"The translated question is {translated_question}.")
answer = image_qa(text=translated_question, image=image)
print(f"The answer is {answer}")
```
=====
[...]
````
Contrary, to the examples of the `run` prompt, each `chat` prompt example has one or more exchanges between the
*Human* and the *Assistant*. Every exchange is structured similarly to the example of the `run` prompt.
The user's input is appended to behind *Human:* and the agent is prompted to first generate what needs to be done
before generating code. An exchange can be based on previous exchanges, therefore allowing the user to refer
to past exchanges as is done *e.g.* above by the user's input of "I tried **this** code" refers to the
previously generated code of the agent.
Upon running `.chat`, the user's input or *task* is cast into an unfinished example of the form:
```text
Human: <user-input>\n\nAssistant:
```
which the agent completes. Contrary to the `run` command, the `chat` command then appends the completed example
to the prompt, thus giving the agent more context for the next `chat` turn.
Great now that we know how the prompt is structured, let's see how we can customize it!
### Writing good user inputs
While large language models are getting better and better at understanding users' intentions, it helps
enormously to be as precise as possible to help the agent pick the correct task. What does it mean to be
as precise as possible?
The agent sees a list of tool names and their description in its prompt. The more tools are added the
more difficult it becomes for the agent to choose the correct tool and it's even more difficult to choose
the correct sequences of tools to run. Let's look at a common failure case, here we will only return
the code to analyze it.
```py
from transformers import HfAgent
agent = HfAgent("https://api-inference.huggingface.co/models/bigcode/starcoder")
agent.run("Show me a tree", return_code=True)
```
gives:
```text
==Explanation from the agent==
I will use the following tool: `image_segmenter` to create a segmentation mask for the image.
==Code generated by the agent==
mask = image_segmenter(image, prompt="tree")
```
which is probably not what we wanted. Instead, it is more likely that we want an image of a tree to be generated.
To steer the agent more towards using a specific tool it can therefore be very helpful to use important keywords that
are present in the tool's name and description. Let's have a look.
```py
agent.toolbox["image_generator"].description
```
```text
'This is a tool that creates an image according to a prompt, which is a text description. It takes an input named `prompt` which contains the image description and outputs an image.
```
The name and description make use of the keywords "image", "prompt", "create" and "generate". Using these words will most likely work better here. Let's refine our prompt a bit.
```py
agent.run("Create an image of a tree", return_code=True)
```
gives:
```text
==Explanation from the agent==
I will use the following tool `image_generator` to generate an image of a tree.
==Code generated by the agent==
image = image_generator(prompt="tree")
```
Much better! That looks more like what we want. In short, when you notice that the agent struggles to
correctly map your task to the correct tools, try looking up the most pertinent keywords of the tool's name
and description and try refining your task request with it.
### Customizing the tool descriptions
As we've seen before the agent has access to each of the tools' names and descriptions. The base tools
should have very precise names and descriptions, however, you might find that it could help to change the
the description or name of a tool for your specific use case. This might become especially important
when you've added multiple tools that are very similar or if you want to use your agent only for a certain
domain, *e.g.* image generation and transformations.
A common problem is that the agent confuses image generation with image transformation/modification when
used a lot for image generation tasks, *e.g.*
```py
agent.run("Make an image of a house and a car", return_code=True)
```
returns
```text
==Explanation from the agent==
I will use the following tools `image_generator` to generate an image of a house and `image_transformer` to transform the image of a car into the image of a house.
==Code generated by the agent==
house_image = image_generator(prompt="A house")
car_image = image_generator(prompt="A car")
house_car_image = image_transformer(image=car_image, prompt="A house")
```
which is probably not exactly what we want here. It seems like the agent has a difficult time
to understand the difference between `image_generator` and `image_transformer` and often uses the two together.
We can help the agent here by changing the tool name and description of `image_transformer`. Let's instead call it `modifier`
to disassociate it a bit from "image" and "prompt":
```py
agent.toolbox["modifier"] = agent.toolbox.pop("image_transformer")
agent.toolbox["modifier"].description = agent.toolbox["modifier"].description.replace(
"transforms an image according to a prompt", "modifies an image"
)
```
Now "modify" is a strong cue to use the new image processor which should help with the above prompt. Let's run it again.
```py
agent.run("Make an image of a house and a car", return_code=True)
```
Now we're getting:
```text
==Explanation from the agent==
I will use the following tools: `image_generator` to generate an image of a house, then `image_generator` to generate an image of a car.
==Code generated by the agent==
house_image = image_generator(prompt="A house")
car_image = image_generator(prompt="A car")
```
which is definitely closer to what we had in mind! However, we want to have both the house and car in the same image. Steering the task more toward single image generation should help:
```py
agent.run("Create image: 'A house and car'", return_code=True)
```
```text
==Explanation from the agent==
I will use the following tool: `image_generator` to generate an image.
==Code generated by the agent==
image = image_generator(prompt="A house and car")
```
<Tip warning={true}>
Agents are still brittle for many use cases, especially when it comes to
slightly more complex use cases like generating an image of multiple objects.
Both the agent itself and the underlying prompt will be further improved in the coming
months making sure that agents become more robust to a variety of user inputs.
</Tip>
### Customizing the whole prompt
To give the user maximum flexibility, the whole prompt template as explained in [above](#structure-of-the-prompt)
can be overwritten by the user. In this case make sure that your custom prompt includes an introduction section,
a tool section, an example section, and an unfinished example section. If you want to overwrite the `run` prompt template,
you can do as follows:
```py
template = """ [...] """
agent = HfAgent(your_endpoint, run_prompt_template=template)
```
<Tip warning={true}>
Please make sure to have the `<<all_tools>>` string and the `<<prompt>>` defined somewhere in the `template` so that the agent can be aware
of the tools, it has available to it as well as correctly insert the user's prompt.
</Tip>
Similarly, one can overwrite the `chat` prompt template. Note that the `chat` mode always uses the following format for the exchanges:
```text
Human: <<task>>
Assistant:
```
Therefore it is important that the examples of the custom `chat` prompt template also make use of this format.
You can overwrite the `chat` template at instantiation as follows.
```
template = """ [...] """
agent = HfAgent(url_endpoint=your_endpoint, chat_prompt_template=template)
```
<Tip warning={true}>
Please make sure to have the `<<all_tools>>` string defined somewhere in the `template` so that the agent can be aware
of the tools, it has available to it.
</Tip>
In both cases, you can pass a repo ID instead of the prompt template if you would like to use a template hosted by someone in the community. The default prompts live in [this repo](https://huggingface.co/datasets/huggingface-tools/default-prompts) as an example.
To upload your custom prompt on a repo on the Hub and share it with the community just make sure:
- to use a dataset repository
- to put the prompt template for the `run` command in a file named `run_prompt_template.txt`
- to put the prompt template for the `chat` command in a file named `chat_prompt_template.txt`
## Using custom tools
In this section, we'll be leveraging two existing custom tools that are specific to image generation:
- We replace [huggingface-tools/image-transformation](https://huggingface.co/spaces/huggingface-tools/image-transformation),
with [diffusers/controlnet-canny-tool](https://huggingface.co/spaces/diffusers/controlnet-canny-tool)
to allow for more image modifications.
- We add a new tool for image upscaling to the default toolbox:
[diffusers/latent-upscaler-tool](https://huggingface.co/spaces/diffusers/latent-upscaler-tool) replace the existing image-transformation tool.
We'll start by loading the custom tools with the convenient [`load_tool`] function:
```py
from transformers import load_tool
controlnet_transformer = load_tool("diffusers/controlnet-canny-tool")
upscaler = load_tool("diffusers/latent-upscaler-tool")
```
Upon adding custom tools to an agent, the tools' descriptions and names are automatically
included in the agents' prompts. Thus, it is imperative that custom tools have
a well-written description and name in order for the agent to understand how to use them.
Let's take a look at the description and name of `controlnet_transformer`:
```py
print(f"Description: '{controlnet_transformer.description}'")
print(f"Name: '{controlnet_transformer.name}'")
```
gives
```text
Description: 'This is a tool that transforms an image with ControlNet according to a prompt.
It takes two inputs: `image`, which should be the image to transform, and `prompt`, which should be the prompt to use to change it. It returns the modified image.'
Name: 'image_transformer'
```
The name and description are accurate and fit the style of the [curated set of tools](./transformers_agents#a-curated-set-of-tools).
Next, let's instantiate an agent with `controlnet_transformer` and `upscaler`:
```py
tools = [controlnet_transformer, upscaler]
agent = HfAgent("https://api-inference.huggingface.co/models/bigcode/starcoder", additional_tools=tools)
```
This command should give you the following info:
```text
image_transformer has been replaced by <transformers_modules.diffusers.controlnet-canny-tool.bd76182c7777eba9612fc03c0
8718a60c0aa6312.image_transformation.ControlNetTransformationTool object at 0x7f1d3bfa3a00> as provided in `additional_tools`
```
The set of curated tools already has an `image_transformer` tool which is hereby replaced with our custom tool.
<Tip>
Overwriting existing tools can be beneficial if we want to use a custom tool exactly for the same task as an existing tool
because the agent is well-versed in using the specific task. Beware that the custom tool should follow the exact same API
as the overwritten tool in this case, or you should adapt the prompt template to make sure all examples using that
tool are updated.
</Tip>
The upscaler tool was given the name `image_upscaler` which is not yet present in the default toolbox and is therefore simply added to the list of tools.
You can always have a look at the toolbox that is currently available to the agent via the `agent.toolbox` attribute:
```py
print("\n".join([f"- {a}" for a in agent.toolbox.keys()]))
```
```text
- document_qa
- image_captioner
- image_qa
- image_segmenter
- transcriber
- summarizer
- text_classifier
- text_qa
- text_reader
- translator
- image_transformer
- text_downloader
- image_generator
- video_generator
- image_upscaler
```
Note how `image_upscaler` is now part of the agents' toolbox.
Let's now try out the new tools! We will re-use the image we generated in [Transformers Agents Quickstart](./transformers_agents#single-execution-run).
```py
from diffusers.utils import load_image
image = load_image(
"https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/rivers_and_lakes.png"
)
```
<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/rivers_and_lakes.png" width=200>
Let's transform the image into a beautiful winter landscape:
```py
image = agent.run("Transform the image: 'A frozen lake and snowy forest'", image=image)
```
```text
==Explanation from the agent==
I will use the following tool: `image_transformer` to transform the image.
==Code generated by the agent==
image = image_transformer(image, prompt="A frozen lake and snowy forest")
```
<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/rivers_and_lakes_winter.png" width=200>
The new image processing tool is based on ControlNet which can make very strong modifications to the image.
By default the image processing tool returns an image of size 512x512 pixels. Let's see if we can upscale it.
```py
image = agent.run("Upscale the image", image)
```
```text
==Explanation from the agent==
I will use the following tool: `image_upscaler` to upscale the image.
==Code generated by the agent==
upscaled_image = image_upscaler(image)
```
<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/rivers_and_lakes_winter_upscale.png" width=400>
The agent automatically mapped our prompt "Upscale the image" to the just added upscaler tool purely based on the description and name of the upscaler tool
and was able to correctly run it.
Next, let's have a look at how you can create a new custom tool.
### Adding new tools
In this section, we show how to create a new tool that can be added to the agent.
#### Creating a new tool
We'll first start by creating a tool. We'll add the not-so-useful yet fun task of fetching the model on the Hugging Face
Hub with the most downloads for a given task.
We can do that with the following code:
```python
from huggingface_hub import list_models
task = "text-classification"
model = next(iter(list_models(filter=task, sort="downloads", direction=-1)))
print(model.id)
```
For the task `text-classification`, this returns `'facebook/bart-large-mnli'`, for `translation` it returns `'t5-base`.
How do we convert this to a tool that the agent can leverage? All tools depend on the superclass `Tool` that holds the
main attributes necessary. We'll create a class that inherits from it:
```python
from transformers import Tool
class HFModelDownloadsTool(Tool):
pass
```
This class has a few needs:
- An attribute `name`, which corresponds to the name of the tool itself. To be in tune with other tools which have a
performative name, we'll name it `model_download_counter`.
- An attribute `description`, which will be used to populate the prompt of the agent.
- `inputs` and `outputs` attributes. Defining this will help the python interpreter make educated choices about types,
and will allow for a gradio-demo to be spawned when we push our tool to the Hub. They're both a list of expected
values, which can be `text`, `image`, or `audio`.
- A `__call__` method which contains the inference code. This is the code we've played with above!
Here's what our class looks like now:
```python
from transformers import Tool
from huggingface_hub import list_models
class HFModelDownloadsTool(Tool):
name = "model_download_counter"
description = (
"This is a tool that returns the most downloaded model of a given task on the Hugging Face Hub. "
"It takes the name of the category (such as text-classification, depth-estimation, etc), and "
"returns the name of the checkpoint."
)
inputs = ["text"]
outputs = ["text"]
def __call__(self, task: str):
model = next(iter(list_models(filter=task, sort="downloads", direction=-1)))
return model.id
```
We now have our tool handy. Save it in a file and import it from your main script. Let's name this file
`model_downloads.py`, so the resulting import code looks like this:
```python
from model_downloads import HFModelDownloadsTool
tool = HFModelDownloadsTool()
```
In order to let others benefit from it and for simpler initialization, we recommend pushing it to the Hub under your
namespace. To do so, just call `push_to_hub` on the `tool` variable:
```python
tool.push_to_hub("hf-model-downloads")
```
You now have your code on the Hub! Let's take a look at the final step, which is to have the agent use it.
#### Having the agent use the tool
We now have our tool that lives on the Hub which can be instantiated as such (change the user name for your tool):
```python
from transformers import load_tool
tool = load_tool("lysandre/hf-model-downloads")
```
In order to use it in the agent, simply pass it in the `additional_tools` parameter of the agent initialization method:
```python
from transformers import HfAgent
agent = HfAgent("https://api-inference.huggingface.co/models/bigcode/starcoder", additional_tools=[tool])
agent.run(
"Can you read out loud the name of the model that has the most downloads in the 'text-to-video' task on the Hugging Face Hub?"
)
```
which outputs the following:
```text
==Code generated by the agent==
model = model_download_counter(task="text-to-video")
print(f"The model with the most downloads is {model}.")
audio_model = text_reader(model)
==Result==
The model with the most downloads is damo-vilab/text-to-video-ms-1.7b.
```
and generates the following audio.
| **Audio** |
|------------------------------------------------------------------------------------------------------------------------------------------------------|
| <audio controls><source src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/damo.wav" type="audio/wav"/> |
<Tip>
Depending on the LLM, some are quite brittle and require very exact prompts in order to work well. Having a well-defined
name and description of the tool is paramount to having it be leveraged by the agent.
</Tip>
### Replacing existing tools
Replacing existing tools can be done simply by assigning a new item to the agent's toolbox. Here's how one would do so:
```python
from transformers import HfAgent, load_tool
agent = HfAgent("https://api-inference.huggingface.co/models/bigcode/starcoder")
agent.toolbox["image-transformation"] = load_tool("diffusers/controlnet-canny-tool")
```
<Tip>
Beware when replacing tools with others! This will also adjust the agent's prompt. This can be good if you have a better
prompt suited for the task, but it can also result in your tool being selected way more than others or for other
tools to be selected instead of the one you have defined.
</Tip>
## Leveraging gradio-tools
[gradio-tools](https://github.com/freddyaboulton/gradio-tools) is a powerful library that allows using Hugging
Face Spaces as tools. It supports many existing Spaces as well as custom Spaces to be designed with it.
We offer support for `gradio_tools` by using the `Tool.from_gradio` method. For example, we want to take
advantage of the `StableDiffusionPromptGeneratorTool` tool offered in the `gradio-tools` toolkit so as to
improve our prompts and generate better images.
We first import the tool from `gradio_tools` and instantiate it:
```python
from gradio_tools import StableDiffusionPromptGeneratorTool
gradio_tool = StableDiffusionPromptGeneratorTool()
```
We pass that instance to the `Tool.from_gradio` method:
```python
from transformers import Tool
tool = Tool.from_gradio(gradio_tool)
```
Now we can manage it exactly as we would a usual custom tool. We leverage it to improve our prompt
` a rabbit wearing a space suit`:
```python
from transformers import HfAgent
agent = HfAgent("https://api-inference.huggingface.co/models/bigcode/starcoder", additional_tools=[tool])
agent.run("Generate an image of the `prompt` after improving it.", prompt="A rabbit wearing a space suit")
```
The model adequately leverages the tool:
```text
==Explanation from the agent==
I will use the following tools: `StableDiffusionPromptGenerator` to improve the prompt, then `image_generator` to generate an image according to the improved prompt.
==Code generated by the agent==
improved_prompt = StableDiffusionPromptGenerator(prompt)
print(f"The improved prompt is {improved_prompt}.")
image = image_generator(improved_prompt)
```
Before finally generating the image:
<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/rabbit.png">
<Tip warning={true}>
gradio-tools requires *textual* inputs and outputs, even when working with different modalities. This implementation
works with image and audio objects. The two are currently incompatible, but will rapidly become compatible as we
work to improve the support.
</Tip>
## Future compatibility with Langchain
We love Langchain and think it has a very compelling suite of tools. In order to handle these tools,
Langchain requires *textual* inputs and outputs, even when working with different modalities.
This is often the serialized version (i.e., saved to disk) of the objects.
This difference means that multi-modality isn't handled between transformers-agents and langchain.
We aim for this limitation to be resolved in future versions, and welcome any help from avid langchain
users to help us achieve this compatibility.
We would love to have better support. If you would like to help, please
[open an issue](https://github.com/huggingface/transformers/issues/new) and share what you have in mind.
| huggingface/transformers/blob/main/docs/source/en/custom_tools.md |
!--Copyright 2020 The HuggingFace Team. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
the License. You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
specific language governing permissions and limitations under the License.
⚠️ Note that this file is in Markdown but contain specific syntax for our doc-builder (similar to MDX) that may not be
rendered properly in your Markdown viewer.
-->
# Benchmarks
<Tip warning={true}>
Hugging Face's Benchmarking tools are deprecated and it is advised to use external Benchmarking libraries to measure the speed
and memory complexity of Transformer models.
</Tip>
[[open-in-colab]]
Let's take a look at how 🤗 Transformers models can be benchmarked, best practices, and already available benchmarks.
A notebook explaining in more detail how to benchmark 🤗 Transformers models can be found [here](https://github.com/huggingface/notebooks/tree/main/examples/benchmark.ipynb).
## How to benchmark 🤗 Transformers models
The classes [`PyTorchBenchmark`] and [`TensorFlowBenchmark`] allow to flexibly benchmark 🤗 Transformers models. The benchmark classes allow us to measure the _peak memory usage_ and _required time_ for both _inference_ and _training_.
<Tip>
Hereby, _inference_ is defined by a single forward pass, and _training_ is defined by a single forward pass and
backward pass.
</Tip>
The benchmark classes [`PyTorchBenchmark`] and [`TensorFlowBenchmark`] expect an object of type [`PyTorchBenchmarkArguments`] and
[`TensorFlowBenchmarkArguments`], respectively, for instantiation. [`PyTorchBenchmarkArguments`] and [`TensorFlowBenchmarkArguments`] are data classes and contain all relevant configurations for their corresponding benchmark class. In the following example, it is shown how a BERT model of type _bert-base-cased_ can be benchmarked.
<frameworkcontent>
<pt>
```py
>>> from transformers import PyTorchBenchmark, PyTorchBenchmarkArguments
>>> args = PyTorchBenchmarkArguments(models=["bert-base-uncased"], batch_sizes=[8], sequence_lengths=[8, 32, 128, 512])
>>> benchmark = PyTorchBenchmark(args)
```
</pt>
<tf>
```py
>>> from transformers import TensorFlowBenchmark, TensorFlowBenchmarkArguments
>>> args = TensorFlowBenchmarkArguments(
... models=["bert-base-uncased"], batch_sizes=[8], sequence_lengths=[8, 32, 128, 512]
... )
>>> benchmark = TensorFlowBenchmark(args)
```
</tf>
</frameworkcontent>
Here, three arguments are given to the benchmark argument data classes, namely `models`, `batch_sizes`, and
`sequence_lengths`. The argument `models` is required and expects a `list` of model identifiers from the
[model hub](https://huggingface.co/models) The `list` arguments `batch_sizes` and `sequence_lengths` define
the size of the `input_ids` on which the model is benchmarked. There are many more parameters that can be configured
via the benchmark argument data classes. For more detail on these one can either directly consult the files
`src/transformers/benchmark/benchmark_args_utils.py`, `src/transformers/benchmark/benchmark_args.py` (for PyTorch)
and `src/transformers/benchmark/benchmark_args_tf.py` (for Tensorflow). Alternatively, running the following shell
commands from root will print out a descriptive list of all configurable parameters for PyTorch and Tensorflow
respectively.
<frameworkcontent>
<pt>
```bash
python examples/pytorch/benchmarking/run_benchmark.py --help
```
An instantiated benchmark object can then simply be run by calling `benchmark.run()`.
```py
>>> results = benchmark.run()
>>> print(results)
==================== INFERENCE - SPEED - RESULT ====================
--------------------------------------------------------------------------------
Model Name Batch Size Seq Length Time in s
--------------------------------------------------------------------------------
bert-base-uncased 8 8 0.006
bert-base-uncased 8 32 0.006
bert-base-uncased 8 128 0.018
bert-base-uncased 8 512 0.088
--------------------------------------------------------------------------------
==================== INFERENCE - MEMORY - RESULT ====================
--------------------------------------------------------------------------------
Model Name Batch Size Seq Length Memory in MB
--------------------------------------------------------------------------------
bert-base-uncased 8 8 1227
bert-base-uncased 8 32 1281
bert-base-uncased 8 128 1307
bert-base-uncased 8 512 1539
--------------------------------------------------------------------------------
==================== ENVIRONMENT INFORMATION ====================
- transformers_version: 2.11.0
- framework: PyTorch
- use_torchscript: False
- framework_version: 1.4.0
- python_version: 3.6.10
- system: Linux
- cpu: x86_64
- architecture: 64bit
- date: 2020-06-29
- time: 08:58:43.371351
- fp16: False
- use_multiprocessing: True
- only_pretrain_model: False
- cpu_ram_mb: 32088
- use_gpu: True
- num_gpus: 1
- gpu: TITAN RTX
- gpu_ram_mb: 24217
- gpu_power_watts: 280.0
- gpu_performance_state: 2
- use_tpu: False
```
</pt>
<tf>
```bash
python examples/tensorflow/benchmarking/run_benchmark_tf.py --help
```
An instantiated benchmark object can then simply be run by calling `benchmark.run()`.
```py
>>> results = benchmark.run()
>>> print(results)
>>> results = benchmark.run()
>>> print(results)
==================== INFERENCE - SPEED - RESULT ====================
--------------------------------------------------------------------------------
Model Name Batch Size Seq Length Time in s
--------------------------------------------------------------------------------
bert-base-uncased 8 8 0.005
bert-base-uncased 8 32 0.008
bert-base-uncased 8 128 0.022
bert-base-uncased 8 512 0.105
--------------------------------------------------------------------------------
==================== INFERENCE - MEMORY - RESULT ====================
--------------------------------------------------------------------------------
Model Name Batch Size Seq Length Memory in MB
--------------------------------------------------------------------------------
bert-base-uncased 8 8 1330
bert-base-uncased 8 32 1330
bert-base-uncased 8 128 1330
bert-base-uncased 8 512 1770
--------------------------------------------------------------------------------
==================== ENVIRONMENT INFORMATION ====================
- transformers_version: 2.11.0
- framework: Tensorflow
- use_xla: False
- framework_version: 2.2.0
- python_version: 3.6.10
- system: Linux
- cpu: x86_64
- architecture: 64bit
- date: 2020-06-29
- time: 09:26:35.617317
- fp16: False
- use_multiprocessing: True
- only_pretrain_model: False
- cpu_ram_mb: 32088
- use_gpu: True
- num_gpus: 1
- gpu: TITAN RTX
- gpu_ram_mb: 24217
- gpu_power_watts: 280.0
- gpu_performance_state: 2
- use_tpu: False
```
</tf>
</frameworkcontent>
By default, the _time_ and the _required memory_ for _inference_ are benchmarked. In the example output above the first
two sections show the result corresponding to _inference time_ and _inference memory_. In addition, all relevant
information about the computing environment, _e.g._ the GPU type, the system, the library versions, etc... are printed
out in the third section under _ENVIRONMENT INFORMATION_. This information can optionally be saved in a _.csv_ file
when adding the argument `save_to_csv=True` to [`PyTorchBenchmarkArguments`] and
[`TensorFlowBenchmarkArguments`] respectively. In this case, every section is saved in a separate
_.csv_ file. The path to each _.csv_ file can optionally be defined via the argument data classes.
Instead of benchmarking pre-trained models via their model identifier, _e.g._ `bert-base-uncased`, the user can
alternatively benchmark an arbitrary configuration of any available model class. In this case, a `list` of
configurations must be inserted with the benchmark args as follows.
<frameworkcontent>
<pt>
```py
>>> from transformers import PyTorchBenchmark, PyTorchBenchmarkArguments, BertConfig
>>> args = PyTorchBenchmarkArguments(
... models=["bert-base", "bert-384-hid", "bert-6-lay"], batch_sizes=[8], sequence_lengths=[8, 32, 128, 512]
... )
>>> config_base = BertConfig()
>>> config_384_hid = BertConfig(hidden_size=384)
>>> config_6_lay = BertConfig(num_hidden_layers=6)
>>> benchmark = PyTorchBenchmark(args, configs=[config_base, config_384_hid, config_6_lay])
>>> benchmark.run()
==================== INFERENCE - SPEED - RESULT ====================
--------------------------------------------------------------------------------
Model Name Batch Size Seq Length Time in s
--------------------------------------------------------------------------------
bert-base 8 128 0.006
bert-base 8 512 0.006
bert-base 8 128 0.018
bert-base 8 512 0.088
bert-384-hid 8 8 0.006
bert-384-hid 8 32 0.006
bert-384-hid 8 128 0.011
bert-384-hid 8 512 0.054
bert-6-lay 8 8 0.003
bert-6-lay 8 32 0.004
bert-6-lay 8 128 0.009
bert-6-lay 8 512 0.044
--------------------------------------------------------------------------------
==================== INFERENCE - MEMORY - RESULT ====================
--------------------------------------------------------------------------------
Model Name Batch Size Seq Length Memory in MB
--------------------------------------------------------------------------------
bert-base 8 8 1277
bert-base 8 32 1281
bert-base 8 128 1307
bert-base 8 512 1539
bert-384-hid 8 8 1005
bert-384-hid 8 32 1027
bert-384-hid 8 128 1035
bert-384-hid 8 512 1255
bert-6-lay 8 8 1097
bert-6-lay 8 32 1101
bert-6-lay 8 128 1127
bert-6-lay 8 512 1359
--------------------------------------------------------------------------------
==================== ENVIRONMENT INFORMATION ====================
- transformers_version: 2.11.0
- framework: PyTorch
- use_torchscript: False
- framework_version: 1.4.0
- python_version: 3.6.10
- system: Linux
- cpu: x86_64
- architecture: 64bit
- date: 2020-06-29
- time: 09:35:25.143267
- fp16: False
- use_multiprocessing: True
- only_pretrain_model: False
- cpu_ram_mb: 32088
- use_gpu: True
- num_gpus: 1
- gpu: TITAN RTX
- gpu_ram_mb: 24217
- gpu_power_watts: 280.0
- gpu_performance_state: 2
- use_tpu: False
```
</pt>
<tf>
```py
>>> from transformers import TensorFlowBenchmark, TensorFlowBenchmarkArguments, BertConfig
>>> args = TensorFlowBenchmarkArguments(
... models=["bert-base", "bert-384-hid", "bert-6-lay"], batch_sizes=[8], sequence_lengths=[8, 32, 128, 512]
... )
>>> config_base = BertConfig()
>>> config_384_hid = BertConfig(hidden_size=384)
>>> config_6_lay = BertConfig(num_hidden_layers=6)
>>> benchmark = TensorFlowBenchmark(args, configs=[config_base, config_384_hid, config_6_lay])
>>> benchmark.run()
==================== INFERENCE - SPEED - RESULT ====================
--------------------------------------------------------------------------------
Model Name Batch Size Seq Length Time in s
--------------------------------------------------------------------------------
bert-base 8 8 0.005
bert-base 8 32 0.008
bert-base 8 128 0.022
bert-base 8 512 0.106
bert-384-hid 8 8 0.005
bert-384-hid 8 32 0.007
bert-384-hid 8 128 0.018
bert-384-hid 8 512 0.064
bert-6-lay 8 8 0.002
bert-6-lay 8 32 0.003
bert-6-lay 8 128 0.0011
bert-6-lay 8 512 0.074
--------------------------------------------------------------------------------
==================== INFERENCE - MEMORY - RESULT ====================
--------------------------------------------------------------------------------
Model Name Batch Size Seq Length Memory in MB
--------------------------------------------------------------------------------
bert-base 8 8 1330
bert-base 8 32 1330
bert-base 8 128 1330
bert-base 8 512 1770
bert-384-hid 8 8 1330
bert-384-hid 8 32 1330
bert-384-hid 8 128 1330
bert-384-hid 8 512 1540
bert-6-lay 8 8 1330
bert-6-lay 8 32 1330
bert-6-lay 8 128 1330
bert-6-lay 8 512 1540
--------------------------------------------------------------------------------
==================== ENVIRONMENT INFORMATION ====================
- transformers_version: 2.11.0
- framework: Tensorflow
- use_xla: False
- framework_version: 2.2.0
- python_version: 3.6.10
- system: Linux
- cpu: x86_64
- architecture: 64bit
- date: 2020-06-29
- time: 09:38:15.487125
- fp16: False
- use_multiprocessing: True
- only_pretrain_model: False
- cpu_ram_mb: 32088
- use_gpu: True
- num_gpus: 1
- gpu: TITAN RTX
- gpu_ram_mb: 24217
- gpu_power_watts: 280.0
- gpu_performance_state: 2
- use_tpu: False
```
</tf>
</frameworkcontent>
Again, _inference time_ and _required memory_ for _inference_ are measured, but this time for customized configurations
of the `BertModel` class. This feature can especially be helpful when deciding for which configuration the model
should be trained.
## Benchmark best practices
This section lists a couple of best practices one should be aware of when benchmarking a model.
- Currently, only single device benchmarking is supported. When benchmarking on GPU, it is recommended that the user
specifies on which device the code should be run by setting the `CUDA_VISIBLE_DEVICES` environment variable in the
shell, _e.g._ `export CUDA_VISIBLE_DEVICES=0` before running the code.
- The option `no_multi_processing` should only be set to `True` for testing and debugging. To ensure accurate
memory measurement it is recommended to run each memory benchmark in a separate process by making sure
`no_multi_processing` is set to `True`.
- One should always state the environment information when sharing the results of a model benchmark. Results can vary
heavily between different GPU devices, library versions, etc., so that benchmark results on their own are not very
useful for the community.
## Sharing your benchmark
Previously all available core models (10 at the time) have been benchmarked for _inference time_, across many different
settings: using PyTorch, with and without TorchScript, using TensorFlow, with and without XLA. All of those tests were
done across CPUs (except for TensorFlow XLA) and GPUs.
The approach is detailed in the [following blogpost](https://medium.com/huggingface/benchmarking-transformers-pytorch-and-tensorflow-e2917fb891c2) and the results are
available [here](https://docs.google.com/spreadsheets/d/1sryqufw2D0XlUH4sq3e9Wnxu5EAQkaohzrJbd5HdQ_w/edit?usp=sharing).
With the new _benchmark_ tools, it is easier than ever to share your benchmark results with the community
- [PyTorch Benchmarking Results](https://github.com/huggingface/transformers/tree/main/examples/pytorch/benchmarking/README.md).
- [TensorFlow Benchmarking Results](https://github.com/huggingface/transformers/tree/main/examples/tensorflow/benchmarking/README.md).
| huggingface/transformers/blob/main/docs/source/en/benchmarks.md |
!--Copyright 2023 IBM and HuggingFace Inc. team. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
the License. You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
specific language governing permissions and limitations under the License.
⚠️ Note that this file is in Markdown but contain specific syntax for our doc-builder (similar to MDX) that may not be
rendered properly in your Markdown viewer.
-->
# PatchTSMixer
## Overview
The PatchTSMixer model was proposed in [TSMixer: Lightweight MLP-Mixer Model for Multivariate Time Series Forecasting](https://arxiv.org/pdf/2306.09364.pdf) by Vijay Ekambaram, Arindam Jati, Nam Nguyen, Phanwadee Sinthong and Jayant Kalagnanam.
PatchTSMixer is a lightweight time-series modeling approach based on the MLP-Mixer architecture. In this HuggingFace implementation, we provide PatchTSMixer's capabilities to effortlessly facilitate lightweight mixing across patches, channels, and hidden features for effective multivariate time-series modeling. It also supports various attention mechanisms starting from simple gated attention to more complex self-attention blocks that can be customized accordingly. The model can be pretrained and subsequently used for various downstream tasks such as forecasting, classification and regression.
The abstract from the paper is the following:
*TSMixer is a lightweight neural architecture exclusively composed of multi-layer perceptron (MLP) modules designed for multivariate forecasting and representation learning on patched time series. Our model draws inspiration from the success of MLP-Mixer models in computer vision. We demonstrate the challenges involved in adapting Vision MLP-Mixer for time series and introduce empirically validated components to enhance accuracy. This includes a novel design paradigm of attaching online reconciliation heads to the MLP-Mixer backbone, for explicitly modeling the time-series properties such as hierarchy and channel-correlations. We also propose a Hybrid channel modeling approach to effectively handle noisy channel interactions and generalization across diverse datasets, a common challenge in existing patch channel-mixing methods. Additionally, a simple gated attention mechanism is introduced in the backbone to prioritize important features. By incorporating these lightweight components, we significantly enhance the learning capability of simple MLP structures, outperforming complex Transformer models with minimal computing usage. Moreover, TSMixer's modular design enables compatibility with both supervised and masked self-supervised learning methods, making it a promising building block for time-series Foundation Models. TSMixer outperforms state-of-the-art MLP and Transformer models in forecasting by a considerable margin of 8-60%. It also outperforms the latest strong benchmarks of Patch-Transformer models (by 1-2%) with a significant reduction in memory and runtime (2-3X).*
This model was contributed by [ajati](https://huggingface.co/ajati), [vijaye12](https://huggingface.co/vijaye12),
[gsinthong](https://huggingface.co/gsinthong), [namctin](https://huggingface.co/namctin),
[wmgifford](https://huggingface.co/wmgifford), [kashif](https://huggingface.co/kashif).
## Sample usage
```python
from transformers import PatchTSMixerConfig, PatchTSMixerForPrediction
from transformers import Trainer, TrainingArguments,
config = PatchTSMixerConfig(context_length = 512, prediction_length = 96)
model = PatchTSMixerForPrediction(config)
trainer = Trainer(model=model, args=training_args,
train_dataset=train_dataset,
eval_dataset=valid_dataset)
trainer.train()
results = trainer.evaluate(test_dataset)
```
## Usage tips
The model can also be used for time series classification and time series regression. See the respective [`PatchTSMixerForTimeSeriesClassification`] and [`PatchTSMixerForRegression`] classes.
## PatchTSMixerConfig
[[autodoc]] PatchTSMixerConfig
## PatchTSMixerModel
[[autodoc]] PatchTSMixerModel
- forward
## PatchTSMixerForPrediction
[[autodoc]] PatchTSMixerForPrediction
- forward
## PatchTSMixerForTimeSeriesClassification
[[autodoc]] PatchTSMixerForTimeSeriesClassification
- forward
## PatchTSMixerForPretraining
[[autodoc]] PatchTSMixerForPretraining
- forward
## PatchTSMixerForRegression
[[autodoc]] PatchTSMixerForRegression
- forward | huggingface/transformers/blob/main/docs/source/en/model_doc/patchtsmixer.md |
!--Copyright 2020 The HuggingFace Team. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
the License. You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
specific language governing permissions and limitations under the License.
⚠️ Note that this file is in Markdown but contain specific syntax for our doc-builder (similar to MDX) that may not be
rendered properly in your Markdown viewer.
-->
# OpenAI GPT2
<div class="flex flex-wrap space-x-1">
<a href="https://huggingface.co/models?filter=gpt2">
<img alt="Models" src="https://img.shields.io/badge/All_model_pages-gpt2-blueviolet">
</a>
<a href="https://huggingface.co/spaces/docs-demos/gpt2">
<img alt="Spaces" src="https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Spaces-blue">
</a>
</div>
## Overview
OpenAI GPT-2 model was proposed in [Language Models are Unsupervised Multitask Learners](https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf) by Alec
Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei and Ilya Sutskever from [OpenAI](https://huggingface.co/openai). It's a causal (unidirectional)
transformer pretrained using language modeling on a very large corpus of ~40 GB of text data.
The abstract from the paper is the following:
*GPT-2 is a large transformer-based language model with 1.5 billion parameters, trained on a dataset[1] of 8 million
web pages. GPT-2 is trained with a simple objective: predict the next word, given all of the previous words within some
text. The diversity of the dataset causes this simple goal to contain naturally occurring demonstrations of many tasks
across diverse domains. GPT-2 is a direct scale-up of GPT, with more than 10X the parameters and trained on more than
10X the amount of data.*
[Write With Transformer](https://transformer.huggingface.co/doc/gpt2-large) is a webapp created and hosted by
Hugging Face showcasing the generative capabilities of several models. GPT-2 is one of them and is available in five
different sizes: small, medium, large, xl and a distilled version of the small checkpoint: *distilgpt-2*.
This model was contributed by [thomwolf](https://huggingface.co/thomwolf). The original code can be found [here](https://openai.com/blog/better-language-models/).
## Usage tips
- GPT-2 is a model with absolute position embeddings so it's usually advised to pad the inputs on the right rather than
the left.
- GPT-2 was trained with a causal language modeling (CLM) objective and is therefore powerful at predicting the next
token in a sequence. Leveraging this feature allows GPT-2 to generate syntactically coherent text as it can be
observed in the *run_generation.py* example script.
- The model can take the *past_key_values* (for PyTorch) or *past* (for TF) as input, which is the previously computed
key/value attention pairs. Using this (*past_key_values* or *past*) value prevents the model from re-computing
pre-computed values in the context of text generation. For PyTorch, see *past_key_values* argument of the
[`GPT2Model.forward`] method, or for TF the *past* argument of the
[`TFGPT2Model.call`] method for more information on its usage.
- Enabling the *scale_attn_by_inverse_layer_idx* and *reorder_and_upcast_attn* flags will apply the training stability
improvements from [Mistral](https://github.com/stanford-crfm/mistral/) (for PyTorch only).
## Resources
A list of official Hugging Face and community (indicated by 🌎) resources to help you get started with GPT2. If you're interested in submitting a resource to be included here, please feel free to open a Pull Request and we'll review it! The resource should ideally demonstrate something new instead of duplicating an existing resource.
<PipelineTag pipeline="text-generation"/>
- A blog on how to [Finetune a non-English GPT-2 Model with Hugging Face](https://www.philschmid.de/fine-tune-a-non-english-gpt-2-model-with-huggingface).
- A blog on [How to generate text: using different decoding methods for language generation with Transformers](https://huggingface.co/blog/how-to-generate) with GPT-2.
- A blog on [Training CodeParrot 🦜 from Scratch](https://huggingface.co/blog/codeparrot), a large GPT-2 model.
- A blog on [Faster Text Generation with TensorFlow and XLA](https://huggingface.co/blog/tf-xla-generate) with GPT-2.
- A blog on [How to train a Language Model with Megatron-LM](https://huggingface.co/blog/megatron-training) with a GPT-2 model.
- A notebook on how to [finetune GPT2 to generate lyrics in the style of your favorite artist](https://colab.research.google.com/github/AlekseyKorshuk/huggingartists/blob/master/huggingartists-demo.ipynb). 🌎
- A notebook on how to [finetune GPT2 to generate tweets in the style of your favorite Twitter user](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb). 🌎
- [Causal language modeling](https://huggingface.co/course/en/chapter7/6?fw=pt#training-a-causal-language-model-from-scratch) chapter of the 🤗 Hugging Face Course.
- [`GPT2LMHeadModel`] is supported by this [causal language modeling example script](https://github.com/huggingface/transformers/tree/main/examples/pytorch/language-modeling#gpt-2gpt-and-causal-language-modeling), [text generation example script](https://github.com/huggingface/transformers/tree/main/examples/pytorch/text-generation), and [notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/language_modeling.ipynb).
- [`TFGPT2LMHeadModel`] is supported by this [causal language modeling example script](https://github.com/huggingface/transformers/tree/main/examples/tensorflow/language-modeling#run_clmpy) and [notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/language_modeling-tf.ipynb).
- [`FlaxGPT2LMHeadModel`] is supported by this [causal language modeling example script](https://github.com/huggingface/transformers/tree/main/examples/flax/language-modeling#causal-language-modeling) and [notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/causal_language_modeling_flax.ipynb).
- [Text classification task guide](../tasks/sequence_classification)
- [Token classification task guide](../tasks/token_classification)
- [Causal language modeling task guide](../tasks/language_modeling)
## GPT2Config
[[autodoc]] GPT2Config
## GPT2Tokenizer
[[autodoc]] GPT2Tokenizer
- save_vocabulary
## GPT2TokenizerFast
[[autodoc]] GPT2TokenizerFast
## GPT2 specific outputs
[[autodoc]] models.gpt2.modeling_gpt2.GPT2DoubleHeadsModelOutput
[[autodoc]] models.gpt2.modeling_tf_gpt2.TFGPT2DoubleHeadsModelOutput
<frameworkcontent>
<pt>
## GPT2Model
[[autodoc]] GPT2Model
- forward
## GPT2LMHeadModel
[[autodoc]] GPT2LMHeadModel
- forward
## GPT2DoubleHeadsModel
[[autodoc]] GPT2DoubleHeadsModel
- forward
## GPT2ForQuestionAnswering
[[autodoc]] GPT2ForQuestionAnswering
- forward
## GPT2ForSequenceClassification
[[autodoc]] GPT2ForSequenceClassification
- forward
## GPT2ForTokenClassification
[[autodoc]] GPT2ForTokenClassification
- forward
</pt>
<tf>
## TFGPT2Model
[[autodoc]] TFGPT2Model
- call
## TFGPT2LMHeadModel
[[autodoc]] TFGPT2LMHeadModel
- call
## TFGPT2DoubleHeadsModel
[[autodoc]] TFGPT2DoubleHeadsModel
- call
## TFGPT2ForSequenceClassification
[[autodoc]] TFGPT2ForSequenceClassification
- call
## TFSequenceClassifierOutputWithPast
[[autodoc]] modeling_tf_outputs.TFSequenceClassifierOutputWithPast
## TFGPT2Tokenizer
[[autodoc]] TFGPT2Tokenizer
</tf>
<jax>
## FlaxGPT2Model
[[autodoc]] FlaxGPT2Model
- __call__
## FlaxGPT2LMHeadModel
[[autodoc]] FlaxGPT2LMHeadModel
- __call__
</jax>
</frameworkcontent>
| huggingface/transformers/blob/main/docs/source/en/model_doc/gpt2.md |
!--Copyright 2020 The HuggingFace Team. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
the License. You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
specific language governing permissions and limitations under the License.
⚠️ Note that this file is in Markdown but contain specific syntax for our doc-builder (similar to MDX) that may not be
rendered properly in your Markdown viewer.
-->
# RetriBERT
<Tip warning={true}>
This model is in maintenance mode only, so we won't accept any new PRs changing its code.
If you run into any issues running this model, please reinstall the last version that supported this model: v4.30.0.
You can do so by running the following command: `pip install -U transformers==4.30.0`.
</Tip>
## Overview
The RetriBERT model was proposed in the blog post [Explain Anything Like I'm Five: A Model for Open Domain Long Form
Question Answering](https://yjernite.github.io/lfqa.html). RetriBERT is a small model that uses either a single or
pair of BERT encoders with lower-dimension projection for dense semantic indexing of text.
This model was contributed by [yjernite](https://huggingface.co/yjernite). Code to train and use the model can be
found [here](https://github.com/huggingface/transformers/tree/main/examples/research-projects/distillation).
## RetriBertConfig
[[autodoc]] RetriBertConfig
## RetriBertTokenizer
[[autodoc]] RetriBertTokenizer
## RetriBertTokenizerFast
[[autodoc]] RetriBertTokenizerFast
## RetriBertModel
[[autodoc]] RetriBertModel
- forward
| huggingface/transformers/blob/main/docs/source/en/model_doc/retribert.md |
!---
Copyright 2021 The HuggingFace Team. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
-->
# Multiple-choice training (e.g. SWAG)
This folder contains the `run_swag.py` script, showing an examples of *multiple-choice answering* with the
🤗 Transformers library. For straightforward use-cases you may be able to use these scripts without modification,
although we have also included comments in the code to indicate areas that you may need to adapt to your own projects.
### Multi-GPU and TPU usage
By default, the script uses a `MirroredStrategy` and will use multiple GPUs effectively if they are available. TPUs
can also be used by passing the name of the TPU resource with the `--tpu` argument.
### Memory usage and data loading
One thing to note is that all data is loaded into memory in this script. Most multiple-choice datasets are small
enough that this is not an issue, but if you have a very large dataset you will need to modify the script to handle
data streaming. This is particularly challenging for TPUs, given the stricter requirements and the sheer volume of data
required to keep them fed. A full explanation of all the possible pitfalls is a bit beyond this example script and
README, but for more information you can see the 'Input Datasets' section of
[this document](https://www.tensorflow.org/guide/tpu).
### Example command
```bash
python run_swag.py \
--model_name_or_path distilbert-base-cased \
--output_dir output \
--do_eval \
--do_train
```
| huggingface/transformers/blob/main/examples/tensorflow/multiple-choice/README.md |
!---
Copyright 2022 The HuggingFace Team. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
⚠️ Note that this file is in Markdown but contain specific syntax for our doc-builder (similar to MDX) that may not be
rendered properly in your Markdown viewer.
-->
# Installation
Install 🤗 Transformers for whichever deep learning library you're working with, setup your cache, and optionally configure 🤗 Transformers to run offline.
🤗 Transformers is tested on Python 3.6+, PyTorch 1.1.0+, TensorFlow 2.0+, and Flax. Follow the installation instructions below for the deep learning library you are using:
* [PyTorch](https://pytorch.org/get-started/locally/) installation instructions.
* [TensorFlow 2.0](https://www.tensorflow.org/install/pip) installation instructions.
* [Flax](https://flax.readthedocs.io/en/latest/) installation instructions.
## Install with pip
You should install 🤗 Transformers in a [virtual environment](https://docs.python.org/3/library/venv.html). If you're unfamiliar with Python virtual environments, take a look at this [guide](https://packaging.python.org/guides/installing-using-pip-and-virtual-environments/). A virtual environment makes it easier to manage different projects, and avoid compatibility issues between dependencies.
Start by creating a virtual environment in your project directory:
```bash
python -m venv .env
```
Activate the virtual environment. On Linux and MacOs:
```bash
source .env/bin/activate
```
Activate Virtual environment on Windows
```bash
.env/Scripts/activate
```
Now you're ready to install 🤗 Transformers with the following command:
```bash
pip install transformers
```
For CPU-support only, you can conveniently install 🤗 Transformers and a deep learning library in one line. For example, install 🤗 Transformers and PyTorch with:
```bash
pip install 'transformers[torch]'
```
🤗 Transformers and TensorFlow 2.0:
```bash
pip install 'transformers[tf-cpu]'
```
<Tip warning={true}>
M1 / ARM Users
You will need to install the following before installing TensorFLow 2.0
```
brew install cmake
brew install pkg-config
```
</Tip>
🤗 Transformers and Flax:
```bash
pip install 'transformers[flax]'
```
Finally, check if 🤗 Transformers has been properly installed by running the following command. It will download a pretrained model:
```bash
python -c "from transformers import pipeline; print(pipeline('sentiment-analysis')('we love you'))"
```
Then print out the label and score:
```bash
[{'label': 'POSITIVE', 'score': 0.9998704791069031}]
```
## Install from source
Install 🤗 Transformers from source with the following command:
```bash
pip install git+https://github.com/huggingface/transformers
```
This command installs the bleeding edge `main` version rather than the latest `stable` version. The `main` version is useful for staying up-to-date with the latest developments. For instance, if a bug has been fixed since the last official release but a new release hasn't been rolled out yet. However, this means the `main` version may not always be stable. We strive to keep the `main` version operational, and most issues are usually resolved within a few hours or a day. If you run into a problem, please open an [Issue](https://github.com/huggingface/transformers/issues) so we can fix it even sooner!
Check if 🤗 Transformers has been properly installed by running the following command:
```bash
python -c "from transformers import pipeline; print(pipeline('sentiment-analysis')('I love you'))"
```
## Editable install
You will need an editable install if you'd like to:
* Use the `main` version of the source code.
* Contribute to 🤗 Transformers and need to test changes in the code.
Clone the repository and install 🤗 Transformers with the following commands:
```bash
git clone https://github.com/huggingface/transformers.git
cd transformers
pip install -e .
```
These commands will link the folder you cloned the repository to and your Python library paths. Python will now look inside the folder you cloned to in addition to the normal library paths. For example, if your Python packages are typically installed in `~/anaconda3/envs/main/lib/python3.7/site-packages/`, Python will also search the folder you cloned to: `~/transformers/`.
<Tip warning={true}>
You must keep the `transformers` folder if you want to keep using the library.
</Tip>
Now you can easily update your clone to the latest version of 🤗 Transformers with the following command:
```bash
cd ~/transformers/
git pull
```
Your Python environment will find the `main` version of 🤗 Transformers on the next run.
## Install with conda
Install from the conda channel `huggingface`:
```bash
conda install -c huggingface transformers
```
## Cache setup
Pretrained models are downloaded and locally cached at: `~/.cache/huggingface/hub`. This is the default directory given by the shell environment variable `TRANSFORMERS_CACHE`. On Windows, the default directory is given by `C:\Users\username\.cache\huggingface\hub`. You can change the shell environment variables shown below - in order of priority - to specify a different cache directory:
1. Shell environment variable (default): `HUGGINGFACE_HUB_CACHE` or `TRANSFORMERS_CACHE`.
2. Shell environment variable: `HF_HOME`.
3. Shell environment variable: `XDG_CACHE_HOME` + `/huggingface`.
<Tip>
🤗 Transformers will use the shell environment variables `PYTORCH_TRANSFORMERS_CACHE` or `PYTORCH_PRETRAINED_BERT_CACHE` if you are coming from an earlier iteration of this library and have set those environment variables, unless you specify the shell environment variable `TRANSFORMERS_CACHE`.
</Tip>
## Offline mode
Run 🤗 Transformers in a firewalled or offline environment with locally cached files by setting the environment variable `TRANSFORMERS_OFFLINE=1`.
<Tip>
Add [🤗 Datasets](https://huggingface.co/docs/datasets/) to your offline training workflow with the environment variable `HF_DATASETS_OFFLINE=1`.
</Tip>
```bash
HF_DATASETS_OFFLINE=1 TRANSFORMERS_OFFLINE=1 \
python examples/pytorch/translation/run_translation.py --model_name_or_path t5-small --dataset_name wmt16 --dataset_config ro-en ...
```
This script should run without hanging or waiting to timeout because it won't attempt to download the model from the Hub.
You can also bypass loading a model from the Hub from each [`~PreTrainedModel.from_pretrained`] call with the [`local_files_only`] parameter. When set to `True`, only local files are loaded:
```py
from transformers import T5Model
model = T5Model.from_pretrained("./path/to/local/directory", local_files_only=True)
```
### Fetch models and tokenizers to use offline
Another option for using 🤗 Transformers offline is to download the files ahead of time, and then point to their local path when you need to use them offline. There are three ways to do this:
* Download a file through the user interface on the [Model Hub](https://huggingface.co/models) by clicking on the ↓ icon.
* Use the [`PreTrainedModel.from_pretrained`] and [`PreTrainedModel.save_pretrained`] workflow:
1. Download your files ahead of time with [`PreTrainedModel.from_pretrained`]:
```py
>>> from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
>>> tokenizer = AutoTokenizer.from_pretrained("bigscience/T0_3B")
>>> model = AutoModelForSeq2SeqLM.from_pretrained("bigscience/T0_3B")
```
2. Save your files to a specified directory with [`PreTrainedModel.save_pretrained`]:
```py
>>> tokenizer.save_pretrained("./your/path/bigscience_t0")
>>> model.save_pretrained("./your/path/bigscience_t0")
```
3. Now when you're offline, reload your files with [`PreTrainedModel.from_pretrained`] from the specified directory:
```py
>>> tokenizer = AutoTokenizer.from_pretrained("./your/path/bigscience_t0")
>>> model = AutoModel.from_pretrained("./your/path/bigscience_t0")
```
* Programmatically download files with the [huggingface_hub](https://github.com/huggingface/huggingface_hub/tree/main/src/huggingface_hub) library:
1. Install the `huggingface_hub` library in your virtual environment:
```bash
python -m pip install huggingface_hub
```
2. Use the [`hf_hub_download`](https://huggingface.co/docs/hub/adding-a-library#download-files-from-the-hub) function to download a file to a specific path. For example, the following command downloads the `config.json` file from the [T0](https://huggingface.co/bigscience/T0_3B) model to your desired path:
```py
>>> from huggingface_hub import hf_hub_download
>>> hf_hub_download(repo_id="bigscience/T0_3B", filename="config.json", cache_dir="./your/path/bigscience_t0")
```
Once your file is downloaded and locally cached, specify it's local path to load and use it:
```py
>>> from transformers import AutoConfig
>>> config = AutoConfig.from_pretrained("./your/path/bigscience_t0/config.json")
```
<Tip>
See the [How to download files from the Hub](https://huggingface.co/docs/hub/how-to-downstream) section for more details on downloading files stored on the Hub.
</Tip>
| huggingface/transformers/blob/main/docs/source/en/installation.md |
!--Copyright 2021 The HuggingFace Team. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
the License. You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
specific language governing permissions and limitations under the License.
⚠️ Note that this file is in Markdown but contain specific syntax for our doc-builder (similar to MDX) that may not be
rendered properly in your Markdown viewer.
-->
# GPT Neo
## Overview
The GPTNeo model was released in the [EleutherAI/gpt-neo](https://github.com/EleutherAI/gpt-neo) repository by Sid
Black, Stella Biderman, Leo Gao, Phil Wang and Connor Leahy. It is a GPT2 like causal language model trained on the
[Pile](https://pile.eleuther.ai/) dataset.
The architecture is similar to GPT2 except that GPT Neo uses local attention in every other layer with a window size of
256 tokens.
This model was contributed by [valhalla](https://huggingface.co/valhalla).
## Usage example
The `generate()` method can be used to generate text using GPT Neo model.
```python
>>> from transformers import GPTNeoForCausalLM, GPT2Tokenizer
>>> model = GPTNeoForCausalLM.from_pretrained("EleutherAI/gpt-neo-1.3B")
>>> tokenizer = GPT2Tokenizer.from_pretrained("EleutherAI/gpt-neo-1.3B")
>>> prompt = (
... "In a shocking finding, scientists discovered a herd of unicorns living in a remote, "
... "previously unexplored valley, in the Andes Mountains. Even more surprising to the "
... "researchers was the fact that the unicorns spoke perfect English."
... )
>>> input_ids = tokenizer(prompt, return_tensors="pt").input_ids
>>> gen_tokens = model.generate(
... input_ids,
... do_sample=True,
... temperature=0.9,
... max_length=100,
... )
>>> gen_text = tokenizer.batch_decode(gen_tokens)[0]
```
## Combining GPT-Neo and Flash Attention 2
First, make sure to install the latest version of Flash Attention 2 to include the sliding window attention feature, and make sure your hardware is compatible with Flash-Attention 2. More details are available [here](https://huggingface.co/docs/transformers/perf_infer_gpu_one#flashattention-2) concerning the installation.
Make sure as well to load your model in half-precision (e.g. `torch.float16`).
To load and run a model using Flash Attention 2, refer to the snippet below:
```python
>>> import torch
>>> from transformers import AutoModelForCausalLM, AutoTokenizer
>>> device = "cuda" # the device to load the model onto
>>> model = AutoModelForCausalLM.from_pretrained("EleutherAI/gpt-neo-2.7B", torch_dtype=torch.float16, attn_implementation="flash_attention_2")
>>> tokenizer = AutoTokenizer.from_pretrained("EleutherAI/gpt-neo-2.7B")
>>> prompt = "def hello_world():"
>>> model_inputs = tokenizer([prompt], return_tensors="pt").to(device)
>>> model.to(device)
>>> generated_ids = model.generate(**model_inputs, max_new_tokens=100, do_sample=True)
>>> tokenizer.batch_decode(generated_ids)[0]
"def hello_world():\n >>> run_script("hello.py")\n >>> exit(0)\n<|endoftext|>"
```
### Expected speedups
Below is an expected speedup diagram that compares pure inference time between the native implementation in transformers using `EleutherAI/gpt-neo-2.7B` checkpoint and the Flash Attention 2 version of the model.
Note that for GPT-Neo it is not possible to train / run on very long context as the max [position embeddings](https://huggingface.co/EleutherAI/gpt-neo-2.7B/blob/main/config.json#L58 ) is limited to 2048 - but this is applicable to all gpt-neo models and not specific to FA-2
<div style="text-align: center">
<img src="https://user-images.githubusercontent.com/49240599/272241893-b1c66b75-3a48-4265-bc47-688448568b3d.png">
</div>
## Resources
- [Text classification task guide](../tasks/sequence_classification)
- [Causal language modeling task guide](../tasks/language_modeling)
## GPTNeoConfig
[[autodoc]] GPTNeoConfig
<frameworkcontent>
<pt>
## GPTNeoModel
[[autodoc]] GPTNeoModel
- forward
## GPTNeoForCausalLM
[[autodoc]] GPTNeoForCausalLM
- forward
## GPTNeoForQuestionAnswering
[[autodoc]] GPTNeoForQuestionAnswering
- forward
## GPTNeoForSequenceClassification
[[autodoc]] GPTNeoForSequenceClassification
- forward
## GPTNeoForTokenClassification
[[autodoc]] GPTNeoForTokenClassification
- forward
</pt>
<jax>
## FlaxGPTNeoModel
[[autodoc]] FlaxGPTNeoModel
- __call__
## FlaxGPTNeoForCausalLM
[[autodoc]] FlaxGPTNeoForCausalLM
- __call__
</jax>
</frameworkcontent>
| huggingface/transformers/blob/main/docs/source/en/model_doc/gpt_neo.md |
DeeBERT: Early Exiting for *BERT
This is the code base for the paper [DeeBERT: Dynamic Early Exiting for Accelerating BERT Inference](https://www.aclweb.org/anthology/2020.acl-main.204/), modified from its [original code base](https://github.com/castorini/deebert).
The original code base also has information for downloading sample models that we have trained in advance.
## Usage
There are three scripts in the folder which can be run directly.
In each script, there are several things to modify before running:
* `PATH_TO_DATA`: path to the GLUE dataset.
* `--output_dir`: path for saving fine-tuned models. Default: `./saved_models`.
* `--plot_data_dir`: path for saving evaluation results. Default: `./results`. Results are printed to stdout and also saved to `npy` files in this directory to facilitate plotting figures and further analyses.
* `MODEL_TYPE`: bert or roberta
* `MODEL_SIZE`: base or large
* `DATASET`: SST-2, MRPC, RTE, QNLI, QQP, or MNLI
#### train_deebert.sh
This is for fine-tuning DeeBERT models.
#### eval_deebert.sh
This is for evaluating each exit layer for fine-tuned DeeBERT models.
#### entropy_eval.sh
This is for evaluating fine-tuned DeeBERT models, given a number of different early exit entropy thresholds.
## Citation
Please cite our paper if you find the resource useful:
```
@inproceedings{xin-etal-2020-deebert,
title = "{D}ee{BERT}: Dynamic Early Exiting for Accelerating {BERT} Inference",
author = "Xin, Ji and
Tang, Raphael and
Lee, Jaejun and
Yu, Yaoliang and
Lin, Jimmy",
booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
month = jul,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/2020.acl-main.204",
pages = "2246--2251",
}
```
| huggingface/transformers/blob/main/examples/research_projects/deebert/README.md |
!--Copyright 2022 The HuggingFace Team. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
the License. You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
specific language governing permissions and limitations under the License.
⚠️ Note that this file is in Markdown but contain specific syntax for our doc-builder (similar to MDX) that may not be
rendered properly in your Markdown viewer.
-->
# Object detection
[[open-in-colab]]
Object detection is the computer vision task of detecting instances (such as humans, buildings, or cars) in an image. Object detection models receive an image as input and output
coordinates of the bounding boxes and associated labels of the detected objects. An image can contain multiple objects,
each with its own bounding box and a label (e.g. it can have a car and a building), and each object can
be present in different parts of an image (e.g. the image can have several cars).
This task is commonly used in autonomous driving for detecting things like pedestrians, road signs, and traffic lights.
Other applications include counting objects in images, image search, and more.
In this guide, you will learn how to:
1. Finetune [DETR](https://huggingface.co/docs/transformers/model_doc/detr), a model that combines a convolutional
backbone with an encoder-decoder Transformer, on the [CPPE-5](https://huggingface.co/datasets/cppe-5)
dataset.
2. Use your finetuned model for inference.
<Tip>
The task illustrated in this tutorial is supported by the following model architectures:
<!--This tip is automatically generated by `make fix-copies`, do not fill manually!-->
[Conditional DETR](../model_doc/conditional_detr), [Deformable DETR](../model_doc/deformable_detr), [DETA](../model_doc/deta), [DETR](../model_doc/detr), [Table Transformer](../model_doc/table-transformer), [YOLOS](../model_doc/yolos)
<!--End of the generated tip-->
</Tip>
Before you begin, make sure you have all the necessary libraries installed:
```bash
pip install -q datasets transformers evaluate timm albumentations
```
You'll use 🤗 Datasets to load a dataset from the Hugging Face Hub, 🤗 Transformers to train your model,
and `albumentations` to augment the data. `timm` is currently required to load a convolutional backbone for the DETR model.
We encourage you to share your model with the community. Log in to your Hugging Face account to upload it to the Hub.
When prompted, enter your token to log in:
```py
>>> from huggingface_hub import notebook_login
>>> notebook_login()
```
## Load the CPPE-5 dataset
The [CPPE-5 dataset](https://huggingface.co/datasets/cppe-5) contains images with
annotations identifying medical personal protective equipment (PPE) in the context of the COVID-19 pandemic.
Start by loading the dataset:
```py
>>> from datasets import load_dataset
>>> cppe5 = load_dataset("cppe-5")
>>> cppe5
DatasetDict({
train: Dataset({
features: ['image_id', 'image', 'width', 'height', 'objects'],
num_rows: 1000
})
test: Dataset({
features: ['image_id', 'image', 'width', 'height', 'objects'],
num_rows: 29
})
})
```
You'll see that this dataset already comes with a training set containing 1000 images and a test set with 29 images.
To get familiar with the data, explore what the examples look like.
```py
>>> cppe5["train"][0]
{'image_id': 15,
'image': <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=943x663 at 0x7F9EC9E77C10>,
'width': 943,
'height': 663,
'objects': {'id': [114, 115, 116, 117],
'area': [3796, 1596, 152768, 81002],
'bbox': [[302.0, 109.0, 73.0, 52.0],
[810.0, 100.0, 57.0, 28.0],
[160.0, 31.0, 248.0, 616.0],
[741.0, 68.0, 202.0, 401.0]],
'category': [4, 4, 0, 0]}}
```
The examples in the dataset have the following fields:
- `image_id`: the example image id
- `image`: a `PIL.Image.Image` object containing the image
- `width`: width of the image
- `height`: height of the image
- `objects`: a dictionary containing bounding box metadata for the objects in the image:
- `id`: the annotation id
- `area`: the area of the bounding box
- `bbox`: the object's bounding box (in the [COCO format](https://albumentations.ai/docs/getting_started/bounding_boxes_augmentation/#coco) )
- `category`: the object's category, with possible values including `Coverall (0)`, `Face_Shield (1)`, `Gloves (2)`, `Goggles (3)` and `Mask (4)`
You may notice that the `bbox` field follows the COCO format, which is the format that the DETR model expects.
However, the grouping of the fields inside `objects` differs from the annotation format DETR requires. You will
need to apply some preprocessing transformations before using this data for training.
To get an even better understanding of the data, visualize an example in the dataset.
```py
>>> import numpy as np
>>> import os
>>> from PIL import Image, ImageDraw
>>> image = cppe5["train"][0]["image"]
>>> annotations = cppe5["train"][0]["objects"]
>>> draw = ImageDraw.Draw(image)
>>> categories = cppe5["train"].features["objects"].feature["category"].names
>>> id2label = {index: x for index, x in enumerate(categories, start=0)}
>>> label2id = {v: k for k, v in id2label.items()}
>>> for i in range(len(annotations["id"])):
... box = annotations["bbox"][i]
... class_idx = annotations["category"][i]
... x, y, w, h = tuple(box)
... draw.rectangle((x, y, x + w, y + h), outline="red", width=1)
... draw.text((x, y), id2label[class_idx], fill="white")
>>> image
```
<div class="flex justify-center">
<img src="https://i.imgur.com/TdaqPJO.png" alt="CPPE-5 Image Example"/>
</div>
To visualize the bounding boxes with associated labels, you can get the labels from the dataset's metadata, specifically
the `category` field.
You'll also want to create dictionaries that map a label id to a label class (`id2label`) and the other way around (`label2id`).
You can use them later when setting up the model. Including these maps will make your model reusable by others if you share
it on the Hugging Face Hub.
As a final step of getting familiar with the data, explore it for potential issues. One common problem with datasets for
object detection is bounding boxes that "stretch" beyond the edge of the image. Such "runaway" bounding boxes can raise
errors during training and should be addressed at this stage. There are a few examples with this issue in this dataset.
To keep things simple in this guide, we remove these images from the data.
```py
>>> remove_idx = [590, 821, 822, 875, 876, 878, 879]
>>> keep = [i for i in range(len(cppe5["train"])) if i not in remove_idx]
>>> cppe5["train"] = cppe5["train"].select(keep)
```
## Preprocess the data
To finetune a model, you must preprocess the data you plan to use to match precisely the approach used for the pre-trained model.
[`AutoImageProcessor`] takes care of processing image data to create `pixel_values`, `pixel_mask`, and
`labels` that a DETR model can train with. The image processor has some attributes that you won't have to worry about:
- `image_mean = [0.485, 0.456, 0.406 ]`
- `image_std = [0.229, 0.224, 0.225]`
These are the mean and standard deviation used to normalize images during the model pre-training. These values are crucial
to replicate when doing inference or finetuning a pre-trained image model.
Instantiate the image processor from the same checkpoint as the model you want to finetune.
```py
>>> from transformers import AutoImageProcessor
>>> checkpoint = "facebook/detr-resnet-50"
>>> image_processor = AutoImageProcessor.from_pretrained(checkpoint)
```
Before passing the images to the `image_processor`, apply two preprocessing transformations to the dataset:
- Augmenting images
- Reformatting annotations to meet DETR expectations
First, to make sure the model does not overfit on the training data, you can apply image augmentation with any data augmentation library. Here we use [Albumentations](https://albumentations.ai/docs/) ...
This library ensures that transformations affect the image and update the bounding boxes accordingly.
The 🤗 Datasets library documentation has a detailed [guide on how to augment images for object detection](https://huggingface.co/docs/datasets/object_detection),
and it uses the exact same dataset as an example. Apply the same approach here, resize each image to (480, 480),
flip it horizontally, and brighten it:
```py
>>> import albumentations
>>> import numpy as np
>>> import torch
>>> transform = albumentations.Compose(
... [
... albumentations.Resize(480, 480),
... albumentations.HorizontalFlip(p=1.0),
... albumentations.RandomBrightnessContrast(p=1.0),
... ],
... bbox_params=albumentations.BboxParams(format="coco", label_fields=["category"]),
... )
```
The `image_processor` expects the annotations to be in the following format: `{'image_id': int, 'annotations': List[Dict]}`,
where each dictionary is a COCO object annotation. Let's add a function to reformat annotations for a single example:
```py
>>> def formatted_anns(image_id, category, area, bbox):
... annotations = []
... for i in range(0, len(category)):
... new_ann = {
... "image_id": image_id,
... "category_id": category[i],
... "isCrowd": 0,
... "area": area[i],
... "bbox": list(bbox[i]),
... }
... annotations.append(new_ann)
... return annotations
```
Now you can combine the image and annotation transformations to use on a batch of examples:
```py
>>> # transforming a batch
>>> def transform_aug_ann(examples):
... image_ids = examples["image_id"]
... images, bboxes, area, categories = [], [], [], []
... for image, objects in zip(examples["image"], examples["objects"]):
... image = np.array(image.convert("RGB"))[:, :, ::-1]
... out = transform(image=image, bboxes=objects["bbox"], category=objects["category"])
... area.append(objects["area"])
... images.append(out["image"])
... bboxes.append(out["bboxes"])
... categories.append(out["category"])
... targets = [
... {"image_id": id_, "annotations": formatted_anns(id_, cat_, ar_, box_)}
... for id_, cat_, ar_, box_ in zip(image_ids, categories, area, bboxes)
... ]
... return image_processor(images=images, annotations=targets, return_tensors="pt")
```
Apply this preprocessing function to the entire dataset using 🤗 Datasets [`~datasets.Dataset.with_transform`] method. This method applies
transformations on the fly when you load an element of the dataset.
At this point, you can check what an example from the dataset looks like after the transformations. You should see a tensor
with `pixel_values`, a tensor with `pixel_mask`, and `labels`.
```py
>>> cppe5["train"] = cppe5["train"].with_transform(transform_aug_ann)
>>> cppe5["train"][15]
{'pixel_values': tensor([[[ 0.9132, 0.9132, 0.9132, ..., -1.9809, -1.9809, -1.9809],
[ 0.9132, 0.9132, 0.9132, ..., -1.9809, -1.9809, -1.9809],
[ 0.9132, 0.9132, 0.9132, ..., -1.9638, -1.9638, -1.9638],
...,
[-1.5699, -1.5699, -1.5699, ..., -1.9980, -1.9980, -1.9980],
[-1.5528, -1.5528, -1.5528, ..., -1.9980, -1.9809, -1.9809],
[-1.5528, -1.5528, -1.5528, ..., -1.9980, -1.9809, -1.9809]],
[[ 1.3081, 1.3081, 1.3081, ..., -1.8431, -1.8431, -1.8431],
[ 1.3081, 1.3081, 1.3081, ..., -1.8431, -1.8431, -1.8431],
[ 1.3081, 1.3081, 1.3081, ..., -1.8256, -1.8256, -1.8256],
...,
[-1.3179, -1.3179, -1.3179, ..., -1.8606, -1.8606, -1.8606],
[-1.3004, -1.3004, -1.3004, ..., -1.8606, -1.8431, -1.8431],
[-1.3004, -1.3004, -1.3004, ..., -1.8606, -1.8431, -1.8431]],
[[ 1.4200, 1.4200, 1.4200, ..., -1.6476, -1.6476, -1.6476],
[ 1.4200, 1.4200, 1.4200, ..., -1.6476, -1.6476, -1.6476],
[ 1.4200, 1.4200, 1.4200, ..., -1.6302, -1.6302, -1.6302],
...,
[-1.0201, -1.0201, -1.0201, ..., -1.5604, -1.5604, -1.5604],
[-1.0027, -1.0027, -1.0027, ..., -1.5604, -1.5430, -1.5430],
[-1.0027, -1.0027, -1.0027, ..., -1.5604, -1.5430, -1.5430]]]),
'pixel_mask': tensor([[1, 1, 1, ..., 1, 1, 1],
[1, 1, 1, ..., 1, 1, 1],
[1, 1, 1, ..., 1, 1, 1],
...,
[1, 1, 1, ..., 1, 1, 1],
[1, 1, 1, ..., 1, 1, 1],
[1, 1, 1, ..., 1, 1, 1]]),
'labels': {'size': tensor([800, 800]), 'image_id': tensor([756]), 'class_labels': tensor([4]), 'boxes': tensor([[0.7340, 0.6986, 0.3414, 0.5944]]), 'area': tensor([519544.4375]), 'iscrowd': tensor([0]), 'orig_size': tensor([480, 480])}}
```
You have successfully augmented the individual images and prepared their annotations. However, preprocessing isn't
complete yet. In the final step, create a custom `collate_fn` to batch images together.
Pad images (which are now `pixel_values`) to the largest image in a batch, and create a corresponding `pixel_mask`
to indicate which pixels are real (1) and which are padding (0).
```py
>>> def collate_fn(batch):
... pixel_values = [item["pixel_values"] for item in batch]
... encoding = image_processor.pad(pixel_values, return_tensors="pt")
... labels = [item["labels"] for item in batch]
... batch = {}
... batch["pixel_values"] = encoding["pixel_values"]
... batch["pixel_mask"] = encoding["pixel_mask"]
... batch["labels"] = labels
... return batch
```
## Training the DETR model
You have done most of the heavy lifting in the previous sections, so now you are ready to train your model!
The images in this dataset are still quite large, even after resizing. This means that finetuning this model will
require at least one GPU.
Training involves the following steps:
1. Load the model with [`AutoModelForObjectDetection`] using the same checkpoint as in the preprocessing.
2. Define your training hyperparameters in [`TrainingArguments`].
3. Pass the training arguments to [`Trainer`] along with the model, dataset, image processor, and data collator.
4. Call [`~Trainer.train`] to finetune your model.
When loading the model from the same checkpoint that you used for the preprocessing, remember to pass the `label2id`
and `id2label` maps that you created earlier from the dataset's metadata. Additionally, we specify `ignore_mismatched_sizes=True` to replace the existing classification head with a new one.
```py
>>> from transformers import AutoModelForObjectDetection
>>> model = AutoModelForObjectDetection.from_pretrained(
... checkpoint,
... id2label=id2label,
... label2id=label2id,
... ignore_mismatched_sizes=True,
... )
```
In the [`TrainingArguments`] use `output_dir` to specify where to save your model, then configure hyperparameters as you see fit.
It is important you do not remove unused columns because this will drop the image column. Without the image column, you
can't create `pixel_values`. For this reason, set `remove_unused_columns` to `False`.
If you wish to share your model by pushing to the Hub, set `push_to_hub` to `True` (you must be signed in to Hugging
Face to upload your model).
```py
>>> from transformers import TrainingArguments
>>> training_args = TrainingArguments(
... output_dir="detr-resnet-50_finetuned_cppe5",
... per_device_train_batch_size=8,
... num_train_epochs=10,
... fp16=True,
... save_steps=200,
... logging_steps=50,
... learning_rate=1e-5,
... weight_decay=1e-4,
... save_total_limit=2,
... remove_unused_columns=False,
... push_to_hub=True,
... )
```
Finally, bring everything together, and call [`~transformers.Trainer.train`]:
```py
>>> from transformers import Trainer
>>> trainer = Trainer(
... model=model,
... args=training_args,
... data_collator=collate_fn,
... train_dataset=cppe5["train"],
... tokenizer=image_processor,
... )
>>> trainer.train()
```
If you have set `push_to_hub` to `True` in the `training_args`, the training checkpoints are pushed to the
Hugging Face Hub. Upon training completion, push the final model to the Hub as well by calling the [`~transformers.Trainer.push_to_hub`] method.
```py
>>> trainer.push_to_hub()
```
## Evaluate
Object detection models are commonly evaluated with a set of <a href="https://cocodataset.org/#detection-eval">COCO-style metrics</a>.
You can use one of the existing metrics implementations, but here you'll use the one from `torchvision` to evaluate the final
model that you pushed to the Hub.
To use the `torchvision` evaluator, you'll need to prepare a ground truth COCO dataset. The API to build a COCO dataset
requires the data to be stored in a certain format, so you'll need to save images and annotations to disk first. Just like
when you prepared your data for training, the annotations from the `cppe5["test"]` need to be formatted. However, images
should stay as they are.
The evaluation step requires a bit of work, but it can be split in three major steps.
First, prepare the `cppe5["test"]` set: format the annotations and save the data to disk.
```py
>>> import json
>>> # format annotations the same as for training, no need for data augmentation
>>> def val_formatted_anns(image_id, objects):
... annotations = []
... for i in range(0, len(objects["id"])):
... new_ann = {
... "id": objects["id"][i],
... "category_id": objects["category"][i],
... "iscrowd": 0,
... "image_id": image_id,
... "area": objects["area"][i],
... "bbox": objects["bbox"][i],
... }
... annotations.append(new_ann)
... return annotations
>>> # Save images and annotations into the files torchvision.datasets.CocoDetection expects
>>> def save_cppe5_annotation_file_images(cppe5):
... output_json = {}
... path_output_cppe5 = f"{os.getcwd()}/cppe5/"
... if not os.path.exists(path_output_cppe5):
... os.makedirs(path_output_cppe5)
... path_anno = os.path.join(path_output_cppe5, "cppe5_ann.json")
... categories_json = [{"supercategory": "none", "id": id, "name": id2label[id]} for id in id2label]
... output_json["images"] = []
... output_json["annotations"] = []
... for example in cppe5:
... ann = val_formatted_anns(example["image_id"], example["objects"])
... output_json["images"].append(
... {
... "id": example["image_id"],
... "width": example["image"].width,
... "height": example["image"].height,
... "file_name": f"{example['image_id']}.png",
... }
... )
... output_json["annotations"].extend(ann)
... output_json["categories"] = categories_json
... with open(path_anno, "w") as file:
... json.dump(output_json, file, ensure_ascii=False, indent=4)
... for im, img_id in zip(cppe5["image"], cppe5["image_id"]):
... path_img = os.path.join(path_output_cppe5, f"{img_id}.png")
... im.save(path_img)
... return path_output_cppe5, path_anno
```
Next, prepare an instance of a `CocoDetection` class that can be used with `cocoevaluator`.
```py
>>> import torchvision
>>> class CocoDetection(torchvision.datasets.CocoDetection):
... def __init__(self, img_folder, image_processor, ann_file):
... super().__init__(img_folder, ann_file)
... self.image_processor = image_processor
... def __getitem__(self, idx):
... # read in PIL image and target in COCO format
... img, target = super(CocoDetection, self).__getitem__(idx)
... # preprocess image and target: converting target to DETR format,
... # resizing + normalization of both image and target)
... image_id = self.ids[idx]
... target = {"image_id": image_id, "annotations": target}
... encoding = self.image_processor(images=img, annotations=target, return_tensors="pt")
... pixel_values = encoding["pixel_values"].squeeze() # remove batch dimension
... target = encoding["labels"][0] # remove batch dimension
... return {"pixel_values": pixel_values, "labels": target}
>>> im_processor = AutoImageProcessor.from_pretrained("devonho/detr-resnet-50_finetuned_cppe5")
>>> path_output_cppe5, path_anno = save_cppe5_annotation_file_images(cppe5["test"])
>>> test_ds_coco_format = CocoDetection(path_output_cppe5, im_processor, path_anno)
```
Finally, load the metrics and run the evaluation.
```py
>>> import evaluate
>>> from tqdm import tqdm
>>> model = AutoModelForObjectDetection.from_pretrained("devonho/detr-resnet-50_finetuned_cppe5")
>>> module = evaluate.load("ybelkada/cocoevaluate", coco=test_ds_coco_format.coco)
>>> val_dataloader = torch.utils.data.DataLoader(
... test_ds_coco_format, batch_size=8, shuffle=False, num_workers=4, collate_fn=collate_fn
... )
>>> with torch.no_grad():
... for idx, batch in enumerate(tqdm(val_dataloader)):
... pixel_values = batch["pixel_values"]
... pixel_mask = batch["pixel_mask"]
... labels = [
... {k: v for k, v in t.items()} for t in batch["labels"]
... ] # these are in DETR format, resized + normalized
... # forward pass
... outputs = model(pixel_values=pixel_values, pixel_mask=pixel_mask)
... orig_target_sizes = torch.stack([target["orig_size"] for target in labels], dim=0)
... results = im_processor.post_process(outputs, orig_target_sizes) # convert outputs of model to Pascal VOC format (xmin, ymin, xmax, ymax)
... module.add(prediction=results, reference=labels)
... del batch
>>> results = module.compute()
>>> print(results)
Accumulating evaluation results...
DONE (t=0.08s).
IoU metric: bbox
Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.352
Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.681
Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.292
Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.168
Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.208
Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.429
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.274
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.484
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.501
Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.191
Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.323
Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.590
```
These results can be further improved by adjusting the hyperparameters in [`~transformers.TrainingArguments`]. Give it a go!
## Inference
Now that you have finetuned a DETR model, evaluated it, and uploaded it to the Hugging Face Hub, you can use it for inference.
The simplest way to try out your finetuned model for inference is to use it in a [`Pipeline`]. Instantiate a pipeline
for object detection with your model, and pass an image to it:
```py
>>> from transformers import pipeline
>>> import requests
>>> url = "https://i.imgur.com/2lnWoly.jpg"
>>> image = Image.open(requests.get(url, stream=True).raw)
>>> obj_detector = pipeline("object-detection", model="devonho/detr-resnet-50_finetuned_cppe5")
>>> obj_detector(image)
```
You can also manually replicate the results of the pipeline if you'd like:
```py
>>> image_processor = AutoImageProcessor.from_pretrained("devonho/detr-resnet-50_finetuned_cppe5")
>>> model = AutoModelForObjectDetection.from_pretrained("devonho/detr-resnet-50_finetuned_cppe5")
>>> with torch.no_grad():
... inputs = image_processor(images=image, return_tensors="pt")
... outputs = model(**inputs)
... target_sizes = torch.tensor([image.size[::-1]])
... results = image_processor.post_process_object_detection(outputs, threshold=0.5, target_sizes=target_sizes)[0]
>>> for score, label, box in zip(results["scores"], results["labels"], results["boxes"]):
... box = [round(i, 2) for i in box.tolist()]
... print(
... f"Detected {model.config.id2label[label.item()]} with confidence "
... f"{round(score.item(), 3)} at location {box}"
... )
Detected Coverall with confidence 0.566 at location [1215.32, 147.38, 4401.81, 3227.08]
Detected Mask with confidence 0.584 at location [2449.06, 823.19, 3256.43, 1413.9]
```
Let's plot the result:
```py
>>> draw = ImageDraw.Draw(image)
>>> for score, label, box in zip(results["scores"], results["labels"], results["boxes"]):
... box = [round(i, 2) for i in box.tolist()]
... x, y, x2, y2 = tuple(box)
... draw.rectangle((x, y, x2, y2), outline="red", width=1)
... draw.text((x, y), model.config.id2label[label.item()], fill="white")
>>> image
```
<div class="flex justify-center">
<img src="https://i.imgur.com/4QZnf9A.png" alt="Object detection result on a new image"/>
</div>
| huggingface/transformers/blob/main/docs/source/en/tasks/object_detection.md |
Fine-Tuning week of XLSR-Wav2Vec2 on 60 languages 🌍
Welcome to the fine-tuning week! The goal of this week is to have state-of-the-art automatic speech recognition (ASR) models in as many languages as possible. The fine-tuning week ends on Friday, the 26th March at midnight PST time.
Participants are encouraged to fine-tune the pretrained [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) checkpoint on one or more of the 60 languages of [Common Voice dataset](https://commonvoice.mozilla.org/en/datasets).
Furthermore, it is very much appreciated if participants fine-tune XLSR-Wav2Vec2 on a language that is not included in the Common Voice dataset.
All fine-tuned models uploaded until Friday, the 26th March midnight PST, will be taken into account for competition, and the best model per language will be awarded a prize if the best model performs reasonably well.
The testing data to evaluate the models will be the official [Common Voice dataset](https://commonvoice.mozilla.org/en/datasets) *`test data`* of version 6.1. Again, participants are very much encouraged to fine-tune XLSR-Wav2Vec2 on languages that are not found in the Common Voice dataset since those languages are even more likely to be underrepresented in the speech community.
Each model fine-tuned on a language not found in Common Voice, will be evaluated by the Hugging Face team after Friday, the 26th March at midnight PST, and if the model performs reasonably well, the model receives a prize as well.
For more information on which data can be used for training, how the models are evaluated exactly, and what type of data preprocessing can be used, please see ["Training and Evaluation Rules"](#training-and-evaluation-rules).
**Please keep in mind:**
The spirit of the fine-tuning week is to provide state-of-the-art speech recognition in as many languages as possible to the community!
So while we encourage healthy competition between people/groups of the same language so that better results are obtained, it is extremely important that we help each other and share our insights with the whole team/community.
What matters in the end is what has been achieved by the team as a whole during the fine-tuning week.
That being said, we strongly encourage people to share tips & tricks on the forum or Slack, help each other when team members encounter bugs, and work in groups.
To make it easier to share and help, forum threads have been created under the name {language} ASR: Fine-Tuning Wav2Vec2, e.g. here.
It is very much possible that prizes will be given to groups of people instead of individuals. Also, don't hesitate to ask questions, propose improvements to the organization, to the material given to participants, etc...🤗
## Table of Contents
- [Organization of the fine tuning week](#organization-of-the-fine-tuning-week)
- [How to fine tune XLSR Wav2Vec2](#how-to-fine-tune-xlsr-wav2vec2)
- [Google colab setup](#google-colab-setup)
- [Local machine](#local-machine)
- [How to upload my trained checkpoint](#how-to-upload-my-trained-checkpoint)
- [How to create the README](#how-to-create-the-readme)
- [How to evaluate my trained checkpoint](#how-to-evaluate-my-trained-checkpoint)
- [Rules of training and evaluation](#rules-of-training-and-evaluation)
- [Tips and tricks](#tips-and-tricks)
- [How to combine multiple datasests into one](#how-to-combine-multiple-datasets-into-one)
- [How to effectively preprocess the data](#how-to-effectively-preprocess-the-data)
- [How to efficiently preproces the data](#how-to-do-efficiently-load-datasets-with-limited-ram-and-hard-drive-space)
- [How to do hyperparameter tuning](#how-to-do-hyperparameter-tuning)
- [How to preprocess and evaluate character based languages](#how-to-preprocess-and-evaluate-character-based-languages)
- [Further reading material](#further-reading-material)
- [FAQ](#faq)
## Organization of the fine tuning week
The week officially starts on 22.03.2021 and ends on 29.03.2021, but you are more than welcome to start fine-tuning models before the start date.
General questions you might have, general problems you encounter, and general tips can be shared directly on the Slack channel (see [this post](https://discuss.huggingface.co/t/open-to-the-community-xlsr-wav2vec2-fine-tuning-week-for-low-resource-languages/4467) on how to be added to Slack).
More language-specific questions or specific bugs should be posted on the [forum](https://discuss.huggingface.co/) (feel free to use already existing language-specific threads, *e.g.* [this one](https://discuss.huggingface.co/t/arabic-asr-fine-tuning-wav2vec2/4608) or open a new one if there is no thread for your language yet) or directly on [github](https://github.com/huggingface/transformers) if you think some code or document needs correction/improvement.
Starting on Monday, the 22.03.2021, the Hugging Face team will try to provide an overview of currently trained models along with their evaluation results.
All the necessary information on:
- How to fine-tune the XLSR model
- How to upload the model
- How to share your evaluation results & training/eval script
- What are the training/evaluation rules
can be found in the sections below. If something is still unclear, feel free to drop a message in the Slack channel.
## How to fine tune XLSR Wav2Vec2
This chapter gives an in-detail explanation of how to fine-tune [Facebook's multi-lingual Wav2vec2](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on any language of the [Common Voice dataset](https://commonvoice.mozilla.org/en/datasets).
Two possible setups can be used to fine-tune Wav2Vec2. The easiest setup is to simply use [google colab](https://colab.research.google.com/). It is possible to train the full model in a *free* google colab, but it is recommended to use google colab pro since it is more stable.
The other option is to run a script locally. While this can be more difficult to set up, it also means that you have more control over the training run and probably access to better GPUs than you would have in a google colab.
For small datasets, it is usually totally sufficient to train your model
in a google colab. For larger and thus more memory-intensive datasets, it is probably
better to fine-tune the model locally.
For each option, we explain in detail how to fine-tune XLSR-Wav2Vec2 in the following.
### Google colab setup
**Note**: Instead of reading the following section, you can simply watch [this](https://www.youtube.com/watch?v=UynYn2C3tI0&ab_channel=PatrickvonPlaten) video, where Patrick explains how to adapt the google colab for your specific language.
**1.**: If you plan on training XLSR-Wav2Vec2 in a google colab, you should first make sure to have a valid gmail account. You can sign up for a gmail account [here](https://accounts.google.com/signup/v2/webcreateaccount?hl=en&flowName=GlifWebSignIn&flowEntry=SignUp).
Having successfully signed up for gmail, you can now sign in to your account to make sure you are logged in when opening new tabs in your browser.
**2.**: Next, head over to the official [Fine-Tune XLSR-Wav2Vec2 with 🤗 Transformes](https://colab.research.google.com/github/patrickvonplaten/notebooks/blob/master/Fine_Tune_XLSR_Wav2Vec2_on_Turkish_ASR_with_%F0%9F%A4%97_Transformers.ipynb) google colab. The first thing you should do is to make a copy of it - click `->File->Save a copy in Drive`. This should save a copy of the google colab in your google drive.
**3.**: Now it is highly recommended to carefully read the google colab without running the cells yet.
You should get an understanding of the model is trained and what you will have to change when training the model in a different language.
Having done so, you can again head over to [Common Voice](https://commonvoice.mozilla.org/en/datasets) and pick a language you want to fine-tune [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on. Make sure you remember the language code (For each language, you can find it under the field "*Version*". It corresponds to **all characters before the first underscore**. *E.g.* for Greek it is *el*, while for Irish it is *ga-IE*.
**4.**: Now you should replace the language code used for the demo of this colab, being *tr* for Turkish with the language code corresponding to the language you just chose in the **second** cell of the google colab. This will load the correct data for your language.
**5.**: It is time to start running the google colab! Make sure that you have selected "GPU" as your runtime environment and you can start running the cells one-by-one. Make sure you attentively read the text between the cells to understand what is happening and to eventually correct the cells to improve the fine-tuning script for your language. Things you might want to improve/change:
- Data loading. It is very much recommended to use more than just the official training data of the Common Voice dataset. If you find more data on the internet, feel free to use it! Check out the section ["How to combined multiple datasets into one"](#how-to-combine-multiple-datasets-into-one)
- Data Processing. You should adapt the data processing to your specific language. In data processing, you should make the data more uniform so that it will be easier for the model to learn how to classify speech in your data. Here it can be really helpful to be proficient in the language to know what can be done to simplify the language without changing the meaning.
Data processing methods include, but are not limited to:
- Normalizing your data. Make sure all characters are lower-cased.
- Remove typographical symbols and punctuation marks. See a list [here](https://en.wikipedia.org/wiki/List_of_typographical_symbols_and_punctuation_marks). Be careful to not remove punctuation marks that can change the meaning of the sentence. *E.g.* you should not remove the single quotation mark `'` in English, as it would change the words `"it's"` to `"its"` which is a different word and has thus a different meaning. For more tips on data processing see ["How to effectively preprocess the data"](#how-to-effectively-preprocess-the-data")
- Hyperparameter Tuning. Depending on the size of the data you should probably change the hyperparameters of the google colab. You can change any parameter you like. For more tips and tricks see ["How to do hyperparameter tuning for my language"](#how-to-do-hyperparameter-tuning-for-my-language)
When running the google colab make sure that you uncomment the cell corresponding to mounting your google drive to the colab. This cell looks as follows:
```python
# from google.colab import drive
# drive.mount('/content/gdrive/')
```
Uncomment it, run it, and follow the instructions to mount your google drive. This way you can be sure that the model parameters and created tokenizer & feature extractor files are saved in **your** google drive.
Also, make sure that you uncomment the cells corresponding to save the preprocessing files and trained model weights to your drive. Otherwise, you might lose a trained model if you google crashes. You should change the name of your model from `wav2vec2-large-xlsr-turkish-demo` to `wav2vec2-large-xlsr-{your_favorite_name}`.
Those cells correspond to:
```python
# processor.save_pretrained("/content/gdrive/MyDrive/wav2vec2-large-xlsr-turkish-demo")
```
and the line:
```python
output_dir="/content/gdrive/MyDrive/wav2vec2-large-xlsr-turkish-demo",
```
further below (which should already be uncommented).
Having finished the training you should find the following files/folders under the folder `wav2vec2-large-xlsr-{your_favorite_name}` in your google drive:
- `preprocessor_config.json` - the parameters of the feature extractor
- `special_tokens_map.json` - the special token map of the tokenizer
- `tokenizer_config.json` - the parameters of the tokenizer
- `vocab.json` - the vocabulary of the tokenizer
- `checkpoint-{...}/` - the saved checkpoints saved during training. Each checkpoint should contain the files: `config.json`, `optimizer.pt`, `pytorch_model.bin`, `scheduler.pt`, `training_args.bin`. The files `config.json` and `pytorch_model.bin` define your model.
If you are happy with your training results it is time to upload your model!
Download the following files to your local computer: **`preprocessor_config.json`, `special_tokens_map.json`, `tokenizer_config.json`, `vocab.json`, `config.json`, `pytorch_model.bin`**. Those files fully define a XLSR-Wav2Vec2 model checkpoint.
Awesome you have successfully trained a XLSR-Wav2Vec2 model 😎. Now you can jump to the section ["How to upload my trained checkpoint"](#how-to-upload-my-trained-checkpoint)
### Local machine
We have provided `run_common_voice.py` script to run fine-tuning on local machine. The script is similar to the colab but allows you to launch training using command line, save and continue training from previous checkpoints and launch training on multiple GPUs.
For bigger datasets, we recommend to train Wav2Vec2 locally instead of in a google colab.
1. To begin with, we should clone transformers localy and install all the required packages.
First, you need to clone the `transformers` repo with:
```
$ git clone https://github.com/huggingface/transformers.git
```
Second, head over to the `examples/research_projects/wav2vec2` directory, where the `run_common_voice.py` script is located.
```
$ cd transformers/examples/research_projects/wav2vec2
```
Third, install the required packages. The
packages are listed in the `requirements.txt` file and can be installed with
```
$ pip install -r requirements.txt
```
**Note**: Installing the latest version of `torchaudio` will also upgrade `torch` to it's latest stable version. If you are using specific version of `torch` then make sure
to use the correct `torchaudio` version compatible with your version of `torch`. By default the `requirements.txt` will install the latest version of `torchaudio`.
2. Next, take a look at the `run_common_voice.py` script to get an understanding of how it works. In short the script does the following:
- Load the given common voice dataset
- Create vocab for the language
- Load the model with given hyperparameters
- Pre-process the dataset to input into the model
- Run training
- Run evaluation
3. The following examples show how you can launch fine-tuning for the common voice dataset.
Here we will run the script on the *Turkish* Common Voice dataset for demonstration purposes.
**To lanuch fine-tuninig on a single GPU:**
```bash
python run_common_voice.py \
--model_name_or_path="facebook/wav2vec2-large-xlsr-53" \
--dataset_config_name="tr" \ # use this argument to specify the language code
--output_dir=./wav2vec2-large-xlsr-turkish-demo \
--overwrite_output_dir \
--num_train_epochs="5" \
--per_device_train_batch_size="16" \
--learning_rate="3e-4" \
--warmup_steps="500" \
--evaluation_strategy="steps" \
--save_steps="400" \
--eval_steps="400" \
--logging_steps="400" \
--save_total_limit="3" \
--freeze_feature_extractor \
--feat_proj_dropout="0.0" \
--layerdrop="0.1" \
--gradient_checkpointing \
--fp16 \
--group_by_length \
--do_train --do_eval
```
**To lanuch fine-tuninig on multiple GPUs:**
```bash
python -m torch.distributed.launch \
--nproc_per_node 4 run_common_voice.py \
--model_name_or_path="facebook/wav2vec2-large-xlsr-53" \
--dataset_config_name="tr" \ # use this argument to specify the language code
--output_dir=./wav2vec2-large-xlsr-turkish-demo \
--overwrite_output_dir \
--num_train_epochs="5" \
--per_device_train_batch_size="16" \
--learning_rate="3e-4" \
--warmup_steps="500" \
--evaluation_strategy="steps" \
--save_steps="400" \
--eval_steps="400" \
--logging_steps="400" \
--save_total_limit="3" \
--freeze_feature_extractor \
--feat_proj_dropout="0.0" \
--layerdrop="0.1" \
--gradient_checkpointing \
--fp16 \
--group_by_length \
--do_train --do_eval
```
The above command will launch the training on 4 GPUs. Use the `--nproc_per_node` option to specify the number of GPUs.
Once the training is finished, the model and checkpoints will be saved under the directory specified by the `--output_dir` argument.
4. The script also allows you to resume training from the last saved checkpoint. To resume training from last saved checkpoint remove the `--overwrite_output_dir` option and run the same command again. And to continue training from a specific checkpoint, keep the `--overwrite_output_dir`
option and pass the path of the checkpoint as `--model_name_or_path`.
As the script is based on the `Trainer` API, refer to the [Trainer docs](https://huggingface.co/transformers/main_classes/trainer.html) for more information about ``Trainer`` and ``TrainingArguments``.
[OVH cloud](https://www.ovh.com/world/) has generously offered free compute for this sprint. Please refer to [this video](https://www.youtube.com/watch?v=2hlkWAESMk8&ab_channel=Databuzzword) to get started with OVH.
## How to upload my trained checkpoint
To upload your trained checkpoint, you have to create a new model repository on the 🤗 model hub, from this page: https://huggingface.co/new
> You can also follow the more in-depth instructions [here](https://huggingface.co/transformers/model_sharing.html) if needed.
Having created your model repository on the hub, you should clone it locally:
```bash
git lfs install
git clone https://huggingface.co/username/your-model-name
```
Then and add the following files that fully define a XLSR-Wav2Vec2 checkpoint into the repository. You should have added the following files.
- `preprocessor_config.json`
- `special_tokens_map.json`
- `tokenizer_config.json`
- `vocab.json`
- `config.json`
- `pytorch_model.bin`
Having added the above files, you should run the following to push files to your model repository.
```
git add . && git commit -m "Add model files" && git push
```
The next **very important** step is to create the model card. For people to use your fine-tuned
model it is important to understand:
- What kind of model is it?
- What is your model useful for?
- What data was your model trained on?
- How well does your model perform?
All these questions should be answered in a model card which is the first thing people see when
visiting your model on the hub under `https://huggingface.co/{your_username}/{your_modelname}`.
**Note**:
It is extremely important that you add this model card or else we cannot find your model and thus cannot take the model into
account for the final evaluation.
### How to create the readme
The model card is written in markdown (`.md`) and should be added by simply clicking on the "Add model card" button which is found on the top right corner.
You are encouraged to copy-paste the following template into your model card.
**Make sure that** instead of copying the output of the markdown file you copy the **raw** version of the following part.
To get the raw version of this file, simply click on the "`raw`" button on the top right corner of this file next to "`blame`" and copy everything below the marker.
Make sure that you read and consequently remove all #TODO: statements from the model card.
<======================Copy **raw** version from here=========================
---
language: {lang_id} #TODO: replace {lang_id} in your language code here. Make sure the code is one of the *ISO codes* of [this](https://huggingface.co/languages) site.
datasets:
- common_voice #TODO: remove if you did not use the common voice dataset
- TODO: add more datasets if you have used additional datasets. Make sure to use the exact same
dataset name as the one found [here](https://huggingface.co/datasets). If the dataset can not be found in the official datasets, just give it a new name
metrics:
- wer
tags:
- audio
- automatic-speech-recognition
- speech
- xlsr-fine-tuning-week
license: apache-2.0
model-index:
- name: {human_readable_name} #TODO: replace {human_readable_name} with a name of your model as it should appear on the leaderboard. It could be something like `Elgeish XLSR Wav2Vec2 Large 53`
results:
- task:
name: Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice {lang_id} #TODO: replace {lang_id} in your language code here. Make sure the code is one of the *ISO codes* of [this](https://huggingface.co/languages) site.
type: common_voice
args: {lang_id} #TODO: replace {lang_id} in your language code here. Make sure the code is one of the *ISO codes* of [this](https://huggingface.co/languages) site.
metrics:
- name: Test WER
type: wer
value: {wer_result_on_test} #TODO (IMPORTANT): replace {wer_result_on_test} with the WER error rate you achieved on the common_voice test set. It should be in the format XX.XX (don't add the % sign here). **Please** remember to fill out this value after you evaluated your model, so that your model appears on the leaderboard. If you fill out this model card before evaluating your model, please remember to edit the model card afterward to fill in your value
---
# Wav2Vec2-Large-XLSR-53-{language} #TODO: replace language with your {language}, *e.g.* French
Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on {language} using the [Common Voice](https://huggingface.co/datasets/common_voice), ... and ... dataset{s}. #TODO: replace {language} with your language, *e.g.* French and eventually add more datasets that were used and eventually remove common voice if model was not trained on common voice
When using this model, make sure that your speech input is sampled at 16kHz.
## Usage
The model can be used directly (without a language model) as follows:
```python
import torch
import torchaudio
from datasets import load_dataset
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
test_dataset = load_dataset("common_voice", "{lang_id}", split="test[:2%]") #TODO: replace {lang_id} in your language code here. Make sure the code is one of the *ISO codes* of [this](https://huggingface.co/languages) site.
processor = Wav2Vec2Processor.from_pretrained("{model_id}") #TODO: replace {model_id} with your model id. The model id consists of {your_username}/{your_modelname}, *e.g.* `elgeish/wav2vec2-large-xlsr-53-arabic`
model = Wav2Vec2ForCTC.from_pretrained("{model_id}") #TODO: replace {model_id} with your model id. The model id consists of {your_username}/{your_modelname}, *e.g.* `elgeish/wav2vec2-large-xlsr-53-arabic`
resampler = torchaudio.transforms.Resample(48_000, 16_000)
# Preprocessing the datasets.
# We need to read the aduio files as arrays
def speech_file_to_array_fn(batch):
speech_array, sampling_rate = torchaudio.load(batch["path"])
batch["speech"] = resampler(speech_array).squeeze().numpy()
return batch
test_dataset = test_dataset.map(speech_file_to_array_fn)
inputs = processor(test_dataset[:2]["speech"], sampling_rate=16_000, return_tensors="pt", padding=True)
with torch.no_grad():
logits = model(inputs.input_values, attention_mask=inputs.attention_mask).logits
predicted_ids = torch.argmax(logits, dim=-1)
print("Prediction:", processor.batch_decode(predicted_ids))
print("Reference:", test_dataset[:2]["sentence"])
```
## Evaluation
The model can be evaluated as follows on the {language} test data of Common Voice. # TODO: replace #TODO: replace language with your {language}, *e.g.* French
```python
import torch
import torchaudio
from datasets import load_dataset, load_metric
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
import re
test_dataset = load_dataset("common_voice", "{lang_id}", split="test") #TODO: replace {lang_id} in your language code here. Make sure the code is one of the *ISO codes* of [this](https://huggingface.co/languages) site.
wer = load_metric("wer")
processor = Wav2Vec2Processor.from_pretrained("{model_id}") #TODO: replace {model_id} with your model id. The model id consists of {your_username}/{your_modelname}, *e.g.* `elgeish/wav2vec2-large-xlsr-53-arabic`
model = Wav2Vec2ForCTC.from_pretrained("{model_id}") #TODO: replace {model_id} with your model id. The model id consists of {your_username}/{your_modelname}, *e.g.* `elgeish/wav2vec2-large-xlsr-53-arabic`
model.to("cuda")
chars_to_ignore_regex = '[\,\?\.\!\-\;\:\"\“]' # TODO: adapt this list to include all special characters you removed from the data
resampler = torchaudio.transforms.Resample(48_000, 16_000)
# Preprocessing the datasets.
# We need to read the aduio files as arrays
def speech_file_to_array_fn(batch):
batch["sentence"] = re.sub(chars_to_ignore_regex, '', batch["sentence"]).lower()
speech_array, sampling_rate = torchaudio.load(batch["path"])
batch["speech"] = resampler(speech_array).squeeze().numpy()
return batch
test_dataset = test_dataset.map(speech_file_to_array_fn)
# Preprocessing the datasets.
# We need to read the aduio files as arrays
def evaluate(batch):
inputs = processor(batch["speech"], sampling_rate=16_000, return_tensors="pt", padding=True)
with torch.no_grad():
logits = model(inputs.input_values.to("cuda"), attention_mask=inputs.attention_mask.to("cuda")).logits
pred_ids = torch.argmax(logits, dim=-1)
batch["pred_strings"] = processor.batch_decode(pred_ids)
return batch
result = test_dataset.map(evaluate, batched=True, batch_size=8)
print("WER: {:2f}".format(100 * wer.compute(predictions=result["pred_strings"], references=result["sentence"])))
```
**Test Result**: XX.XX % # TODO: write output of print here. IMPORTANT: Please remember to also replace {wer_result_on_test} at the top of with this value here. tags.
## Training
The Common Voice `train`, `validation`, and ... datasets were used for training as well as ... and ... # TODO: adapt to state all the datasets that were used for training.
The script used for training can be found [here](...) # TODO: fill in a link to your training script here. If you trained your model in a colab, simply fill in the link here. If you trained the model locally, it would be great if you could upload the training script on github and paste the link here.
=======================To here===============================>
Your model in then available under *huggingface.co/{your_username}/{your_chosen_xlsr-large_model_name}* for everybody to use 🎉.
## How to evaluate my trained checkpoint
Having uploaded your model, you should now evaluate your model in a final step. This should be as simple as
copying the evaluation code of your model card into a python script and running it. Make sure to note
the final result on the model card **both** under the YAML tags at the very top **and** below your evaluation code under "Test Results".
## Rules of training and evaluation
In this section, we will quickly go over what data is allowed to be used as training
data, what kind of data preprocessing is allowed be used, and how the model should be evaluated.
To make it very simple regarding the first point: **All data except the official common voice `test` data set can be used as training data**. For models trained in a language that is not included in Common Voice, the author of the model is responsible to
leave a reasonable amount of data for evaluation.
Second, the rules regarding the preprocessing are not that as straight-forward. It is allowed (and recommended) to
normalize the data to only have lower-case characters. It is also allowed (and recommended) to remove typographical
symbols and punctuation marks. A list of such symbols can *e.g.* be fonud [here](https://en.wikipedia.org/wiki/List_of_typographical_symbols_and_punctuation_marks) - however here we already must be careful. We should **not** remove a symbol that
would change the meaning of the words, *e.g.* in English, we should not remove the single quotation mark `'` since it
would change the meaning of the word `"it's"` to `"its"` which would then be incorrect. So the golden rule here is to
not remove any characters that could change the meaning of a word into another word. This is not always obvious and should
be given some consideration. As another example, it is fine to remove the "Hypen-minus" sign "`-`" since it doesn't change the
meaninng of a word to another one. *E.g.* "`fine-tuning`" would be changed to "`finetuning`" which has still the same meaning.
Since those choices are not always obvious when in doubt feel free to ask on Slack or even better post on the forum, as was
done, *e.g.* [here](https://discuss.huggingface.co/t/spanish-asr-fine-tuning-wav2vec2/4586).
## Tips and tricks
This section summarizes a couple of tips and tricks across various topics. It will continously be updated during the week.
### How to combine multiple datasets into one
Check out [this](https://discuss.huggingface.co/t/how-to-combine-local-data-files-with-an-official-dataset/4685) post.
### How to effectively preprocess the data
### How to do efficiently load datasets with limited ram and hard drive space
Check out [this](https://discuss.huggingface.co/t/german-asr-fine-tuning-wav2vec2/4558/8?u=patrickvonplaten) post.
### How to do hyperparameter tuning
### How to preprocess and evaluate character based languages
## Further reading material
It is recommended that take some time to read up on how Wav2vec2 works in theory.
Getting a better understanding of the theory and the inner mechanisms of the model often helps when fine-tuning the model.
**However**, if you don't like reading blog posts/papers, don't worry - it is by no means necessary to go through the theory to fine-tune Wav2Vec2 on your language of choice.
If you are interested in learning more about the model though, here are a couple of resources that are important to better understand Wav2Vec2:
- [Facebook's Wav2Vec2 blog post](https://ai.facebook.com/blog/wav2vec-state-of-the-art-speech-recognition-through-self-supervision/)
- [Official Wav2Vec2 paper](https://arxiv.org/abs/2006.11477)
- [Official XLSR Wav2vec2 paper](https://arxiv.org/pdf/2006.13979.pdf)
- [Hugging Face Blog](https://huggingface.co/blog/fine-tune-xlsr-wav2vec2)
- [How does CTC (Connectionist Temporal Classification) work](https://distill.pub/2017/ctc/)
It helps to have a good understanding of the following points:
- How was XLSR-Wav2Vec2 pretrained? -> Feature vectors were masked and had to be predicted by the model; very similar in spirit to masked language model of BERT.
- What parts of XLSR-Wav2Vec2 are responsible for what? What is the feature extractor part used for? -> extract feature vectors from the 1D raw audio waveform; What is the transformer part doing? -> mapping feature vectors to contextualized feature vectors; ...
- What part of the model needs to be fine-tuned? -> The pretrained model **does not** include a language head to classify the contextualized features to letters. This is randomly initialized when loading the pretrained checkpoint and has to be fine-tuned. Also, note that the authors recommend to **not** further fine-tune the feature extractor.
- What data was used to XLSR-Wav2Vec2? The checkpoint we will use for further fine-tuning was pretrained on **53** languages.
- What languages are considered to be similar by XLSR-Wav2Vec2? In the official [XLSR Wav2Vec2 paper](https://arxiv.org/pdf/2006.13979.pdf), the authors show nicely which languages share a common contextualized latent space. It might be useful for you to extend your training data with data of other languages that are considered to be very similar by the model (or you).
## FAQ
- Can a participant fine-tune models for more than one language?
Yes! A participant can fine-tune models in as many languages she/he likes
- Can a participant use extra data (apart from the common voice data)?
Yes! All data except the official common voice `test data` can be used for training.
If a participant wants to train a model on a language that is not part of Common Voice (which
is very much encouraged!), the participant should make sure that some test data is held out to
make sure the model is not overfitting.
- Can we fine-tune for high-resource languages?
Yes! While we do not really recommend people to fine-tune models in English since there are
already so many fine-tuned speech recognition models in English. However, it is very much
appreciated if participants want to fine-tune models in other "high-resource" languages, such
as French, Spanish, or German. For such cases, one probably needs to train locally and apply
might have to apply tricks such as lazy data loading (check the ["Lazy data loading"](#how-to-do-lazy-data-loading) section for more details).
| huggingface/transformers/blob/main/examples/research_projects/wav2vec2/FINE_TUNE_XLSR_WAV2VEC2.md |
!--Copyright 2023 The HuggingFace Team. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
the License. You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
specific language governing permissions and limitations under the License.
⚠️ Note that this file is in Markdown but contain specific syntax for our doc-builder (similar to MDX) that may not be
rendered properly in your Markdown viewer.
-->
# MPT
## Overview
The MPT model was proposed by the [MosaicML](https://www.mosaicml.com/) team and released with multiple sizes and finetuned variants. The MPT models is a series of open source and commercially usable LLMs pre-trained on 1T tokens.
MPT models are GPT-style decoder-only transformers with several improvements: performance-optimized layer implementations, architecture changes that provide greater training stability, and the elimination of context length limits by replacing positional embeddings with ALiBi.
- MPT base: MPT base pre-trained models on next token prediction
- MPT instruct: MPT base models fine-tuned on instruction based tasks
- MPT storywriter: MPT base models fine-tuned for 2500 steps on 65k-token excerpts of fiction books contained in the books3 corpus, this enables the model to handle very long sequences
The original code is available at the [`llm-foundry`](https://github.com/mosaicml/llm-foundry/tree/main) repository.
Read more about it [in the release blogpost](https://www.mosaicml.com/blog/mpt-7b)
## Usage tips
- Learn more about some techniques behind training of the model [in this section of llm-foundry repository](https://github.com/mosaicml/llm-foundry/blob/main/TUTORIAL.md#faqs)
- If you want to use the advanced version of the model (triton kernels, direct flash attention integration), you can still use the original model implementation by adding `trust_remote_code=True` when calling `from_pretrained`.
## Resources
- [Fine-tuning Notebook](https://colab.research.google.com/drive/1HCpQkLL7UXW8xJUJJ29X7QAeNJKO0frZ?usp=sharing) on how to fine-tune MPT-7B on a free Google Colab instance to turn the model into a Chatbot.
## MptConfig
[[autodoc]] MptConfig
- all
## MptModel
[[autodoc]] MptModel
- forward
## MptForCausalLM
[[autodoc]] MptForCausalLM
- forward
## MptForSequenceClassification
[[autodoc]] MptForSequenceClassification
- forward
## MptForTokenClassification
[[autodoc]] MptForTokenClassification
- forward
## MptForQuestionAnswering
[[autodoc]] MptForQuestionAnswering
- forward
| huggingface/transformers/blob/main/docs/source/en/model_doc/mpt.md |
!--Copyright 2023 The HuggingFace Team. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
the License. You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
specific language governing permissions and limitations under the License.
⚠️ Note that this file is in Markdown but contain specific syntax for our doc-builder (similar to MDX) that may not be
rendered properly in your Markdown viewer.
-->
# OWLv2
## Overview
OWLv2 was proposed in [Scaling Open-Vocabulary Object Detection](https://arxiv.org/abs/2306.09683) by Matthias Minderer, Alexey Gritsenko, Neil Houlsby. OWLv2 scales up [OWL-ViT](owlvit) using self-training, which uses an existing detector to generate pseudo-box annotations on image-text pairs. This results in large gains over the previous state-of-the-art for zero-shot object detection.
The abstract from the paper is the following:
*Open-vocabulary object detection has benefited greatly from pretrained vision-language models, but is still limited by the amount of available detection training data. While detection training data can be expanded by using Web image-text pairs as weak supervision, this has not been done at scales comparable to image-level pretraining. Here, we scale up detection data with self-training, which uses an existing detector to generate pseudo-box annotations on image-text pairs. Major challenges in scaling self-training are the choice of label space, pseudo-annotation filtering, and training efficiency. We present the OWLv2 model and OWL-ST self-training recipe, which address these challenges. OWLv2 surpasses the performance of previous state-of-the-art open-vocabulary detectors already at comparable training scales (~10M examples). However, with OWL-ST, we can scale to over 1B examples, yielding further large improvement: With an L/14 architecture, OWL-ST improves AP on LVIS rare classes, for which the model has seen no human box annotations, from 31.2% to 44.6% (43% relative improvement). OWL-ST unlocks Web-scale training for open-world localization, similar to what has been seen for image classification and language modelling.*
<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/model_doc/owlv2_overview.png"
alt="drawing" width="600"/>
<small> OWLv2 high-level overview. Taken from the <a href="https://arxiv.org/abs/2306.09683">original paper</a>. </small>
This model was contributed by [nielsr](https://huggingface.co/nielsr).
The original code can be found [here](https://github.com/google-research/scenic/tree/main/scenic/projects/owl_vit).
## Usage example
OWLv2 is, just like its predecessor [OWL-ViT](owlvit), a zero-shot text-conditioned object detection model. OWL-ViT uses [CLIP](clip) as its multi-modal backbone, with a ViT-like Transformer to get visual features and a causal language model to get the text features. To use CLIP for detection, OWL-ViT removes the final token pooling layer of the vision model and attaches a lightweight classification and box head to each transformer output token. Open-vocabulary classification is enabled by replacing the fixed classification layer weights with the class-name embeddings obtained from the text model. The authors first train CLIP from scratch and fine-tune it end-to-end with the classification and box heads on standard detection datasets using a bipartite matching loss. One or multiple text queries per image can be used to perform zero-shot text-conditioned object detection.
[`Owlv2ImageProcessor`] can be used to resize (or rescale) and normalize images for the model and [`CLIPTokenizer`] is used to encode the text. [`Owlv2Processor`] wraps [`Owlv2ImageProcessor`] and [`CLIPTokenizer`] into a single instance to both encode the text and prepare the images. The following example shows how to perform object detection using [`Owlv2Processor`] and [`Owlv2ForObjectDetection`].
```python
>>> import requests
>>> from PIL import Image
>>> import torch
>>> from transformers import Owlv2Processor, Owlv2ForObjectDetection
>>> processor = Owlv2Processor.from_pretrained("google/owlv2-base-patch16-ensemble")
>>> model = Owlv2ForObjectDetection.from_pretrained("google/owlv2-base-patch16-ensemble")
>>> url = "http://images.cocodataset.org/val2017/000000039769.jpg"
>>> image = Image.open(requests.get(url, stream=True).raw)
>>> texts = [["a photo of a cat", "a photo of a dog"]]
>>> inputs = processor(text=texts, images=image, return_tensors="pt")
>>> outputs = model(**inputs)
>>> # Target image sizes (height, width) to rescale box predictions [batch_size, 2]
>>> target_sizes = torch.Tensor([image.size[::-1]])
>>> # Convert outputs (bounding boxes and class logits) to Pascal VOC Format (xmin, ymin, xmax, ymax)
>>> results = processor.post_process_object_detection(outputs=outputs, target_sizes=target_sizes, threshold=0.1)
>>> i = 0 # Retrieve predictions for the first image for the corresponding text queries
>>> text = texts[i]
>>> boxes, scores, labels = results[i]["boxes"], results[i]["scores"], results[i]["labels"]
>>> for box, score, label in zip(boxes, scores, labels):
... box = [round(i, 2) for i in box.tolist()]
... print(f"Detected {text[label]} with confidence {round(score.item(), 3)} at location {box}")
Detected a photo of a cat with confidence 0.614 at location [341.67, 17.54, 642.32, 278.51]
Detected a photo of a cat with confidence 0.665 at location [6.75, 38.97, 326.62, 354.85]
```
## Resources
- A demo notebook on using OWLv2 for zero- and one-shot (image-guided) object detection can be found [here](https://github.com/NielsRogge/Transformers-Tutorials/tree/master/OWLv2).
- [Zero-shot object detection task guide](../tasks/zero_shot_object_detection)
<Tip>
The architecture of OWLv2 is identical to [OWL-ViT](owlvit), however the object detection head now also includes an objectness classifier, which predicts the (query-agnostic) likelihood that a predicted box contains an object (as opposed to background). The objectness score can be used to rank or filter predictions independently of text queries.
Usage of OWLv2 is identical to [OWL-ViT](owlvit) with a new, updated image processor ([`Owlv2ImageProcessor`]).
</Tip>
## Owlv2Config
[[autodoc]] Owlv2Config
- from_text_vision_configs
## Owlv2TextConfig
[[autodoc]] Owlv2TextConfig
## Owlv2VisionConfig
[[autodoc]] Owlv2VisionConfig
## Owlv2ImageProcessor
[[autodoc]] Owlv2ImageProcessor
- preprocess
- post_process_object_detection
- post_process_image_guided_detection
## Owlv2Processor
[[autodoc]] Owlv2Processor
## Owlv2Model
[[autodoc]] Owlv2Model
- forward
- get_text_features
- get_image_features
## Owlv2TextModel
[[autodoc]] Owlv2TextModel
- forward
## Owlv2VisionModel
[[autodoc]] Owlv2VisionModel
- forward
## Owlv2ForObjectDetection
[[autodoc]] Owlv2ForObjectDetection
- forward
- image_guided_detection
| huggingface/transformers/blob/main/docs/source/en/model_doc/owlv2.md |
Flax/JAX community week 🤗
Welcome to the Flax/JAX community week! The goal of this week is to make compute-intensive NLP and CV projects (like pre-training BERT, GPT2, CLIP, ViT)
practicable for a wider audience of engineers and researchers.
To do so, we will try to teach **you** how to effectively use JAX/Flax on TPU and help you to complete a fun NLP and/or CV project in JAX/Flax during the community week.
Free access to a TPUv3-8 will kindly be provided by the Google Cloud team!
In this document, we list all the important information that you will need during the Flax/JAX community week.
Don't forget to sign up [here](https://forms.gle/tVGPhjKXyEsSgUcs8)!
## Table of Contents
- [Organization](#organization)
- [Important dates](#important-dates)
- [Communication](#communication)
- [Projects](#projects)
- [How to propose](#how-to-propose-a-project)
- [How to form a team](#how-to-form-a-team-around-a-project)
- [Tips & Tricks for project](#tips-on-how-to-organize-the-project)
- [How to install flax, jax, optax, transformers, datasets](#how-to-install-relevant-libraries)
- [Quickstart Flax/JAX](#quickstart-flax-and-jax)
- [Quickstart Flax/JAX in 🤗 Transformers](#quickstart-flax-and-jax-in-transformers)
- [Flax design philosophy in 🤗 Transformers](#flax-design-philosophy-in-transformers)
- [How to use flax models & scripts](#how-to-use-flax-models-and-example-scripts)
- [Talks](#talks)
- [How to use the 🤗 Hub for training](#how-to-use-the-hub-for-collaboration)
- [How to setup TPU VM](#how-to-setup-tpu-vm)
- [How to build a demo](#how-to-build-a-demo)
- [Using the Hugging Face Widgets](#using-the-hugging-face-widgets)
- [Using a Streamlit demo](#using-a-streamlit-demo)
- [Using a Gradio demo](#using-a-gradio-demo)
- [Project evaluation](#project-evaluation)
- [General Tips & Tricks](#general-tips-and-tricks)
- [FAQ](#faq)
## Organization
Participants can propose ideas for an interesting NLP and/or CV project. Teams of 3 to 5 will then be formed around the most promising and interesting projects. Make sure to read through the [Projects](#projects) section on how to propose projects, comment on other participants' project ideas, and create a team.
To help each team successfully finish their project, we have organized talks by leading scientists and engineers from Google, Hugging Face, and the open-source NLP & CV community. The talks will take place before the community week from June 30th to July 2nd. Make sure to attend the talks to get the most out of your participation! Check out the [Talks](#talks) section to get an overview of the talks, including the speaker and the time of the talk.
Each team is then given **free access to a TPUv3-8 VM** from July 7th to July 14th. In addition, we will provide training examples in JAX/Flax for a variety of NLP and Vision models to kick-start your project. During the week, we'll make sure to answer any questions you might have about JAX/Flax and Transformers and help each team as much as possible to complete their project!
At the end of the community week, each team should submit a demo of their project. All demonstrations will be evaluated by a jury and the top-3 demos will be awarded a prize. Check out the [How to submit a demo](#how-to-submit-a-demo) section for more information and suggestions on how to submit your project.
## Important dates
- **23.06.** Official announcement of the community week. Make sure to sign-up in [this google form](https://forms.gle/tVGPhjKXyEsSgUcs8).
- **23.06. - 30.06.** Participants will be added to an internal Slack channel. Project ideas can be proposed here and groups of 3-5 are formed. Read this document for more information.
- **30.06.** Release of all relevant training scripts in JAX/Flax as well as other documents on how to set up a TPU, how to use the training scripts, how to submit a demo, tips & tricks for JAX/Flax, tips & tricks for efficient use of the hub.
- **30.06. - 2.07.** Talks about JAX/Flax, TPU, Transformers, Computer Vision & NLP will be held.
- **7.07.** Start of the community week! Access to TPUv3-8 will be given to each team.
- **7.07. - 14.07.** The Hugging Face & JAX/Flax & Cloud team will be available for any questions, problems the teams might run into.
- **15.07.** Access to TPU is deactivated and community week officially ends.
- **16.07.** Deadline for each team to submit a demo.
## Communication
All important communication will take place in an internal Slack channel, called `#flax-jax-community-week`.
Important announcements of the Hugging Face, Flax/JAX, and Google Cloud team will be posted there.
Such announcements include general information about the community week (Dates, Rules, ...), release of relevant training scripts (Flax/JAX example scripts for NLP and Vision), release of other important documents (How to access the TPU), etc.
The Slack channel will also be the central place for participants to post about their results, share their learning experiences, ask questions, etc.
For issues with Flax/JAX, Transformers, Datasets or for questions that are specific to your project we would be **very happy** if you could use the following public repositories and forums:
- Flax: [Issues](https://github.com/google/flax/issues), [Questions](https://github.com/google/flax/discussions)
- JAX: [Issues](https://github.com/google/jax/issues), [Questions](https://github.com/google/jax/discussions)
- 🤗 Transformers: [Issues](https://github.com/huggingface/transformers/issues), [Questions](https://discuss.huggingface.co/c/transformers/9)
- 🤗 Datasets: [Issues](https://github.com/huggingface/datasets/issues), [Questions](https://discuss.huggingface.co/c/datasets/10)
- Project specific questions: [Forum](https://discuss.huggingface.co/c/flax-jax-projects/22)
- TPU related questions: [TODO]()
Please do **not** post the complete issue/project-specific question in the Slack channel, but instead a link to your issue/question that we will try to answer as soon as possible.
This way, we make sure that the everybody in the community can benefit from your questions - even after the community week - and that the same question is not answered twice.
To be invited to the Slack channel, please make sure you have signed up [on the Google form](https://forms.gle/tVGPhjKXyEsSgUcs8).
**Note**: If you have signed up on the google form, but you are not in the Slack channel, please leave a message on [(TODO) the official forum announcement]( ) and ping `@Suzana` and `@patrickvonplaten`.
## Projects
During the first week after the community week announcement, **23.06. - 30.06.**, teams will be formed around the most promising and interesting project ideas. Each team can consist of 2 to 10 participants. Projects can be accessed [here](https://discuss.huggingface.co/c/flax-jax-projects/22).
All officially defined projects can be seen [here](https://docs.google.com/spreadsheets/d/1GpHebL7qrwJOc9olTpIPgjf8vOS0jNb6zR_B8x_Jtik/edit?usp=sharing).
### How to propose a project
Some default project ideas are given by the organizers. **However, we strongly encourage participants to submit their own project ideas!**
Check out the [HOW_TO_PROPOSE_PROJECT.md](https://github.com/huggingface/transformers/tree/main/examples/research_projects/jax-projects/HOW_TO_PROPOSE_PROJECT.md) for more information on how to propose a new project.
### How to form a team around a project
You can check out all existing projects ideas on the forum under [Flax/JAX projects category](https://discuss.huggingface.co/c/flax-jax-projects/22).
Make sure to quickly check out each project idea and leave a ❤️ if you like an idea.
Feel free to leave comments, suggestions for improvement, or questions about more details directly on the discussion thread.
If you have found the project that you ❤️ the most, leave a message "I would like to join this project" on the discussion thread.
We strongly advise you to also shortly state who you are, which time zone you are in and why you would like to work on this project, how you can contribute to the project and what your vision is for the project.
For projects that see a lot of interest and for which enough participants have expressed interest in joining, an official team will be created by the organizers.
One of the organizers (`@Suzana`, `@valhalla`, `@osanseviero`, `@patrickvonplaten`) will leave a message "For this project the team: `<team_name>`, `<team_members>` , is officially created" on the thread and note down the teams on [this google sheet](https://docs.google.com/spreadsheets/d/1GpHebL7qrwJOc9olTpIPgjf8vOS0jNb6zR_B8x_Jtik/edit?usp=sharing).
Once created, the team can start refining their project:
- What is the goal of the project? *E.g.*, Present a language model that writes poetry in Russian.
- What model will we use? *E.g.*, FlaxGPT2
- What data will we use? *E.g.* Russian dataset of OSCAR & publicly available book on poetry
- Should we use a pre-trained model or train a model from scratch? E.g. Train a model from scratch
- What training scripts do we need? *E.g.* `transformers/examples/flax/run_clm_flax.py` can be used
- What kind of demo would we like to present? E.g. Text-generation API of the 🤗 Hub in combination with a Streamlit demo that lets the user generate a poem of a given length
- How will the work be divided? *E.g.* Team member 1 works on data preprocessing, Team member 2 works on adapting the Flax script, ...
We highly recommend that each team discusses all relevant ideas for their project directly on the forum thread.
This way valuable learning experiences are shared and accessible by the whole community in the future.
Additionally, the organizers, other participants, or anybody in the community really can read through your discussions and leave comments/tips for improvement. Obviously, you can also create private chats, ... to discuss more sensitive topics, etc.
**Important**:
- For project ideas that see a lot of interest, we are more than happy to create more than one team.
- Participants are welcome to join multiple teams, even though we encourage them to only work on a single project.
- Under special circumstances, participants can change/create new teams. Please note that we would like to keep this the exception. If however, you would like to change/leave existing teams, please leave a post on the project's thread where you ping the corresponding organizer that created the group.
- It is often easy to propose/join a project that is done in your native language. Feel free to reach out to existing [language-specific groups](https://discuss.huggingface.co/c/languages-at-hugging-face/15) to look for community members that might be interested in joining your project.
## Tips on how to organize the project
This section gives you some tips on how to most efficiently & effectively
work as a team to achieve your goal. It is by no means a strict recipe to follow,
but rather a collection of tips from the 🤗 team.
Once your team is defined, you can start working on the project as soon as possible.
### Communication
At first, it is always useful to get to know each other and to set up a means of communication.
While we recommend that all technical aspects of work can be discussed directly on the [forum](https://discuss.huggingface.co/c/flax-jax-projects/22) under your project thread,
it can be very helpful to have a more direct way of communicating, *e.g.* in a channel.
For this we have created a discord that you can access [here](https://discord.com/channels/858019234139602994/858019234139602997).
This discord will not be managed by anybody and is just there so that you can communicate more effectively with your team members.
Feel free to create a new channel for you and your team where you can discuss everything. If you and your team have already set up other ways of communicating, it is absolutely not required to make use of the discord. However, we do recommend each team to set up some kind
of channel or group for quick discussions.
### Project definition
In the very beginning, you should make sure your project is well-defined and that
everybody in the team understands the goal of the project and the work that needs to be
done in order to achieve the goal. A well-defined project:
- has defined the task on which the model will be trained
- has defined the model that will be trained
- has defined the datasets that will be used for training
- has defined the type of training scripts that need to be written
- has defined the desired outcome of the project
- has defined the workflows
By "has defined" we don't meant that the corresponding code already has to be written and ready
to be used, but that everybody in team is on the same page on what type of model, data and training script should be used.
To give an example, a well-defined project would be the following:
- task: summarization
- model: [t5-small](https://huggingface.co/t5-small)
- dataset: [CNN/Daily mail](https://huggingface.co/datasets/cnn_dailymail)
- training script: [run_summarization_flax.py](https://github.com/huggingface/transformers/blob/main/examples/flax/summarization/run_summarization_flax.py)
- outcome: t5 model that can summarize news
- work flow: adapt `run_summarization_flax.py` to work with `t5-small`.
This example is a very easy and not the most interesting project since a `t5-small`
summarization model exists already for CNN/Daily mail and pretty much no code has to be
written.
A well-defined project does not need to have the dataset be part of
the `datasets` library and the training script already be pre-written, however it should
be clear how the desired dataset can be accessed and how the training script can be
written.
It is also important to have a clear plan regarding the workflow. Usually, the
data processing is done in a first step. Once the data is in a format that the model can
work with, the training script can be written, etc. These steps should be more detailed
once the team has a clearly defined project. It can be helpful to set deadlines for each step.
### Workload division
To effectively work as a team, it is crucial to divide the workload among everybody.
Some team members will be more motivated and experienced than others and
some team members simply want to participate to learn more and cannot contribute that
much to the team. This is totally fine! One cannot expect everybody in the team to have the same level of experience and time/motivation during the community week.
As a conclusion, being honest about one's expected involvement is crucial so that
the workload can be divided accordingly. If someone doesn't think her/his tasks are feasible - let
the team know early on so that someone else can take care of it!
It is recommended that the motivated and experienced team members take the lead in dividing the work and are ready to take over the tasks of another team member if necessary.
The workload can often be divided according to:
- data preprocessing (load the data and preprocess data in the correct format)
- data tokenization / data collator (process data samples into tokens or images)
- model configuration (writing the code that defines the model)
- model forward pass (make sure input / output work correctly)
- loss function (define the loss function)
- putting the pieces together in a training script
Many of the steps above require other steps to be finished, so it often makes sense
to use dummy data in the expected format to start, *e.g.*, with the model forward pass
before the data preprocessing is done.
### Expectations
It is also very important to stay realistic with the scope of your project. Each team
has access to a TPUv3-8 for only *ca.* 10 days, so it's important to keep the scope of
the project reasonable. While we do want each team to work on interesting projects, each
team should make sure that the project goals can be achieved within the provided compute
time on TPU. For instance, pretraining a 11 billion parameters T5 model is not really a realistic
task with just 10 days of TPUv3-8 compute.
Also, it might be difficult to finish a project where the whole modeling, dataset and training code has to be written from scratch.
Having defined your project, feel free to reach out on Slack or the forum for feedback from the organizers. We can surely give you our opinion on whether the project is feasible and what can be done to improve it.
the project is feasible.
### Other tips
Here is a collection of some more tips:
- We strongly recommend to work as publicly and collaboratively as possible during the week so that other teams
and the organizers can best help you. This includes publishing important discussions on
the forum and making use of the [🤗 hub](http://huggingface.co/) to have a version
control for your models and training logs.
- When debugging, it is important that the debugging cycle is kept as short as possible to
be able to effectively debug. *E.g.* if there is a problem with your training script,
you should run it with just a couple of hundreds of examples and not the whole dataset script. This can be done by either making use of [datasets streaming](https://huggingface.co/docs/datasets/master/dataset_streaming?highlight=streaming) or by selecting just the first
X number of data samples after loading:
```python
datasets["train"] = datasets["train"].select(range(1000))
```
- Ask for help. If you are stuck, use the public Slack channel or the [forum](https://discuss.huggingface.co/c/flax-jax-projects/22) to ask for help.
## How to install relevant libraries
In the following we will explain how to install all relevant libraries on your local computer and on TPU VM.
It is recommended to install all relevant libraries both on your local machine
and on the TPU virtual machine. This way, quick prototyping and testing can be done on
your local machine and the actual training can be done on the TPU VM.
### Local computer
The following libraries are required to train a JAX/Flax model with 🤗 Transformers and 🤗 Datasets:
- [JAX](https://github.com/google/jax/)
- [Flax](https://github.com/google/flax)
- [Optax](https://github.com/deepmind/optax)
- [Transformers](https://github.com/huggingface/transformers)
- [Datasets](https://github.com/huggingface/datasets)
You should install the above libraries in a [virtual environment](https://docs.python.org/3/library/venv.html).
If you're unfamiliar with Python virtual environments, check out the [user guide](https://packaging.python.org/guides/installing-using-pip-and-virtual-environments/). Create a virtual environment with the version of Python you're going
to use and activate it.
You should be able to run the command:
```bash
python3 -m venv <your-venv-name>
```
You can activate your venv by running
```bash
source ~/<your-venv-name>/bin/activate
```
We strongly recommend to make use of the provided JAX/Flax examples scripts in [transformers/examples/flax](https://github.com/huggingface/transformers/tree/main/examples/flax) even if you want to train a JAX/Flax model of another github repository that is not integrated into 🤗 Transformers.
In all likelihood, you will need to adapt one of the example scripts, so we recommend forking and cloning the 🤗 Transformers repository as follows.
Doing so will allow you to share your fork of the Transformers library with your team members so that the team effectively works on the same code base. It will also automatically install the newest versions of `flax`, `jax` and `optax`.
1. Fork the [repository](https://github.com/huggingface/transformers) by
clicking on the 'Fork' button on the repository's page. This creates a copy of the code
under your GitHub user account.
2. Clone your fork to your local disk, and add the base repository as a remote:
```bash
$ git clone https://github.com/<your Github handle>/transformers.git
$ cd transformers
$ git remote add upstream https://github.com/huggingface/transformers.git
```
3. Create a new branch to hold your development changes. This is especially useful to share code changes with your team:
```bash
$ git checkout -b a-descriptive-name-for-my-project
```
4. Set up a flax environment by running the following command in a virtual environment:
```bash
$ pip install -e ".[flax]"
```
(If transformers was already installed in the virtual environment, remove
it with `pip uninstall transformers` before reinstalling it in editable
mode with the `-e` flag.)
If you have already cloned that repo, you might need to `git pull` to get the most recent changes in the `datasets`
library.
Running this command will automatically install `flax`, `jax` and `optax`.
Next, you should also install the 🤗 Datasets library. We strongly recommend installing the
library from source to profit from the most current additions during the community week.
Simply run the following steps:
```
$ cd ~/
$ git clone https://github.com/huggingface/datasets.git
$ cd datasets
$ pip install -e ".[streaming]"
```
If you plan on contributing a specific dataset during
the community week, please fork the datasets repository and follow the instructions
[here](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-create-a-pull-request).
To verify that all libraries are correctly installed, you can run the following command.
It assumes that both `transformers` and `datasets` were installed from main - otherwise
datasets streaming will not work correctly.
```python
from transformers import FlaxRobertaModel, RobertaTokenizerFast
from datasets import load_dataset
import jax
dataset = load_dataset('oscar', "unshuffled_deduplicated_en", split='train', streaming=True)
dummy_input = next(iter(dataset))["text"]
tokenizer = RobertaTokenizerFast.from_pretrained("roberta-base")
input_ids = tokenizer(dummy_input, return_tensors="np").input_ids[:, :10]
model = FlaxRobertaModel.from_pretrained("julien-c/dummy-unknown")
# run a forward pass, should return an object `FlaxBaseModelOutputWithPooling`
model(input_ids)
```
### TPU VM
**VERY IMPORTANT** - Only one process can access the TPU cores at a time. This means that if multiple team members
are trying to connect to the TPU cores errors, such as:
```
libtpu.so already in used by another process. Not attempting to load libtpu.so in this process.
```
are thrown. As a conclusion, we recommend every team member to create her/his own virtual environment, but only one
person should run the heavy training processes. Also, please take turns when setting up the TPUv3-8 so that everybody
can verify that JAX is correctly installed.
The following libraries are required to train a JAX/Flax model with 🤗 Transformers and 🤗 Datasets on TPU VM:
- [JAX](https://github.com/google/jax/)
- [Flax](https://github.com/google/flax)
- [Optax](https://github.com/deepmind/optax)
- [Transformers](https://github.com/huggingface/transformers)
- [Datasets](https://github.com/huggingface/datasets)
You should install the above libraries in a [virtual environment](https://docs.python.org/3/library/venv.html).
If you're unfamiliar with Python virtual environments, check out the [user guide](https://packaging.python.org/guides/installing-using-pip-and-virtual-environments/). Create a virtual environment with the version of Python you're going
to use and activate it.
You should be able to run the command:
```bash
python3 -m venv <your-venv-name>
```
If this doesn't work, you first might to have install `python3-venv`. You can do this as follows:
```bash
sudo apt-get install python3-venv
```
You can activate your venv by running
```bash
source ~/<your-venv-name>/bin/activate
```
Next you should install JAX's TPU version on TPU by running the following command:
```
$ pip install requests
```
and then:
```
$ pip install "jax[tpu]>=0.2.16" -f https://storage.googleapis.com/jax-releases/libtpu_releases.html
```
**Note**: Running this command might actually throw an error, such as:
```
Building wheel for jax (setup.py) ... error
ERROR: Command errored out with exit status 1:
command: /home/patrick/patrick/bin/python3 -u -c 'import sys, setuptools, tokenize; sys.argv[0] = '"'"'/tmp/pip-install-lwseckn1/jax/setup.py'"'"'; __file__='"'"'/tmp/pip-install-lwseckn1/jax/setup.py'"'"';f=getattr(tokenize, '"'"'open'"'"', open)(__file__);code=f.read().replace('"'"'\r\n'"'"', '"'"'\n'"'"');f.close();exec(compile(code, __file__, '"'"'exec'"'"'))' bdist_wheel -d /tmp/pip-wheel-pydotzlo
cwd: /tmp/pip-install-lwseckn1/jax/
Complete output (6 lines):
usage: setup.py [global_opts] cmd1 [cmd1_opts] [cmd2 [cmd2_opts] ...]
or: setup.py --help [cmd1 cmd2 ...]
or: setup.py --help-commands
or: setup.py cmd --help
error: invalid command 'bdist_wheel'
----------------------------------------
ERROR: Failed building wheel for jax
```
Jax should have been installed correctly nevertheless.
To verify that JAX was correctly installed, you can run the following command:
```python
import jax
jax.device_count()
```
This should display the number of TPU cores, which should be 8 on a TPUv3-8 VM.
We strongly recommend to make use of the provided JAX/Flax examples scripts in [transformers/examples/flax](https://github.com/huggingface/transformers/tree/main/examples/flax) even if you want to train a JAX/Flax model of another github repository that is not integrated into 🤗 Transformers.
In all likelihood, you will need to adapt one of the example scripts, so we recommend forking and cloning the 🤗 Transformers repository as follows.
Doing so will allow you to share your fork of the Transformers library with your team members so that the team effectively works on the same code base. It will also automatically install the newest versions of `flax`, `jax` and `optax`.
1. Fork the [repository](https://github.com/huggingface/transformers) by
clicking on the 'Fork' button on the repository's page. This creates a copy of the code
under your GitHub user account.
2. Clone your fork to your local disk, and add the base repository as a remote:
```bash
$ git clone https://github.com/<your Github handle>/transformers.git
$ cd transformers
$ git remote add upstream https://github.com/huggingface/transformers.git
```
3. Create a new branch to hold your development changes. This is especially useful to share code changes with your team:
```bash
$ git checkout -b a-descriptive-name-for-my-project
```
4. Set up a flax environment by running the following command in a virtual environment:
```bash
$ pip install -e ".[flax]"
```
(If transformers was already installed in the virtual environment, remove
it with `pip uninstall transformers` before reinstalling it in editable
mode with the `-e` flag.)
If you have already cloned that repo, you might need to `git pull` to get the most recent changes in the `datasets`
library.
Running this command will automatically install `flax`, `jax` and `optax`.
Next, you should also install the 🤗 Datasets library. We strongly recommend installing the
library from source to profit from the most current additions during the community week.
Simply run the following steps:
```
$ cd ~/
$ git clone https://github.com/huggingface/datasets.git
$ cd datasets
$ pip install -e ".[streaming]"
```
If you plan on contributing a specific dataset during
the community week, please fork the datasets repository and follow the instructions
[here](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-create-a-pull-request).
To verify that all libraries are correctly installed, you can run the following command.
It assumes that both `transformers` and `datasets` were installed from main - otherwise
datasets streaming will not work correctly.
```python
from transformers import FlaxRobertaModel, RobertaTokenizerFast
from datasets import load_dataset
import jax
dataset = load_dataset('oscar', "unshuffled_deduplicated_en", split='train', streaming=True)
dummy_input = next(iter(dataset))["text"]
tokenizer = RobertaTokenizerFast.from_pretrained("roberta-base")
input_ids = tokenizer(dummy_input, return_tensors="np").input_ids[:, :10]
model = FlaxRobertaModel.from_pretrained("julien-c/dummy-unknown")
# run a forward pass, should return an object `FlaxBaseModelOutputWithPooling`
model(input_ids)
```
## Quickstart flax and jax
[JAX](https://jax.readthedocs.io/en/latest/index.html) is Autograd and XLA, brought together for high-performance numerical computing and machine learning research. It provides composable transformations of Python+NumPy programs: differentiate, vectorize, parallelize, Just-In-Time compile to GPU/TPU, and more. A great place for getting started with JAX is the [JAX 101 Tutorial](https://jax.readthedocs.io/en/latest/jax-101/index.html).
[Flax](https://flax.readthedocs.io/en/latest/index.html) is a high-performance neural network library designed for flexibility built on top of JAX. It aims to provide users with full control of their training code and is carefully designed to work well with JAX transformations such as `grad` and `pmap` (see the [Flax philosophy](https://flax.readthedocs.io/en/latest/philosophy.html)). For an introduction to Flax see the [Flax Basics Colab](https://flax.readthedocs.io/en/latest/notebooks/flax_basics.html) or the list of curated [Flax examples](https://flax.readthedocs.io/en/latest/examples.html).
## Quickstart flax and jax in transformers
Currently, we support the following models in Flax.
Note that some models are about to be merged to `main` and will
be available in a couple of days.
- [BART](https://github.com/huggingface/transformers/blob/main/src/transformers/models/bart/modeling_flax_bart.py)
- [BERT](https://github.com/huggingface/transformers/blob/main/src/transformers/models/bert/modeling_flax_bert.py)
- [BigBird](https://github.com/huggingface/transformers/blob/main/src/transformers/models/big_bird/modeling_flax_big_bird.py)
- [CLIP](https://github.com/huggingface/transformers/blob/main/src/transformers/models/clip/modeling_flax_clip.py)
- [ELECTRA](https://github.com/huggingface/transformers/blob/main/src/transformers/models/electra/modeling_flax_electra.py)
- [GPT2](https://github.com/huggingface/transformers/blob/main/src/transformers/models/gpt2/modeling_flax_gpt2.py)
- [(TODO) MBART](https://github.com/huggingface/transformers/blob/main/src/transformers/models/mbart/modeling_flax_mbart.py)
- [RoBERTa](https://github.com/huggingface/transformers/blob/main/src/transformers/models/roberta/modeling_flax_roberta.py)
- [T5](https://github.com/huggingface/transformers/blob/main/src/transformers/models/t5/modeling_flax_t5.py)
- [ViT](https://github.com/huggingface/transformers/blob/main/src/transformers/models/vit/modeling_flax_vit.py)
- [Wav2Vec2](https://github.com/huggingface/transformers/blob/main/src/transformers/models/wav2vec2/modeling_flax_wav2vec2.py)
You can find all available training scripts for JAX/Flax under the
official [flax example folder](https://github.com/huggingface/transformers/tree/main/examples/flax). Note that a couple of training scripts will be released in the following week.
- [Causal language modeling (GPT2)](https://github.com/huggingface/transformers/blob/main/examples/flax/language-modeling/run_clm_flax.py)
- [Masked language modeling (BERT, RoBERTa, ELECTRA, BigBird)](https://github.com/huggingface/transformers/blob/main/examples/flax/language-modeling/run_mlm_flax.py)
- [Text classification (BERT, RoBERTa, ELECTRA, BigBird)](https://github.com/huggingface/transformers/blob/main/examples/flax/text-classification/run_flax_glue.py)
- [Summarization / Seq2Seq (BART, MBART, T5)](https://github.com/huggingface/transformers/blob/main/examples/flax/summarization/run_summarization_flax.py)
- [Masked Seq2Seq pret-training (T5)](https://github.com/huggingface/transformers/blob/main/examples/flax/language-modeling/run_t5_mlm_flax.py)
- [Contrastive Loss pretraining for Wav2Vec2](https://github.com/huggingface/transformers/blob/main/examples/research_projects/jax-projects/wav2vec2)
- [Fine-tuning long-range QA for BigBird](https://github.com/huggingface/transformers/blob/main/examples/research_projects/jax-projects/big_bird)
- [(TODO) Image classification (ViT)]( )
- [(TODO) CLIP pretraining, fine-tuning (CLIP)]( )
### **Flax design philosophy in Transformers**
This section will explain how Flax models are implemented in Transformers and how the design differs from PyTorch.
Let's first go over the difference between Flax and PyTorch.
In JAX, most transformations (notably `jax.jit`) require functions that are transformed to be stateless so that they have no side effects. This is because any such side-effects will only be executed once when the transformed function is run during compilation and all subsequent calls of the compiled function would re-use the same side-effects of the compiled run instead of the "actual" side-effects (see [Stateful Computations in JAX](https://jax.readthedocs.io/en/latest/jax-101/07-state.html)). As a consequence, Flax models, which are designed to work well with JAX transformations, are stateless. This means that when running a model in inference, both the inputs and the model weights are passed to the forward pass. In contrast, PyTorch model are very much stateful with the weights being stored within the model instance and the user just passing the inputs to the forward pass.
Let's illustrate the difference between stateful models in PyTorch and stateless models in Flax.
For simplicity, let's assume the language model consists simply of a single attention layer [`key_proj`, `value_proj`, `query_proj`] and a linear layer `logits_proj` to project the transformed word embeddings to the output logit vectors.
#### **Stateful models in PyTorch**
In PyTorch, the weights matrices would be stored as `torch.nn.Linear` objects alongside the model's config inside the model class `ModelPyTorch`:
```python
class ModelPyTorch:
def __init__(self, config):
self.config = config
self.key_proj = torch.nn.Linear(config)
self.value_proj = torch.nn.Linear(config)
self.query_proj = torch.nn.Linear(config)
self.logits_proj = torch.nn.Linear(config)
```
Instantiating an object `model_pytorch` of the class `ModelPyTorch` would actually allocate memory for the model weights and attach them to the attributes `self.key_proj`, `self.value_proj`, `self.query_proj`, and `self.logits.proj`. We could access the weights via:
```
key_projection_matrix = model_pytorch.key_proj.weight.data
```
Visually, we would represent an object of `model_pytorch` therefore as follows:
Executing a forward pass then simply corresponds to passing the `input_ids` to the object `model_pytorch`:
```python
sequences = model_pytorch(input_ids)
```
In a more abstract way, this can be represented as passing the word embeddings to the model function to get the output logits:
This design is called **stateful** because the output logits, the `sequences`, can change even if the word embeddings, the `input_ids`, stay the same. Hence, the function's output does not only depend on its inputs, but also on its **state**, `[self.key_proj, self.value_proj, self.query_proj, self.logits_proj]`, which makes `model_pytorch` stateful.
#### **Stateless models in Flax/JAX**
Now, let's see how the mathematically equivalent model would be written in JAX/Flax. The model class `ModelFlax` would define the self-attention and logits projection weights as [**`flax.linen.Dense`**](https://flax.readthedocs.io/en/latest/_autosummary/flax.linen.Dense.html#flax.linen.Dense) objects:
```python
class ModelFlax:
def __init__(self, config):
self.config = config
self.key_proj = flax.linen.Dense(config)
self.value_proj = flax.linen.Dense(config)
self.query_proj = flax.linen.Dense(config)
self.logits_proj = flax.linen.Dense(config)
```
At first glance the linear layer class `flax.linen.Dense` looks very similar to PyTorch's `torch.nn.Linear` class. However, instantiating an object `model_flax` only defines the linear transformation functions and does **not** allocate memory to store the linear transformation weights. In a way, the attribute `self.key_proj` tell the instantiated object `model_flax` to perform a linear transformation on some input and force it to expect a weight, called `key_proj`, as an input.
This time we would illustrate the object `model_flax` without the weight matrices:
Accordingly, the forward pass requires both `input_ids` as well as a dictionary consisting of the model's weights (called `state` here) to compute the `sequences`:
To get the initial `state` we need to explicitly do a forward pass by passing a dummy input:
```python
state = model_flax.init(rng, dummy_input_ids)
```
and then we can do the forward pass.
```python
sequences = model_flax.apply(state, input_ids)
```
Visually, the forward pass would now be represented as passing all tensors required for the computation to the model's object:
This design is called **stateless** because the output logits, the `sequences`, **cannot** change if the word embeddings, the `input_ids`, stay the same. Hence, the function's output only depends on its inputs, being the `input_ids` and the `state` dictionary consisting of the weights **state**, `[key_proj, value_proj, query_proj, logits_proj]`.
Another term which is often used to describe the design difference between Flax/JAX and PyTorch is **immutable** vs **mutable**. A instantiated Flax model, `model_flax`, is **immutable** as a logical consequence of `model_flax`'s output being fully defined by its input: If calling `model_flax` could mutate `model_flax`, then calling `model_flax` twice with the same inputs could lead to different results which would violate the "*statelessness*" of Flax models.
#### **Flax models in Transformers**
Now let us see how this is handled in `Transformers.` If you have used a Flax model in Transformers already, you might wonder how come you don't always have to pass the parameters to the function of the forward pass. This is because the `FlaxPreTrainedModel` class abstracts it away.
It is designed this way so that the Flax models in Transformers will have a similar API to PyTorch and Tensorflow models.
The `FlaxPreTrainedModel` is an abstract class that holds a Flax module, handles weights initialization, and provides a simple interface for downloading and loading pre-trained weights i.e. the `save_pretrained` and `from_pretrained` methods. Each Flax model then defines its own subclass of `FlaxPreTrainedModel`; *e.g.* the BERT model has `FlaxBertPreTrainedModel`. Each such class provides two important methods, `init_weights` and `__call__`. Let's see what each of those methods do:
- The `init_weights` method takes the expected input shape and a [`PRNGKey`](https://jax.readthedocs.io/en/latest/_autosummary/jax.random.PRNGKey.html) (and any other arguments that are required to get initial weights) and calls `module.init` by passing it a random example to get the initial weights with the given `dtype` (for ex. `fp32` or `bf16` etc). This method is called when we create an instance of the model class, so the weights are already initialized when you create a model i.e., when you do
model = FlaxBertModel(config)
- The `__call__` method defines forward pass. It takes all necessary model inputs and parameters (and any other arguments required for the forward pass). The parameters are optional; when no parameters are passed, it uses the previously initialized or loaded parameters which can be accessed using `model.params`. It then calls the `module.apply` method, passing it the parameters and inputs to do the actual forward pass. So we can do a forward pass using
output = model(inputs, params=params)
Let's look at an example to see how this works. We will write a simple two-layer MLP model.
First, write a Flax module that will declare the layers and computation.
```python
import flax.linen as nn
import jax.numpy as jnp
class MLPModule(nn.Module):
config: MLPConfig
dtype: jnp.dtype = jnp.float32
def setup(self):
self.dense1 = nn.Dense(self.config.hidden_dim, dtype=self.dtype)
self.dense2 = nn.Desne(self.config.hidden_dim, dtype=self.dtype)
def __call__(self, inputs):
hidden_states = self.dense1(inputs)
hidden_states = nn.relu(hidden_states)
hidden_states = self.dense2(hidden_states)
return hidden_states
```
Now let's define the `FlaxPreTrainedModel` model class.
```python
from transformers.modeling_flax_utils import FlaxPreTrainedModel
class FlaxMLPPreTrainedModel(FlaxPreTrainedModel):
config_class = MLPConfig
base_model_prefix = "model"
module_class: nn.Module = None
def __init__(self, config: BertConfig, input_shape: Tuple = (1, 8), seed: int = 0, dtype: jnp.dtype = jnp.float32, **kwargs):
# initialize the flax module
module = self.module_class(config=config, dtype=dtype, **kwargs)
super().__init__(config, module, input_shape=input_shape, seed=seed, dtype=dtype)
def init_weights(self, rng, input_shape):
# init input tensors
inputs = jnp.zeros(input_shape, dtype="i4")
params_rng, dropout_rng = jax.random.split(rng)
rngs = {"params": params_rng, "dropout": dropout_rng}
params = self.module.init(rngs, inputs)["params"]
return params
def __call__(self, inputs, params: dict = None):
params = {"params": params or self.params}
outputs = self.module.apply(params, jnp.array(inputs))
return outputs
```
Now we can define our model class as follows.
```python
class FlaxMLPModel(FlaxMLPPreTrainedModel):
module_class = FlaxMLPModule
```
Now the `FlaxMLPModel` will have a similar interface as PyTorch or Tensorflow models and allows us to attach loaded or randomly initialized weights to the model instance.
So the important point to remember is that the `model` is not an instance of `nn.Module`; it's an abstract class, like a container that holds a Flax module, its parameters and provides convenient methods for initialization and forward pass. The key take-away here is that an instance of `FlaxMLPModel` is very much stateful now since it holds all the model parameters, whereas the underlying Flax module `FlaxMLPModule` is still stateless. Now to make `FlaxMLPModel` fully compliant with JAX transformations, it is always possible to pass the parameters to `FlaxMLPModel` as well to make it stateless and easier to work with during training. Feel free to take a look at the code to see how exactly this is implemented for ex. [`modeling_flax_bert.py`](https://github.com/huggingface/transformers/blob/main/src/transformers/models/bert/modeling_flax_bert.py#L536)
Another significant difference between Flax and PyTorch models is that, we can pass the `labels` directly to PyTorch's forward pass to compute the loss, whereas Flax models never accept `labels` as an input argument. In PyTorch, gradient backpropagation is performed by simply calling `.backward()` on the computed loss which makes it very handy for the user to be able to pass the `labels`. In Flax however, gradient backpropagation cannot be done by simply calling `.backward()` on the loss output, but the loss function itself has to be transformed by `jax.grad` or `jax.value_and_grad` to return the gradients of all parameters. This transformation cannot happen under-the-hood when one passes the `labels` to Flax's forward function, so that in Flax, we simply don't allow `labels` to be passed by design and force the user to implement the loss function oneself. As a conclusion, you will see that all training-related code is decoupled from the modeling code and always defined in the training scripts themselves.
### **How to use flax models and example scripts**
#### **How to do a forward pass**
Let's first see how to load, save and do inference with Flax models. As explained in the above section, all Flax models in Transformers have similar API to PyTorch models, so we can use the familiar `from_pretrained` and `save_pretrained` methods to load and save Flax models.
Let's use the base `FlaxRobertaModel` without any heads as an example.
```python
from transformers import FlaxRobertaModel, RobertaTokenizerFast
import jax
tokenizer = RobertaTokenizerFast.from_pretrained("roberta-base")
inputs = tokenizer("JAX/Flax is amazing ", padding="max_length", max_length=128, return_tensors="np")
model = FlaxRobertaModel.from_pretrained("julien-c/dummy-unknown")
@jax.jit
def run_model(input_ids, attention_mask):
# run a forward pass, should return an object `FlaxBaseModelOutputWithPooling`
return model(input_ids, attention_mask)
outputs = run_model(**inputs)
```
We use `jax.jit` to compile the function to get maximum performance. Note that in the above example, we set `padding=max_length` to pad all examples to the same length. We do this because JAX's compiler has to recompile a function everytime its input shape changes - in a sense a compiled function is not only defined by its code but also by its input and output shape. It is usually much more effective to pad the input to be of a fixed static shape than having to recompile every the function multiple times.
#### **How to write a training loop**
Now let's see how we can write a simple training loop to train Flax models, we will use `FlaxGPT2ForCausalLM` as an example.
A training loop for Flax models typically consists of
- A loss function that takes the parameters and inputs, runs the forward pass and returns the loss.
- We then transform the loss function using `jax.grad` or `jax.value_and_grad` so that we get the gradients of all parameters.
- An optimizer to update the paramteres using the gradients returned by the transformed loss function.
- A train step function which combines the loss function and optimizer update, does the forward and backward pass and returns the updated parameters.
Lets see how that looks like in code:
First initialize our model
```python
import jax
import jax.numpy as jnp
from transformers import FlaxGPT2ForCausalLM
model = FlaxGPT2ForCausalLM(config)
```
As explained above we don't compute the loss inside the model, but rather in the task-specific training script.
For demonstration purposes, we write a pseudo training script for causal language modeling in the following.
```python
from flax.training.common_utils import onehot
def cross_entropy(logits, labels):
return -jnp.sum(labels * jax.nn.log_softmax(logits, axis=-1), axis=-1)
# define a function which will run the forward pass return loss
def compute_loss(params, input_ids, labels):
logits = model(input_ids, params=params, train=True)
num_classes = logits.shape[-1]
loss = cross_entropy(logits, onehot(labels, num_classes)).mean()
return loss
```
Now we transform the loss function with `jax.value_and_grad`.
```python
# transform the loss function to get the gradients
grad_fn = jax.value_and_grad(compute_loss)
```
We use the [optax](https://github.com/deepmind/optax) library to Initialize the optimizer.
```python
import optax
params = model.params
tx = optax.sgd(learning_rate=3e-3)
opt_state = tx.init(params)
```
Now we define a single training step which will do a forward and a backward pass.
```python
def _train_step(params, opt_state, input_ids, labels)
# do the forward pass and get the loss and gradients
loss, grads = grad_fn(params, input_ids, labels)
# use the gradients to update parameters
updates, opt_state = tx.update(grads, opt_state)
updated_params = optax.apply_updates(params, updates)
return updates_params, opt_state, loss
train_step = jax.jit(_train_step)
```
Finally, let's run our training loop.
```python
# train loop
for i in range(10):
params, opt_state, loss = train_step(params, opt_state, input_ids, labels)
```
Note how we always pass the `params` and `opt_state` to the `train_step` which then returns the updated `params` and `opt_state`. This is because of the staless nature of JAX/Flax models, all the state
like parameters, optimizer state is kept external.
We can now save the model with the trained parameters using
```python
model.save_pretrained("awesome-flax-model", params=params)
```
Note that, as JAX is backed by the [XLA](https://www.tensorflow.org/xla) compiler any JAX/Flax code can run on all `XLA` compliant device without code change!
That menas you could use the same training script on CPUs, GPUs, TPUs.
To know more about how to train the Flax models on different devices (GPU, multi-GPUs, TPUs) and use the example scripts, please look at the [examples README](https://github.com/huggingface/transformers/tree/main/examples/flax).
## Talks
3 days of talks around JAX / Flax, Transformers, large-scale language modeling and other great topics during our community event!
### Wednesday, June 30th
- [Watch the talks on YouTube](https://www.youtube.com/watch?v=fuAyUQcVzTY)
- [Chat history](https://docs.google.com/spreadsheets/d/1PZ5xYV2hVwlAVQSqDag65ympv5YNCSDmXyG-eWTaZ_o/edit?usp=sharing)
Speaker | Topic | Time | Video |
|-------------|---------------------------------|------------------------|------------------------|
| Skye Wanderman-Milne, Google Brain | Intro to JAX on Cloud TPUs | 6.00pm-6.45pm CEST / 9.00am-9.45am PST | [](https://www.youtube.com/watch?v=fuAyUQcVzTY) |
| Marc van Zee, Google Brain | Introduction to Flax | 6.45pm-7.30pm CEST / 9.45am-10.30am PST | [](https://youtu.be/fuAyUQcVzTY?t=2569) |
| Pablo Castro, Google Brain | Using Jax & Flax for RL with the Dopamine library | 7.30pm-8.00pm CEST / 10.30am-11.00am PST | [](https://youtu.be/fuAyUQcVzTY?t=5306) |
### Thursday, July 1st
- [Watch the talks on YouTube](https://www.youtube.com/watch?v=__eG63ZP_5g)
- [Chat history](https://docs.google.com/spreadsheets/d/1PZ5xYV2hVwlAVQSqDag65ympv5YNCSDmXyG-eWTaZ_o/edit#gid=1515796400)
Speaker | Topic | Time | Video |
|-------------|---------------------------------|------------------------|------------------------|
| Suraj Patil & Patrick von Platen, Hugging Face | How to use JAX/Flax with Transformers | 5.30pm-6.00pm CEST / 8.30am-9.00am PST | [](https://www.youtube.com/watch?v=__eG63ZP_5g) |
| Sabrina J. Mielke, Johns Hopkins University & HuggingFace | From stateful code to purified JAX: how to build your neural net framework | 6.00pm-6.30pm CEST / 9.00am-9.30am PST | [](https://youtu.be/__eG63ZP_5g?t=1576) |
| Mostafa Dehghani, Google Brain | Long Range Arena: Benchmarking Efficient Transformers | 6.30pm-7.00pm CEST / 9.30am-10.00am PST | [](https://youtu.be/__eG63ZP_5g?t=3695) |
| Rohan Anil, Google Brain | Scalable Second Order Optimization for Deep Learning | 7.00pm-7.30pm CEST / 10.00am-10.30am PST | [](https://youtu.be/__eG63ZP_5g?t=5285) |
### Friday, July 2nd
- [Watch the talks on YouTube](https://www.youtube.com/watch?v=ZCMOPkcTu3s)
- [Chat history](https://docs.google.com/spreadsheets/d/1PZ5xYV2hVwlAVQSqDag65ympv5YNCSDmXyG-eWTaZ_o/edit#gid=1166061401)
Speaker | Topic | Time | Video |
|-------------|---------------------------------|------------------------|------------------------|
| Lucas Beyer, Google Brain | Vision Transformer | 5.00pm-5.30 CEST / 8.00am-8.30 PST | [](https://www.youtube.com/watch?v=ZCMOPkcTu3s) |
| Ben Wang, EleutherAI | Multihost Training in Mesh Transformer JAX | 5.30pm-6.00 CEST / 8.30am-9.00 PST | [](https://youtu.be/ZCMOPkcTu3s?t=1803) |
| Iurii Kemaev, Soňa Mokrá, Junhyuk Oh, DeepMind | DeepMind JAX Ecosystem | 6.00pm-6.30 CEST / 9.00am-9.30am PST | [](https://youtu.be/ZCMOPkcTu3s?t=3388) |
| Siddhartha Kamalakara, Joanna Yoo & João G M Araújo, Cohere | Training large scale language models | 6:30pm-7.00pm CEST / 9:30am-10.00am PST | [](https://youtu.be/ZCMOPkcTu3s?t=5095) |
### Talks & Speakers
#### Skye Wanderman-Milne, JAX developer, Google Brain
- Talk: Intro to JAX on Cloud TPUs
- Abstract: JAX is a system for high-performance machine-learning research that combines the familiarity of Python + NumPy together with the power of hardware acceleration on CPUs, GPUs, and TPUs. It offers composable function transformations for automatic differentiation, automatic batching, end-to-end compilation, and both data and model parallelism. This talk will show you how to get up and running with JAX on a Cloud TPU VM.
- Speaker info: Skye Wanderman-Milne is a software engineer working on JAX. She has previously worked on TensorFlow and Apache Impala, a high-performance distributed database.
#### Marc van Zee, Research SWE, Google Brain (Flax team)
- Talk: Introduction to Flax
- Abstract: In this talk I will provide a high-level introduction to the neural network library Flax. I will discuss the Flax philosophy, talk about the ecosystem around Flax and provide a high-level introduction to the code. I explain the Module abstraction and how to use it to train your models.
- Speaker info: Marc is at Google Research for over 4 years. First he worked on conceptual AI, developing a next generation language understanding and reasoning prototype and he authored the CFQ dataset for compositional generalization. Currently, Marc works as a research software engineer in the Flax team.
#### Pablo Castro, Staff Research Software Developer; Google Research, Brain Team
- Talk: Using Jax & Flax for RL with the Dopamine library
- Abstract: The Dopamine library was launched with TensorFlow in 2018 and we added a Jax/Flax variant of it last year. Internally, Jax's flexibility has facilitated our RL research tremendously, and we are excited to demonstrate its potential.
- Speaker info: Pablo Samuel has been at Google for over 9 years, and is currently a researcher with the Brain team, focusing on fundamental reinforcement learning, as well as machine learning and creativity. Aside from his research, Pablo Samuel is an active musician (with a channel exploring the intersection of music and computer science), and is helping increase the representation of the LatinX community in the research world.
- Dopamine repo: https://github.com/google/dopamine
- Homepage: https://psc-g.github.io/
- Twitter: https://twitter.com/pcastr
#### Suraj Patil & Patrick von Platen, Machine Learning Engineers at Hugging Face
- Talk: How to use JAX/Flax with Transformers
- Abstract: Transformers is one of the most popular open-source ML libraries and supports PyTorch, Tensorflow, and JAX/Flax. In this talk, we will explain how JAX/Flax models should be used in Transformers and compare their design in Transformers with the design of PyTorch models in Transformers. In the second part, we will give you a hands-on presentation of how a model can be trained end-to-end with the official JAX/Flax example scripts using Transformers & Datasets. Along the way, we want to give you some tips and tricks on how to best realize your project.
- Speaker info: Suraj and Patrick are part of Hugging Face’s open source team and lead the integration of JAX/Flax into Transformers.
- GitHub: https://github.com/patil-suraj & https://github.com/patrickvonplaten
#### Sabrina J. Mielke, PhD student at The Johns Hopkins University & Part-time research intern at HuggingFace
- Talk: From stateful code to purified JAX: how to build your neural net framework
- Abstract: Moving from object-oriented (and stateful) PyTorch- or TF2-code with tape-based backprop to JAX isn't easy---and while running grad() on numpy-oneliners is cool and all, you do wonder... how do I build actual big neural nets? Libraries like flax, trax, or haiku make it easy---but how could you build machinery like that yourself?
- Speaker info: Sabrina is a PhD student at the Johns Hopkins University and a part-time research intern at HuggingFace, researching open-vocabulary language models for segmentation and tokenization. She has published and co-organized workshops and shared tasks on these topics as well as on morphology and typological analysis in ACL, NAACL, EMNLP, LREC, and AAAI. You can find her reminisce for a time when formal language theory played a bigger role in NLP on Twitter at @sjmielke.
- Links: The 2020 blogpost this talk will be based on: https://sjmielke.com/jax-purify.htm, leading to our experiment Parallax and eventually Haiku
#### Mostafa Dehghani, Research Scientist, Google Brain
- Talk: Long Range Arena: Benchmarking Efficient Transformers
- Abstract: Transformers do not scale very well to long sequence lengths largely because of quadratic self-attention complexity. In the recent months, a wide spectrum of efficient, fast Transformers have been proposed to tackle this problem, more often than not claiming superior or comparable model quality to vanilla Transformer models. So, we now need a well-established consensus on how to evaluate this class of models. Moreover, inconsistent benchmarking on a wide spectrum of tasks and datasets makes it difficult to assess relative model quality amongst many models. I'll talk about a systematic and unified benchmark, LRA, specifically focused on evaluating model quality under long-context scenarios. LRA is a suite of tasks consisting of sequences ranging from 1K to 16K tokens, encompassing a wide range of data types and modalities such as text, natural, synthetic images, and mathematical expressions requiring similarity, structural, and visual-spatial reasoning. We systematically evaluate ten well-established long-range Transformer models (Reformers, Linformers, Linear Transformers, Sinkhorn Transformers, Performers, Synthesizers, Sparse Transformers, and Longformers) on LRA. LRA paves the way towards better understanding this class of efficient Transformer models, facilitates more research in this direction, and presents new challenging tasks to tackle.
- Speaker info: https://mostafadehghani.com/
#### Rohan Anil, Senior Staff Software Engineer, Google Research, Brain Team
- Talk: Scalable Second Order Optimization for Deep Learning
- Abstract: Optimization in machine learning, both theoretical and applied, is presently dominated by first-order gradient methods such as stochastic gradient descent. Second-order optimization methods, that involve second derivatives and/or second order statistics of the data, are far less prevalent despite strong theoretical properties, due to their prohibitive computation, memory and communication costs. In an attempt to bridge this gap between theoretical and practical optimization, we present a scalable implementation of a second-order preconditioned method (concretely, a variant of full-matrix Adagrad), that along with several critical algorithmic and numerical improvements, provides significant convergence and wall-clock time improvements compared to conventional first-order methods on state-of-the-art deep models. Our novel design effectively utilizes the prevalent heterogeneous hardware architecture for training deep models, consisting of a multicore CPU coupled with multiple accelerator units. We demonstrate superior performance compared to state-of-the-art on very large learning tasks such as machine translation with Transformers, language modeling with BERT, click-through rate prediction on Criteo, and image classification on ImageNet with ResNet-50.
- Speaker info: Rohan Anil is a software engineer at Google Research, Mountain View. Lately, he has been working on scalable and practical optimization techniques for efficient training of neural networks in various regimes.
- Resources:
- https://arxiv.org/abs/2002.09018
- https://arxiv.org/abs/1901.11150
- https://arxiv.org/abs/2106.06199
#### Lucas Beyer, Senior Research Engineer, Google Brain
- Talk: Vision Transformer
- Abstract: This talk will discuss the learning of general visual representations via large-scale pre-training and few-shot transfer, with a special focus on the Vision Transformer (ViT) architecture, which popularized transformers for the visual domain.
- Speaker info: Lucas Beyer is a self-taught hacker and studied engineer. He went on to do his PhD in robotic perception at RWTH Aachen and is currently on a quest to find the ultimate visual representation at Google Brain in Zürich
#### Ben Wang, Independent AI Researcher, EleutherAI
- Talk: Multihost Training in Mesh Transformer JAX
- Abstract: As models become larger, training must be scaled across multiple nodes. This talk discusses some design decisions and tradeoffs made for scaling to multiple nodes in Mesh Transformer JAX, a library for running model parallel transformers on TPU pods.
- Speaker info: Ben is an independent AI researcher who contributes to EleutherAI, an open source research collective centered around democratizing access to powerful AI models. Recently he has released GPT-J-6B, a 6 billion parameter transformer which is the most powerful autoregressive language model in terms of zero-shot performance with public weights.
- Website: https://www.eleuther.ai/
#### Iurii Kemaev, Research Engineer, Soňa Mokrá, Research Engineer, and Junhyuk Oh, Research Scientist, DeepMind
- Talk: DeepMind JAX Ecosystem
- Abstract: The DeepMind JAX Ecosystem is an effort to build a shared substrate of components to enable all aspects of AGI Research. In this talk, our researchers and engineers will give a high-level overview of our Ecosystem goals and design philosophies, using our Haiku (neural network), Optax (optimization) and RLax (reinforcement learning) libraries as examples. We will then deep dive on two examples of recent DeepMind research that have been enabled by JAX and these libraries: generative models and meta-gradient reinforcement learning.
- Speaker info:
- Iurii Kemaev is a Research Engineer at DeepMind. He has been using JAX for 2 years advancing RL research. Iurii is one of the DM JAX ecosystem leads.
- Soňa Mokrá is a Research Engineer at DeepMind. She has a background in machine translation and has been using JAX as the main ML framework for the past 6 months.
- Junhyuk Oh is a Research Scientist at DeepMind, working on reinforcement learning and meta-learning. More information is available at https://junhyuk.com/
#### Siddhartha Kamalakara, Joanna Yoo, João G M Araújo, MLE at Cohere
- Talk: Training large scale language models
- Abstract: A journey through Cohere’s experiences with training large scale language models. Join us in our exploration of pipeline and model parallelism as strategies for efficient training of large language models. We will present and motivate our recent transition to JAX+Flax as our choice of internal tech stack.
- Speaker info:
- João G M Araújo is a Brazilian college student with a passion for mathematics and a fascination for Deep Learning. João conducted research on representation learning and spent 3 months in Japan working on NeuroEvolution. João likes reading fantasy books and spending quality time with family and friends, and also runs a YouTube series on theoretical understanding of Deep Learning where researchers talk about their findings
- Joanna Yoo is one of the founding engineers at Cohere, working on scaling language models for the last year and half. Joanna loves live concerts and rock climbing!
- Siddhartha Rao Kamalakara is an MLE at Cohere and a researcher at FOR.ai with research interests at the intersection of efficient training and empirical understanding of DL.
- Website: https://cohere.ai/
## How to use the hub for collaboration
In this section, we will explain how a team can use the 🤗 hub to collaborate on a project.
The 🤗 hub allows each team to create a repository with integrated git version control that
should be used for their project.
The advantages of using a repository on the 🤗 hub are:
- easy collaboration - each team member has write access to the model repository
- integrated git version control - code scripts as well as large model files are tracked using git version control
- easy sharing - the hub allows each team to easily share their work during and after the event
- integrated tensorboard functionality - uploaded tensorboard traces are automatically displayed on an integrated tensorboard tab
We highly recommend each team to make use of the 🤗 hub during the event.
To better understand how the repository and the hub in general functions, please take a look at the documentation and the videos [here](https://huggingface.co/docs/hub).
Now let's explain in more detail how a project can be created on the hub. Having an officially defined project on [this](https://docs.google.com/spreadsheets/d/1GpHebL7qrwJOc9olTpIPgjf8vOS0jNb6zR_B8x_Jtik/edit?usp=sharing) Google Sheet you should be part of [the Flax Community organization on the hub](https://huggingface.co/flax-community). All repositories should be created under this organization so that write access can be shared and everybody can easily access other participants'
work 🤗. Note that we are giving each team member access to all repositories created under [flax-community](https://huggingface.co/flax-community), but we encourage participants to only clone and edit repositories corresponding to one's teams. If you want to help other teams, please ask them before changing files in their repository! The integrated git version control keeps track of
all changes, so in case a file was deleted by mistake, it is trivial to re-create it.
Awesome! Now, let's first go over a simple example where most of the required we'll pre-train a RoBERTa model on a low-resource language. To begin with, we create a repository
under [the Flax Community organization on the hub](https://huggingface.co/flax-community) by logging in to the hub and going to [*"Add model"*](https://huggingface.co/new). By default
the username should be displayed under "*Owner*", which we want to change to *flax-community*. Next, we give our repository a fitting name for the project - here we'll just call it
*roberta-base-als* because we'll be pretraining a RoBERTa model on the super low-resource language *Alemannic* (`als`). We make sure that the model is a public repository and create it!
It should then be displayed on [the Flax Community organization on the hub](https://huggingface.co/flax-community).
Great, now we have a project directory with integrated git version control and a public model page, which we can access under [flax-community/roberta-base-als](https://huggingface.co/flax-community/roberta-base-als). Let's create a short README so that other participants know what this model is about. You can create the README.md directly on the model page as a markdown file.
Let's now make use of the repository for training.
We assume that the 🤗 Transformers library and [git-lfs](https://git-lfs.github.com/) are correctly installed on our machine or the TPU attributed to us.
If this is not the case, please refer to the [Installation guide](#how-to-install-relevant-libraries) and the official [git-lfs](https://git-lfs.github.com/) website.
At first we should log in:
```bash
$ huggingface-cli login
```
Next we can clone the repo:
```bash
$ git clone https://huggingface.co/flax-community/roberta-base-als
```
We have now cloned the model's repository and it should be under `roberta-base-als`. As you can see,
we have all the usual git functionalities in this repo - when adding a file, we can do `git add .`, `git commit -m "add file"` and `git push`
as usual. Let's try it out by adding the model's config.
We go into the folder:
```bash
$ cd ./roberta-base-als
```
and run the following commands in a Python shell to save a config.
```python
from transformers import RobertaConfig
config = RobertaConfig.from_pretrained("roberta-base")
config.save_pretrained("./")
```
Now we've added a `config.json` file and can upload it by running
```bash
$ git add . && git commit -m "add config" && git push
```
Cool! The file is now displayed on the model page under the [files tab](https://huggingface.co/flax-community/roberta-base-als/tree/main).
We encourage you to upload all files except maybe the actual data files to the repository. This includes training scripts, model weights,
model configurations, training logs, etc...
Next, let's create a tokenizer and save it to the model dir by following the instructions of the [official Flax MLM README](https://github.com/huggingface/transformers/tree/main/examples/flax/language-modeling#train-tokenizer). We can again use a simple Python shell.
```python
from datasets import load_dataset
from tokenizers import ByteLevelBPETokenizer
# load dataset
dataset = load_dataset("oscar", "unshuffled_deduplicated_als", split="train")
# Instantiate tokenizer
tokenizer = ByteLevelBPETokenizer()
def batch_iterator(batch_size=1000):
for i in range(0, len(dataset), batch_size):
yield dataset[i: i + batch_size]["text"]
# Customized training
tokenizer.train_from_iterator(batch_iterator(), vocab_size=50265, min_frequency=2, special_tokens=[
"<s>",
"<pad>",
"</s>",
"<unk>",
"<mask>",
])
# Save files to disk
tokenizer.save("./tokenizer.json")
```
This creates and saves our tokenizer directly in the cloned repository.
Finally, we can start training. For now, we'll simply use the official [`run_mlm_flax`](https://github.com/huggingface/transformers/blob/main/examples/flax/language-modeling/run_mlm_flax.py)
script, but we might make some changes later. So let's copy the script into our model repository.
```bash
$ cp ~/transformers/examples/flax/language-modeling/run_mlm_flax.py ./
```
This way we are certain to have all the code used to train the model tracked in our repository.
Let's start training by running:
```bash
./run_mlm_flax.py \
--output_dir="./" \
--model_type="roberta" \
--config_name="./" \
--tokenizer_name="./" \
--dataset_name="oscar" \
--dataset_config_name="unshuffled_deduplicated_als" \
--max_seq_length="128" \
--per_device_train_batch_size="4" \
--per_device_eval_batch_size="4" \
--learning_rate="3e-4" \
--warmup_steps="1000" \
--overwrite_output_dir \
--num_train_epochs="8" \
--push_to_hub
```
Since the dataset is tiny this command should actually run in less than 5 minutes. Note that we attach
the flag ``--push_to_hub`` so that both model weights and tensorboard traces are automatically uploaded to the hub.
You can see the tensorboard directly on the model page, under the [Training metrics tab](https://huggingface.co/flax-community/roberta-base-als/tensorboard).
As you can see, it is pretty simple to upload model weights and training logs to the model hub. Since the repository
has git version control, you & your team probably already have the necessary skills to collaborate. Thanks
to `git-lfs` being integrated into the hub, model weights and other larger file can just as easily be uploaded
and changed. Finally, at Hugging Face, we believe that the model hub is a great platform to share your project
while you are still working on it:
- Bugs in training scripts can be found and corrected by anybody participating in the event
- Loss curves can be analyzed directly on the model page
- Model weights can be accessed and analyzed by everybody from the model repository
If you are not using a transformers model, don't worry - you should still be able to make use of the hub's functionalities!
The [huggingface_hub](https://github.com/huggingface/huggingface_hub) allows you to upload essentially any JAX/Flax model to the hub with
just a couple of lines of code. *E.g.* assuming you want to call your model simply `flax-model-dummy`, you can upload it to the hub with
just three lines of code:
```python
from flax import serialization
from jax import random
from flax import linen as nn
from huggingface_hub import Repository
model = nn.Dense(features=5)
key1, key2 = random.split(random.PRNGKey(0))
x = random.normal(key1, (10,))
params = model.init(key2, x)
bytes_output = serialization.to_bytes(params)
repo = Repository("flax-model", clone_from="flax-community/flax-model-dummy", token=True)
with repo.commit("My cool Flax model :)"):
with open("flax_model.msgpack", "wb") as f:
f.write(bytes_output)
# Repo is created and available here: https://huggingface.co/flax-community/flax-model-dummy
```
**Note**: Make sure to have `huggingface_hub >= 0.0.13` to make this command work.
For more information, check out [this PR](https://github.com/huggingface/huggingface_hub/pull/143) on how to upload any framework to the hub.
## How to setup TPU VM
In this section we will explain how you can ssh into a TPU VM that has been given to your team.
If your username is in one of the officially defined projects [here](https://docs.google.com/spreadsheets/d/1GpHebL7qrwJOc9olTpIPgjf8vOS0jNb6zR_B8x_Jtik/edit?usp=sharing), you should have received two emails:
- one that states that you have been granted the role "Community Week Participants" for the project hf-flax, and
- one (or more if you are in multiple projects) that gives you the TPU name and the TPU zone for the TPU of your team
You should click on "Open Cloud Console" on the first mail and agree to the pop up windows that follows. It will allow you to use a TPU VM. Don't worry if you cannot access the actual project `hf-flax` visually on the google cloud console and receive an error:
```
You don't have sufficient permission to view this page
```
- this is expected!
Great, now you and your team can access your TPU VM!
In the following, we will describe how to do so using a standard console, but you should also be able to connect to the TPU VM via IDEs, like Visual Studio Code, etc.
1. You need to install the Google Cloud SDK. Please follow the instructions on [cloud.google.com/sdk](https://cloud.google.com/sdk/docs/install#linux).
2. Once you've installed the google cloud sdk, you should set your account by running the following command. Make sure that `<your-email-address>` corresponds to the gmail address you used to sign up for this event.
```bash
$ gcloud config set account <your-email-adress>
```
3. Let's also make sure the correct project is set in case your email is used for multiple gcloud projects:
```bash
$ gcloud config set project hf-flax
```
4. Next, you will need to authenticate yourself. You can do so by running:
```bash
$ gcloud auth login
```
This should give you a link to a website, where you can authenticate your gmail account.
5. Finally, you can ssh into the TPU VM! Please run the following command by setting <zone> to either `europe-west4-a` or `us-central1-a` (depending on what is stated in the second email you received) and <tpu-name> to the TPU name also sent to you in the second email.
```bash
$ gcloud alpha compute tpus tpu-vm ssh <tpu-name> --zone <zone> --project hf-flax
```
This should ssh you into the TPU VM!
Now you can follow the steps of the section [How to install relevant libraries](#how-to-install-relevant-libraries) to install all necessary
libraries. Make sure to carefully follow the explanations of the "**IMPORTANT**" statement to correctly install JAX on TPU.
Also feel free to install other `python` or `apt` packages on your machine if it helps you to work more efficiently!
## How to build a demo
### Using the Hugging Face Widgets
Hugging Face has over [15 widgets](https://huggingface-widgets.netlify.app/) for different use cases using 🤗 Transformers library. Some of them also support [3rd party libraries](https://huggingface.co/docs/hub/libraries) such as [Sentence Similarity](https://huggingface.co/sentence-transformers/paraphrase-xlm-r-multilingual-v1) with Sentence Transformers and [Text to Speech](https://huggingface.co/julien-c/ljspeech_tts_train_tacotron2_raw_phn_tacotron_g2p_en_no_space_train) with [ESPnet](https://github.com/espnet/espnet).
All the widgets are open sourced in the `huggingface_hub` [repo](https://github.com/huggingface/huggingface_hub/tree/main/widgets). Here is a summary of existing widgets:
**NLP**
* **Conversational:** To have the best conversations!. [Example](https://huggingface.co/microsoft/DialoGPT-large?).
* **Feature Extraction:** Retrieve the input embeddings. [Example](https://huggingface.co/sentence-transformers/distilbert-base-nli-mean-tokens?text=test).
* **Fill Mask:** Predict potential words for a mask token. [Example](https://huggingface.co/bert-base-uncased?).
* **Question Answering:** Given a context and a question, predict the answer. [Example](https://huggingface.co/bert-large-uncased-whole-word-masking-finetuned-squad).
* **Sentence Simmilarity:** Predict how similar a set of sentences are. Useful for Sentence Transformers.
* **Summarization:** Given a text, output a summary of it. [Example](https://huggingface.co/sshleifer/distilbart-cnn-12-6).
* **Table Question Answering:** Given a table and a question, predict the answer. [Example](https://huggingface.co/google/tapas-base-finetuned-wtq).
* **Text Generation:** Generate text based on a prompt. [Example](https://huggingface.co/gpt2)
* **Token Classification:** Useful for tasks such as Named Entity Recognition and Part of Speech. [Example](https://huggingface.co/dslim/bert-base-NER).
* **Zero-Shot Classification:** Too cool to explain with words. Here is an [example](https://huggingface.co/typeform/distilbert-base-uncased-mnli)
* ([WIP](https://github.com/huggingface/huggingface_hub/issues/99)) **Table to Text Generation**.
**Speech**
* **Audio to Audio:** For tasks such as audio source separation or speech enhancement.
* **Automatic Speech Recognition:** Convert audio to text. [Example](https://huggingface.co/facebook/wav2vec2-base-960h)
* **Text to Speech**: Convert text to audio.
**Image**
* **Image Classification:** Given an image, predict its class. [Example](https://huggingface.co/osanseviero/llamastic).
* ([WIP](https://github.com/huggingface/huggingface_hub/issues/100)) **Zero Shot Image Classification**
* ([WIP](https://github.com/huggingface/huggingface_hub/issues/112)) **Image Captioning**
* ([WIP](https://github.com/huggingface/huggingface_hub/issues/113)) **Text to Image Generation**
* ([Proposed](https://github.com/huggingface/huggingface_hub/issues/127)) **Visual Question Answering**
You can propose and implement new widgets by [opening an issue](https://github.com/huggingface/huggingface_hub/issues). Contributions are welcomed!
### Using a Streamlit demo
Sometimes you might be using different libraries or a very specific application that is not well supported by the current widgets. In this case, [Streamlit](https://streamlit.io/) can be an excellent option to build a cool visual demo. Setting up a Streamlit application is straightforward and in Python!
A common use case is how to load files you have in your model repository in the Hub from the Streamlit demo. The `huggingface_hub` library is here to help you!
```
pip install huggingface_hub
```
Here is an example downloading (and caching!) a specific file directly from the Hub
```
from huggingface_hub import hf_hub_download
filepath = hf_hub_download("flax-community/roberta-base-als", "flax_model.msgpack");
```
In many cases you will want to download the full repository. Here is an example downloading all the files from a repo. You can even specify specific revisions!
```
from huggingface_hub import snapshot_download
local_path = snapshot_download("flax-community/roberta-base-als");
```
Note that if you're using 🤗 Transformers library, you can quickly load the model and tokenizer as follows
```
from transformers import AutoTokenizer, AutoModelForMaskedLM
tokenizer = AutoTokenizer.from_pretrained("REPO_ID")
model = AutoModelForMaskedLM.from_pretrained("REPO_ID")
```
We'll provide more examples on Streamlit demos next week. Stay tuned!
### Using a Gradio demo
You can also use [Gradio](https://gradio.app/) to share your demos! [Here](https://huggingface.co/blog/gradio) is an example using the Gradio library to create a GUI for a Hugging Face model.
More to come!
## Project evaluation
For your project to be evaluated, please fill out [this google form](https://forms.gle/jQaMkj3JJdD4Xcwn9).
Please make sure that your submitted project includes a demo as well as information about the model, data, training methods, etc.
### Criteria
* **Demo.** All projects are required to have a demo. It’s open ended, but we provide some ideas on how to build demos in the [How to build a demo](#how-to-build-a-demo) section.
* **Technical difficulty.** Difficulty has different aspects, such as working with complex architectures, obtaining better evaluation metrics than existing models, or implementing models for low-resource languages.
* **Social impact.** The project is expected to have a positive social impact, e.g. by tackling under-explored area of practical interest for minorities or under-represented group (low-ressources languages, specific focus on bias, fairness or ethical issues in ML) or by tackling general societal challenges, e.g. health or climate related challenges.
* **Innovativeness.** Projects that propose novel applications or bring new ideas will be rewarded more.
### Jury
* [Niki Parmar](https://research.google/people/NikiParmar/): Staff Research Scientist at Google.
* [Ross Wightman](https://www.linkedin.com/in/wightmanr/): Angel Investor.
* [Thomas Wolf](https://www.linkedin.com/in/thomas-wolf-a056857/): Co-founder and CSO at Hugging Face.
* [Ashish Vaswani](https://research.google/people/AshishVaswani/): Staff Research Scientist at Google Brain.
### Process
* **July 17, 12h00 CEST**: TPU VM access closes.
* **July 19, 12h00 CEST**: Project completition ends (including demo).
* **July 19-21** A group of event organizers (Suraj, Patrick, Suzana, and Omar) will do an initial filter to find the top 15 projects.
* **July 22-26** The jury will go over the 15 projects and pick the top three projects out of them.
* **July 27.** Winner projects are announced
## General tips and tricks
TODO (will be filled continuously)...
## FAQ
TODO (will be filled continuously)...
| huggingface/transformers/blob/main/examples/research_projects/jax-projects/README.md |
!--Copyright 2023 The HuggingFace Team. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
the License. You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
specific language governing permissions and limitations under the License.
⚠️ Note that this file is in Markdown but contain specific syntax for our doc-builder (similar to MDX) that may not be
rendered properly in your Markdown viewer.
-->
# BLIP-2
## Overview
The BLIP-2 model was proposed in [BLIP-2: Bootstrapping Language-Image Pre-training with Frozen Image Encoders and Large Language Models](https://arxiv.org/abs/2301.12597) by
Junnan Li, Dongxu Li, Silvio Savarese, Steven Hoi. BLIP-2 leverages frozen pre-trained image encoders and large language models (LLMs) by training a lightweight, 12-layer Transformer
encoder in between them, achieving state-of-the-art performance on various vision-language tasks. Most notably, BLIP-2 improves upon [Flamingo](https://arxiv.org/abs/2204.14198), an 80 billion parameter model, by 8.7%
on zero-shot VQAv2 with 54x fewer trainable parameters.
The abstract from the paper is the following:
*The cost of vision-and-language pre-training has become increasingly prohibitive due to end-to-end training of large-scale models. This paper proposes BLIP-2, a generic and efficient pre-training strategy that bootstraps vision-language pre-training from off-the-shelf frozen pre-trained image encoders and frozen large language models. BLIP-2 bridges the modality gap with a lightweight Querying Transformer, which is pre-trained in two stages. The first stage bootstraps vision-language representation learning from a frozen image encoder. The second stage bootstraps vision-to-language generative learning from a frozen language model. BLIP-2 achieves state-of-the-art performance on various vision-language tasks, despite having significantly fewer trainable parameters than existing methods. For example, our model outperforms Flamingo80B by 8.7% on zero-shot VQAv2 with 54x fewer trainable parameters. We also demonstrate the model's emerging capabilities of zero-shot image-to-text generation that can follow natural language instructions.*
<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/model_doc/blip2_architecture.jpg"
alt="drawing" width="600"/>
<small> BLIP-2 architecture. Taken from the <a href="https://arxiv.org/abs/2301.12597">original paper.</a> </small>
This model was contributed by [nielsr](https://huggingface.co/nielsr).
The original code can be found [here](https://github.com/salesforce/LAVIS/tree/5ee63d688ba4cebff63acee04adaef2dee9af207).
## Usage tips
- BLIP-2 can be used for conditional text generation given an image and an optional text prompt. At inference time, it's recommended to use the [`generate`] method.
- One can use [`Blip2Processor`] to prepare images for the model, and decode the predicted tokens ID's back to text.
## Resources
A list of official Hugging Face and community (indicated by 🌎) resources to help you get started with BLIP-2.
- Demo notebooks for BLIP-2 for image captioning, visual question answering (VQA) and chat-like conversations can be found [here](https://github.com/NielsRogge/Transformers-Tutorials/tree/master/BLIP-2).
If you're interested in submitting a resource to be included here, please feel free to open a Pull Request and we'll review it! The resource should ideally demonstrate something new instead of duplicating an existing resource.
## Blip2Config
[[autodoc]] Blip2Config
- from_vision_qformer_text_configs
## Blip2VisionConfig
[[autodoc]] Blip2VisionConfig
## Blip2QFormerConfig
[[autodoc]] Blip2QFormerConfig
## Blip2Processor
[[autodoc]] Blip2Processor
## Blip2VisionModel
[[autodoc]] Blip2VisionModel
- forward
## Blip2QFormerModel
[[autodoc]] Blip2QFormerModel
- forward
## Blip2Model
[[autodoc]] Blip2Model
- forward
- get_text_features
- get_image_features
- get_qformer_features
## Blip2ForConditionalGeneration
[[autodoc]] Blip2ForConditionalGeneration
- forward
- generate | huggingface/transformers/blob/main/docs/source/en/model_doc/blip-2.md |
Text Summarization with Pretrained Encoders
This folder contains part of the code necessary to reproduce the results on abstractive summarization from the article [Text Summarization with Pretrained Encoders](https://arxiv.org/pdf/1908.08345.pdf) by [Yang Liu](https://nlp-yang.github.io/) and [Mirella Lapata](https://homepages.inf.ed.ac.uk/mlap/). It can also be used to summarize any document.
The original code can be found on the Yang Liu's [github repository](https://github.com/nlpyang/PreSumm).
The model is loaded with the pre-trained weights for the abstractive summarization model trained on the CNN/Daily Mail dataset with an extractive and then abstractive tasks.
## Setup
```
git clone https://github.com/huggingface/transformers && cd transformers
pip install .
pip install nltk py-rouge
cd examples/seq2seq/bertabs
```
## Reproduce the authors' ROUGE score
To be able to reproduce the authors' results on the CNN/Daily Mail dataset you first need to download both CNN and Daily Mail datasets [from Kyunghyun Cho's website](https://cs.nyu.edu/~kcho/DMQA/) (the links next to "Stories") in the same folder. Then uncompress the archives by running:
```bash
tar -xvf cnn_stories.tgz && tar -xvf dailymail_stories.tgz
```
And move all the stories to the same folder. We will refer as `$DATA_PATH` the path to where you uncompressed both archive. Then run the following in the same folder as `run_summarization.py`:
```bash
python run_summarization.py \
--documents_dir $DATA_PATH \
--summaries_output_dir $SUMMARIES_PATH \ # optional
--no_cuda false \
--batch_size 4 \
--min_length 50 \
--max_length 200 \
--beam_size 5 \
--alpha 0.95 \
--block_trigram true \
--compute_rouge true
```
The scripts executes on GPU if one is available and if `no_cuda` is not set to `true`. Inference on multiple GPUs is not supported yet. The ROUGE scores will be displayed in the console at the end of evaluation and written in a `rouge_scores.txt` file. The script takes 30 hours to compute with a single Tesla V100 GPU and a batch size of 10 (300,000 texts to summarize).
## Summarize any text
Put the documents that you would like to summarize in a folder (the path to which is referred to as `$DATA_PATH` below) and run the following in the same folder as `run_summarization.py`:
```bash
python run_summarization.py \
--documents_dir $DATA_PATH \
--summaries_output_dir $SUMMARIES_PATH \ # optional
--no_cuda false \
--batch_size 4 \
--min_length 50 \
--max_length 200 \
--beam_size 5 \
--alpha 0.95 \
--block_trigram true \
```
You may want to play around with `min_length`, `max_length` and `alpha` to suit your use case. If you want to compute ROUGE on another dataset you will need to tweak the stories/summaries import in `utils_summarization.py` and tell it where to fetch the reference summaries.
| huggingface/transformers/blob/main/examples/research_projects/bertabs/README.md |
!--Copyright 2022 The HuggingFace Team. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
the License. You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
specific language governing permissions and limitations under the License.
⚠️ Note that this file is in Markdown but contain specific syntax for our doc-builder (similar to MDX) that may not be
rendered properly in your Markdown viewer.
-->
# Swin Transformer
## Overview
The Swin Transformer was proposed in [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://arxiv.org/abs/2103.14030)
by Ze Liu, Yutong Lin, Yue Cao, Han Hu, Yixuan Wei, Zheng Zhang, Stephen Lin, Baining Guo.
The abstract from the paper is the following:
*This paper presents a new vision Transformer, called Swin Transformer, that capably serves as a general-purpose backbone
for computer vision. Challenges in adapting Transformer from language to vision arise from differences between the two domains,
such as large variations in the scale of visual entities and the high resolution of pixels in images compared to words in text.
To address these differences, we propose a hierarchical Transformer whose representation is computed with \bold{S}hifted
\bold{win}dows. The shifted windowing scheme brings greater efficiency by limiting self-attention computation to non-overlapping
local windows while also allowing for cross-window connection. This hierarchical architecture has the flexibility to model at
various scales and has linear computational complexity with respect to image size. These qualities of Swin Transformer make it
compatible with a broad range of vision tasks, including image classification (87.3 top-1 accuracy on ImageNet-1K) and dense
prediction tasks such as object detection (58.7 box AP and 51.1 mask AP on COCO test-dev) and semantic segmentation
(53.5 mIoU on ADE20K val). Its performance surpasses the previous state-of-the-art by a large margin of +2.7 box AP and
+2.6 mask AP on COCO, and +3.2 mIoU on ADE20K, demonstrating the potential of Transformer-based models as vision backbones.
The hierarchical design and the shifted window approach also prove beneficial for all-MLP architectures.*
<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/swin_transformer_architecture.png"
alt="drawing" width="600"/>
<small> Swin Transformer architecture. Taken from the <a href="https://arxiv.org/abs/2102.03334">original paper</a>.</small>
This model was contributed by [novice03](https://huggingface.co/novice03). The Tensorflow version of this model was contributed by [amyeroberts](https://huggingface.co/amyeroberts). The original code can be found [here](https://github.com/microsoft/Swin-Transformer).
## Usage tips
- Swin pads the inputs supporting any input height and width (if divisible by `32`).
- Swin can be used as a *backbone*. When `output_hidden_states = True`, it will output both `hidden_states` and `reshaped_hidden_states`. The `reshaped_hidden_states` have a shape of `(batch, num_channels, height, width)` rather than `(batch_size, sequence_length, num_channels)`.
## Resources
A list of official Hugging Face and community (indicated by 🌎) resources to help you get started with Swin Transformer.
<PipelineTag pipeline="image-classification"/>
- [`SwinForImageClassification`] is supported by this [example script](https://github.com/huggingface/transformers/tree/main/examples/pytorch/image-classification) and [notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/image_classification.ipynb).
- See also: [Image classification task guide](../tasks/image_classification)
Besides that:
- [`SwinForMaskedImageModeling`] is supported by this [example script](https://github.com/huggingface/transformers/tree/main/examples/pytorch/image-pretraining).
If you're interested in submitting a resource to be included here, please feel free to open a Pull Request and we'll review it! The resource should ideally demonstrate something new instead of duplicating an existing resource.
## SwinConfig
[[autodoc]] SwinConfig
<frameworkcontent>
<pt>
## SwinModel
[[autodoc]] SwinModel
- forward
## SwinForMaskedImageModeling
[[autodoc]] SwinForMaskedImageModeling
- forward
## SwinForImageClassification
[[autodoc]] transformers.SwinForImageClassification
- forward
</pt>
<tf>
## TFSwinModel
[[autodoc]] TFSwinModel
- call
## TFSwinForMaskedImageModeling
[[autodoc]] TFSwinForMaskedImageModeling
- call
## TFSwinForImageClassification
[[autodoc]] transformers.TFSwinForImageClassification
- call
</tf>
</frameworkcontent> | huggingface/transformers/blob/main/docs/source/en/model_doc/swin.md |
!--Copyright 2020 The HuggingFace Team. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
the License. You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
specific language governing permissions and limitations under the License.
⚠️ Note that this file is in Markdown but contain specific syntax for our doc-builder (similar to MDX) that may not be
rendered properly in your Markdown viewer.
-->
# Exporting 🤗 Transformers models to ONNX
🤗 Transformers provides a `transformers.onnx` package that enables you to
convert model checkpoints to an ONNX graph by leveraging configuration objects.
See the [guide](../serialization) on exporting 🤗 Transformers models for more
details.
## ONNX Configurations
We provide three abstract classes that you should inherit from, depending on the
type of model architecture you wish to export:
* Encoder-based models inherit from [`~onnx.config.OnnxConfig`]
* Decoder-based models inherit from [`~onnx.config.OnnxConfigWithPast`]
* Encoder-decoder models inherit from [`~onnx.config.OnnxSeq2SeqConfigWithPast`]
### OnnxConfig
[[autodoc]] onnx.config.OnnxConfig
### OnnxConfigWithPast
[[autodoc]] onnx.config.OnnxConfigWithPast
### OnnxSeq2SeqConfigWithPast
[[autodoc]] onnx.config.OnnxSeq2SeqConfigWithPast
## ONNX Features
Each ONNX configuration is associated with a set of _features_ that enable you
to export models for different types of topologies or tasks.
### FeaturesManager
[[autodoc]] onnx.features.FeaturesManager
| huggingface/transformers/blob/main/docs/source/en/main_classes/onnx.md |
!--Copyright 2023 The Intel Team Authors and HuggingFace Inc. team. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
the License. You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
specific language governing permissions and limitations under the License.
-->
# TVP
## Overview
The text-visual prompting (TVP) framework was proposed in the paper [Text-Visual Prompting for Efficient 2D Temporal Video Grounding](https://arxiv.org/abs/2303.04995) by Yimeng Zhang, Xin Chen, Jinghan Jia, Sijia Liu, Ke Ding.
The abstract from the paper is the following:
*In this paper, we study the problem of temporal video grounding (TVG), which aims to predict the starting/ending time points of moments described by a text sentence within a long untrimmed video. Benefiting from fine-grained 3D visual features, the TVG techniques have achieved remarkable progress in recent years. However, the high complexity of 3D convolutional neural networks (CNNs) makes extracting dense 3D visual features time-consuming, which calls for intensive memory and computing resources. Towards efficient TVG, we propose a novel text-visual prompting (TVP) framework, which incorporates optimized perturbation patterns (that we call ‘prompts’) into both visual inputs and textual features of a TVG model. In sharp contrast to 3D CNNs, we show that TVP allows us to effectively co-train vision encoder and language encoder in a 2D TVG model and improves the performance of cross-modal feature fusion using only low-complexity sparse 2D visual features. Further, we propose a Temporal-Distance IoU (TDIoU) loss for efficient learning of TVG. Experiments on two benchmark datasets, Charades-STA and ActivityNet Captions datasets, empirically show that the proposed TVP significantly boosts the performance of 2D TVG (e.g., 9.79% improvement on Charades-STA and 30.77% improvement on ActivityNet Captions) and achieves 5× inference acceleration over TVG using 3D visual features.*
This research addresses temporal video grounding (TVG), which is the process of pinpointing the start and end times of specific events in a long video, as described by a text sentence. Text-visual prompting (TVP), is proposed to enhance TVG. TVP involves integrating specially designed patterns, known as 'prompts', into both the visual (image-based) and textual (word-based) input components of a TVG model. These prompts provide additional spatial-temporal context, improving the model's ability to accurately determine event timings in the video. The approach employs 2D visual inputs in place of 3D ones. Although 3D inputs offer more spatial-temporal detail, they are also more time-consuming to process. The use of 2D inputs with the prompting method aims to provide similar levels of context and accuracy more efficiently.
<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/model_doc/tvp_architecture.png"
alt="drawing" width="600"/>
<small> TVP architecture. Taken from the <a href="https://arxiv.org/abs/2303.04995">original paper.</a> </small>
This model was contributed by [Jiqing Feng](https://huggingface.co/Jiqing). The original code can be found [here](https://github.com/intel/TVP).
## Usage tips and examples
Prompts are optimized perturbation patterns, which would be added to input video frames or text features. Universal set refers to using the same exact set of prompts for any input, this means that these prompts are added consistently to all video frames and text features, regardless of the input's content.
TVP consists of a visual encoder and cross-modal encoder. A universal set of visual prompts and text prompts to be integrated into sampled video frames and textual features, respectively. Specially, a set of different visual prompts are applied to uniformly-sampled frames of one untrimmed video in order.
The goal of this model is to incorporate trainable prompts into both visual inputs and textual features to temporal video grounding(TVG) problems.
In principle, one can apply any visual, cross-modal encoder in the proposed architecture.
The [`TvpProcessor`] wraps [`BertTokenizer`] and [`TvpImageProcessor`] into a single instance to both
encode the text and prepare the images respectively.
The following example shows how to run temporal video grounding using [`TvpProcessor`] and [`TvpForVideoGrounding`].
```python
import av
import cv2
import numpy as np
import torch
from huggingface_hub import hf_hub_download
from transformers import AutoProcessor, TvpForVideoGrounding
def pyav_decode(container, sampling_rate, num_frames, clip_idx, num_clips, target_fps):
'''
Convert the video from its original fps to the target_fps and decode the video with PyAV decoder.
Args:
container (container): pyav container.
sampling_rate (int): frame sampling rate (interval between two sampled frames).
num_frames (int): number of frames to sample.
clip_idx (int): if clip_idx is -1, perform random temporal sampling.
If clip_idx is larger than -1, uniformly split the video to num_clips
clips, and select the clip_idx-th video clip.
num_clips (int): overall number of clips to uniformly sample from the given video.
target_fps (int): the input video may have different fps, convert it to
the target video fps before frame sampling.
Returns:
frames (tensor): decoded frames from the video. Return None if the no
video stream was found.
fps (float): the number of frames per second of the video.
'''
video = container.streams.video[0]
fps = float(video.average_rate)
clip_size = sampling_rate * num_frames / target_fps * fps
delta = max(num_frames - clip_size, 0)
start_idx = delta * clip_idx / num_clips
end_idx = start_idx + clip_size - 1
timebase = video.duration / num_frames
video_start_pts = int(start_idx * timebase)
video_end_pts = int(end_idx * timebase)
seek_offset = max(video_start_pts - 1024, 0)
container.seek(seek_offset, any_frame=False, backward=True, stream=video)
frames = {}
for frame in container.decode(video=0):
if frame.pts < video_start_pts:
continue
frames[frame.pts] = frame
if frame.pts > video_end_pts:
break
frames = [frames[pts] for pts in sorted(frames)]
return frames, fps
def decode(container, sampling_rate, num_frames, clip_idx, num_clips, target_fps):
'''
Decode the video and perform temporal sampling.
Args:
container (container): pyav container.
sampling_rate (int): frame sampling rate (interval between two sampled frames).
num_frames (int): number of frames to sample.
clip_idx (int): if clip_idx is -1, perform random temporal sampling.
If clip_idx is larger than -1, uniformly split the video to num_clips
clips, and select the clip_idx-th video clip.
num_clips (int): overall number of clips to uniformly sample from the given video.
target_fps (int): the input video may have different fps, convert it to
the target video fps before frame sampling.
Returns:
frames (tensor): decoded frames from the video.
'''
assert clip_idx >= -2, "Not a valied clip_idx {}".format(clip_idx)
frames, fps = pyav_decode(container, sampling_rate, num_frames, clip_idx, num_clips, target_fps)
clip_size = sampling_rate * num_frames / target_fps * fps
index = np.linspace(0, clip_size - 1, num_frames)
index = np.clip(index, 0, len(frames) - 1).astype(np.int64)
frames = np.array([frames[idx].to_rgb().to_ndarray() for idx in index])
frames = frames.transpose(0, 3, 1, 2)
return frames
file = hf_hub_download(repo_id="Intel/tvp_demo", filename="AK2KG.mp4", repo_type="dataset")
model = TvpForVideoGrounding.from_pretrained("Intel/tvp-base")
decoder_kwargs = dict(
container=av.open(file, metadata_errors="ignore"),
sampling_rate=1,
num_frames=model.config.num_frames,
clip_idx=0,
num_clips=1,
target_fps=3,
)
raw_sampled_frms = decode(**decoder_kwargs)
text = "a person is sitting on a bed."
processor = AutoProcessor.from_pretrained("Intel/tvp-base")
model_inputs = processor(
text=[text], videos=list(raw_sampled_frms), return_tensors="pt", max_text_length=100#, size=size
)
model_inputs["pixel_values"] = model_inputs["pixel_values"].to(model.dtype)
output = model(**model_inputs)
def get_video_duration(filename):
cap = cv2.VideoCapture(filename)
if cap.isOpened():
rate = cap.get(5)
frame_num = cap.get(7)
duration = frame_num/rate
return duration
return -1
duration = get_video_duration(file)
start, end = processor.post_process_video_grounding(output.logits, duration)
print(f"The time slot of the video corresponding to the text \"{text}\" is from {start}s to {end}s")
```
Tips:
- This implementation of TVP uses [`BertTokenizer`] to generate text embeddings and Resnet-50 model to compute visual embeddings.
- Checkpoints for pre-trained [tvp-base](https://huggingface.co/Intel/tvp-base) is released.
- Please refer to [Table 2](https://arxiv.org/pdf/2303.04995.pdf) for TVP's performance on Temporal Video Grounding task.
## TvpConfig
[[autodoc]] TvpConfig
## TvpImageProcessor
[[autodoc]] TvpImageProcessor
- preprocess
## TvpProcessor
[[autodoc]] TvpProcessor
- __call__
## TvpModel
[[autodoc]] TvpModel
- forward
## TvpForVideoGrounding
[[autodoc]] TvpForVideoGrounding
- forward
| huggingface/transformers/blob/main/docs/source/en/model_doc/tvp.md |
# Adversarial evaluation of model performances
Here is an example on evaluating a model using adversarial evaluation of natural language inference with the Heuristic Analysis for NLI Systems (HANS) dataset [McCoy et al., 2019](https://arxiv.org/abs/1902.01007). The example was gracefully provided by [Nafise Sadat Moosavi](https://github.com/ns-moosavi).
The HANS dataset can be downloaded from [this location](https://github.com/tommccoy1/hans).
This is an example of using test_hans.py:
```bash
export HANS_DIR=path-to-hans
export MODEL_TYPE=type-of-the-model-e.g.-bert-roberta-xlnet-etc
export MODEL_PATH=path-to-the-model-directory-that-is-trained-on-NLI-e.g.-by-using-run_glue.py
python run_hans.py \
--task_name hans \
--model_type $MODEL_TYPE \
--do_eval \
--data_dir $HANS_DIR \
--model_name_or_path $MODEL_PATH \
--max_seq_length 128 \
--output_dir $MODEL_PATH \
```
This will create the hans_predictions.txt file in MODEL_PATH, which can then be evaluated using hans/evaluate_heur_output.py from the HANS dataset.
The results of the BERT-base model that is trained on MNLI using batch size 8 and the random seed 42 on the HANS dataset is as follows:
```bash
Heuristic entailed results:
lexical_overlap: 0.9702
subsequence: 0.9942
constituent: 0.9962
Heuristic non-entailed results:
lexical_overlap: 0.199
subsequence: 0.0396
constituent: 0.118
```
| huggingface/transformers/blob/main/examples/research_projects/adversarial/README.md |
!--Copyright 2023 The HuggingFace Team. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
the License. You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
specific language governing permissions and limitations under the License.
⚠️ Note that this file is in Markdown but contain specific syntax for our doc-builder (similar to MDX) that may not be
rendered properly in your Markdown viewer.
-->
# FLAN-UL2
## Overview
Flan-UL2 is an encoder decoder model based on the T5 architecture. It uses the same configuration as the [UL2](ul2) model released earlier last year.
It was fine tuned using the "Flan" prompt tuning and dataset collection. Similar to `Flan-T5`, one can directly use FLAN-UL2 weights without finetuning the model:
According to the original blog here are the notable improvements:
- The original UL2 model was only trained with receptive field of 512, which made it non-ideal for N-shot prompting where N is large.
- The Flan-UL2 checkpoint uses a receptive field of 2048 which makes it more usable for few-shot in-context learning.
- The original UL2 model also had mode switch tokens that was rather mandatory to get good performance. However, they were a little cumbersome as this requires often some changes during inference or finetuning. In this update/change, we continue training UL2 20B for an additional 100k steps (with small batch) to forget “mode tokens” before applying Flan instruction tuning. This Flan-UL2 checkpoint does not require mode tokens anymore.
Google has released the following variants:
The original checkpoints can be found [here](https://github.com/google-research/t5x/blob/main/docs/models.md#flan-ul2-checkpoints).
## Running on low resource devices
The model is pretty heavy (~40GB in half precision) so if you just want to run the model, make sure you load your model in 8bit, and use `device_map="auto"` to make sure you don't have any OOM issue!
```python
>>> from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
>>> model = AutoModelForSeq2SeqLM.from_pretrained("google/flan-ul2", load_in_8bit=True, device_map="auto")
>>> tokenizer = AutoTokenizer.from_pretrained("google/flan-ul2")
>>> inputs = tokenizer("A step by step recipe to make bolognese pasta:", return_tensors="pt")
>>> outputs = model.generate(**inputs)
>>> print(tokenizer.batch_decode(outputs, skip_special_tokens=True))
['In a large skillet, brown the ground beef and onion over medium heat. Add the garlic']
```
<Tip>
Refer to [T5's documentation page](t5) for API reference, tips, code examples and notebooks.
</Tip>
| huggingface/transformers/blob/main/docs/source/en/model_doc/flan-ul2.md |
!--Copyright 2022 The HuggingFace Team. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
the License. You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
⚠️ Note that this file is in Markdown but contain specific syntax for our doc-builder (similar to MDX) that may not be
rendered properly in your Markdown viewer.
-->
# GPU inference
GPUs are the standard choice of hardware for machine learning, unlike CPUs, because they are optimized for memory bandwidth and parallelism. To keep up with the larger sizes of modern models or to run these large models on existing and older hardware, there are several optimizations you can use to speed up GPU inference. In this guide, you'll learn how to use FlashAttention-2 (a more memory-efficient attention mechanism), BetterTransformer (a PyTorch native fastpath execution), and bitsandbytes to quantize your model to a lower precision. Finally, learn how to use 🤗 Optimum to accelerate inference with ONNX Runtime on Nvidia and AMD GPUs.
<Tip>
The majority of the optimizations described here also apply to multi-GPU setups!
</Tip>
## FlashAttention-2
<Tip>
FlashAttention-2 is experimental and may change considerably in future versions.
</Tip>
[FlashAttention-2](https://huggingface.co/papers/2205.14135) is a faster and more efficient implementation of the standard attention mechanism that can significantly speedup inference by:
1. additionally parallelizing the attention computation over sequence length
2. partitioning the work between GPU threads to reduce communication and shared memory reads/writes between them
FlashAttention-2 is currently supported for the following architectures:
* [Bark](https://huggingface.co/docs/transformers/model_doc/bark#transformers.BarkModel)
* [Bart](https://huggingface.co/docs/transformers/model_doc/bart#transformers.BartModel)
* [DistilBert](https://huggingface.co/docs/transformers/model_doc/distilbert#transformers.DistilBertModel)
* [GPTBigCode](https://huggingface.co/docs/transformers/model_doc/gpt_bigcode#transformers.GPTBigCodeModel)
* [GPTNeo](https://huggingface.co/docs/transformers/model_doc/gpt_neo#transformers.GPTNeoModel)
* [GPTNeoX](https://huggingface.co/docs/transformers/model_doc/gpt_neox#transformers.GPTNeoXModel)
* [Falcon](https://huggingface.co/docs/transformers/model_doc/falcon#transformers.FalconModel)
* [Llama](https://huggingface.co/docs/transformers/model_doc/llama#transformers.LlamaModel)
* [Llava](https://huggingface.co/docs/transformers/model_doc/llava)
* [VipLlava](https://huggingface.co/docs/transformers/model_doc/vipllava)
* [MBart](https://huggingface.co/docs/transformers/model_doc/mbart#transformers.MBartModel)
* [Mistral](https://huggingface.co/docs/transformers/model_doc/mistral#transformers.MistralModel)
* [Mixtral](https://huggingface.co/docs/transformers/model_doc/mixtral#transformers.MixtralModel)
* [OPT](https://huggingface.co/docs/transformers/model_doc/opt#transformers.OPTModel)
* [Phi](https://huggingface.co/docs/transformers/model_doc/phi#transformers.PhiModel)
* [Whisper](https://huggingface.co/docs/transformers/model_doc/whisper#transformers.WhisperModel)
You can request to add FlashAttention-2 support for another model by opening a GitHub Issue or Pull Request.
Before you begin, make sure you have FlashAttention-2 installed.
<hfoptions id="install">
<hfoption id="NVIDIA">
```bash
pip install flash-attn --no-build-isolation
```
We strongly suggest referring to the detailed [installation instructions](https://github.com/Dao-AILab/flash-attention?tab=readme-ov-file#installation-and-features) to learn more about supported hardware and data types!
</hfoption>
<hfoption id="AMD">
FlashAttention-2 is also supported on AMD GPUs and current support is limited to **Instinct MI210** and **Instinct MI250**. We strongly suggest using this [Dockerfile](https://github.com/huggingface/optimum-amd/tree/main/docker/transformers-pytorch-amd-gpu-flash/Dockerfile) to use FlashAttention-2 on AMD GPUs.
</hfoption>
</hfoptions>
To enable FlashAttention-2, pass the argument `attn_implementation="flash_attention_2"` to [`~AutoModelForCausalLM.from_pretrained`]:
```python
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, LlamaForCausalLM
model_id = "tiiuae/falcon-7b"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype=torch.bfloat16,
attn_implementation="flash_attention_2",
)
```
<Tip>
FlashAttention-2 can only be used when the model's dtype is `fp16` or `bf16`. Make sure to cast your model to the appropriate dtype and load them on a supported device before using FlashAttention-2.
<br>
You can also set `use_flash_attention_2=True` to enable FlashAttention-2 but it is deprecated in favor of `attn_implementation="flash_attention_2"`.
</Tip>
FlashAttention-2 can be combined with other optimization techniques like quantization to further speedup inference. For example, you can combine FlashAttention-2 with 8-bit or 4-bit quantization:
```py
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, LlamaForCausalLM
model_id = "tiiuae/falcon-7b"
tokenizer = AutoTokenizer.from_pretrained(model_id)
# load in 8bit
model = AutoModelForCausalLM.from_pretrained(
model_id,
load_in_8bit=True,
attn_implementation="flash_attention_2",
)
# load in 4bit
model = AutoModelForCausalLM.from_pretrained(
model_id,
load_in_4bit=True,
attn_implementation="flash_attention_2",
)
```
### Expected speedups
You can benefit from considerable speedups for inference, especially for inputs with long sequences. However, since FlashAttention-2 does not support computing attention scores with padding tokens, you must manually pad/unpad the attention scores for batched inference when the sequence contains padding tokens. This leads to a significant slowdown for batched generations with padding tokens.
To overcome this, you should use FlashAttention-2 without padding tokens in the sequence during training (by packing a dataset or [concatenating sequences](https://github.com/huggingface/transformers/blob/main/examples/pytorch/language-modeling/run_clm.py#L516) until reaching the maximum sequence length).
For a single forward pass on [tiiuae/falcon-7b](https://hf.co/tiiuae/falcon-7b) with a sequence length of 4096 and various batch sizes without padding tokens, the expected speedup is:
<div style="text-align: center">
<img src="https://huggingface.co/datasets/ybelkada/documentation-images/resolve/main/falcon-7b-inference-large-seqlen.png">
</div>
For a single forward pass on [meta-llama/Llama-7b-hf](https://hf.co/meta-llama/Llama-7b-hf) with a sequence length of 4096 and various batch sizes without padding tokens, the expected speedup is:
<div style="text-align: center">
<img src="https://huggingface.co/datasets/ybelkada/documentation-images/resolve/main/llama-7b-inference-large-seqlen.png">
</div>
For sequences with padding tokens (generating with padding tokens), you need to unpad/pad the input sequences to correctly compute the attention scores. With a relatively small sequence length, a single forward pass creates overhead leading to a small speedup (in the example below, 30% of the input is filled with padding tokens):
<div style="text-align: center">
<img src="https://huggingface.co/datasets/ybelkada/documentation-images/resolve/main/llama-2-small-seqlen-padding.png">
</div>
But for larger sequence lengths, you can expect even more speedup benefits:
<Tip>
FlashAttention is more memory efficient, meaning you can train on much larger sequence lengths without running into out-of-memory issues. You can potentially reduce memory usage up to 20x for larger sequence lengths. Take a look at the [flash-attention](https://github.com/Dao-AILab/flash-attention) repository for more details.
</Tip>
<div style="text-align: center">
<img src="https://huggingface.co/datasets/ybelkada/documentation-images/resolve/main/llama-2-large-seqlen-padding.png">
</div>
## PyTorch scaled dot product attention
PyTorch's [`torch.nn.functional.scaled_dot_product_attention`](https://pytorch.org/docs/master/generated/torch.nn.functional.scaled_dot_product_attention.html) (SDPA) can also call FlashAttention and memory-efficient attention kernels under the hood. SDPA support is currently being added natively in Transformers and is used by default for `torch>=2.1.1` when an implementation is available.
For now, Transformers supports SDPA inference and training for the following architectures:
* [Bart](https://huggingface.co/docs/transformers/model_doc/bart#transformers.BartModel)
* [GPTBigCode](https://huggingface.co/docs/transformers/model_doc/gpt_bigcode#transformers.GPTBigCodeModel)
* [Falcon](https://huggingface.co/docs/transformers/model_doc/falcon#transformers.FalconModel)
* [Llama](https://huggingface.co/docs/transformers/model_doc/llama#transformers.LlamaModel)
* [Idefics](https://huggingface.co/docs/transformers/model_doc/idefics#transformers.IdeficsModel)
* [Whisper](https://huggingface.co/docs/transformers/model_doc/whisper#transformers.WhisperModel)
<Tip>
FlashAttention can only be used for models with the `fp16` or `bf16` torch type, so make sure to cast your model to the appropriate type first.
</Tip>
By default, SDPA selects the most performant kernel available but you can check whether a backend is available in a given setting (hardware, problem size) with [`torch.backends.cuda.sdp_kernel`](https://pytorch.org/docs/master/backends.html#torch.backends.cuda.sdp_kernel) as a context manager:
```diff
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("facebook/opt-350m")
model = AutoModelForCausalLM.from_pretrained("facebook/opt-350m", torch_dtype=torch.float16).to("cuda")
# convert the model to BetterTransformer
model.to_bettertransformer()
input_text = "Hello my dog is cute and"
inputs = tokenizer(input_text, return_tensors="pt").to("cuda")
+ with torch.backends.cuda.sdp_kernel(enable_flash=True, enable_math=False, enable_mem_efficient=False):
outputs = model.generate(**inputs)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
```
If you see a bug with the traceback below, try using the nightly version of PyTorch which may have broader coverage for FlashAttention:
```bash
RuntimeError: No available kernel. Aborting execution.
# install PyTorch nightly
pip3 install -U --pre torch torchvision torchaudio --index-url https://download.pytorch.org/whl/nightly/cu118
```
## BetterTransformer
<Tip warning={true}>
Some BetterTransformer features are being upstreamed to Transformers with default support for native `torch.nn.scaled_dot_product_attention`. BetterTransformer still has a wider coverage than the Transformers SDPA integration, but you can expect more and more architectures to natively support SDPA in Transformers.
</Tip>
<Tip>
Check out our benchmarks with BetterTransformer and scaled dot product attention in the [Out of the box acceleration and memory savings of 🤗 decoder models with PyTorch 2.0](https://pytorch.org/blog/out-of-the-box-acceleration/) and learn more about the fastpath execution in the [BetterTransformer](https://medium.com/pytorch/bettertransformer-out-of-the-box-performance-for-huggingface-transformers-3fbe27d50ab2) blog post.
</Tip>
BetterTransformer accelerates inference with its fastpath (native PyTorch specialized implementation of Transformer functions) execution. The two optimizations in the fastpath execution are:
1. fusion, which combines multiple sequential operations into a single "kernel" to reduce the number of computation steps
2. skipping the inherent sparsity of padding tokens to avoid unnecessary computation with nested tensors
BetterTransformer also converts all attention operations to use the more memory-efficient [scaled dot product attention (SDPA)](https://pytorch.org/docs/master/generated/torch.nn.functional.scaled_dot_product_attention), and it calls optimized kernels like [FlashAttention](https://huggingface.co/papers/2205.14135) under the hood.
Before you start, make sure you have 🤗 Optimum [installed](https://huggingface.co/docs/optimum/installation).
Then you can enable BetterTransformer with the [`PreTrainedModel.to_bettertransformer`] method:
```python
model = model.to_bettertransformer()
```
You can return the original Transformers model with the [`~PreTrainedModel.reverse_bettertransformer`] method. You should use this before saving your model to use the canonical Transformers modeling:
```py
model = model.reverse_bettertransformer()
model.save_pretrained("saved_model")
```
## bitsandbytes
bitsandbytes is a quantization library that includes support for 4-bit and 8-bit quantization. Quantization reduces your model size compared to its native full precision version, making it easier to fit large models onto GPUs with limited memory.
Make sure you have bitsandbytes and 🤗 Accelerate installed:
```bash
# these versions support 8-bit and 4-bit
pip install bitsandbytes>=0.39.0 accelerate>=0.20.0
# install Transformers
pip install transformers
```
### 4-bit
To load a model in 4-bit for inference, use the `load_in_4bit` parameter. The `device_map` parameter is optional, but we recommend setting it to `"auto"` to allow 🤗 Accelerate to automatically and efficiently allocate the model given the available resources in the environment.
```py
from transformers import AutoModelForCausalLM
model_name = "bigscience/bloom-2b5"
model_4bit = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto", load_in_4bit=True)
```
To load a model in 4-bit for inference with multiple GPUs, you can control how much GPU RAM you want to allocate to each GPU. For example, to distribute 600MB of memory to the first GPU and 1GB of memory to the second GPU:
```py
max_memory_mapping = {0: "600MB", 1: "1GB"}
model_name = "bigscience/bloom-3b"
model_4bit = AutoModelForCausalLM.from_pretrained(
model_name, device_map="auto", load_in_4bit=True, max_memory=max_memory_mapping
)
```
### 8-bit
<Tip>
If you're curious and interested in learning more about the concepts underlying 8-bit quantization, read the [Gentle Introduction to 8-bit Matrix Multiplication for transformers at scale using Hugging Face Transformers, Accelerate and bitsandbytes](https://huggingface.co/blog/hf-bitsandbytes-integration) blog post.
</Tip>
To load a model in 8-bit for inference, use the `load_in_8bit` parameter. The `device_map` parameter is optional, but we recommend setting it to `"auto"` to allow 🤗 Accelerate to automatically and efficiently allocate the model given the available resources in the environment:
```py
from transformers import AutoModelForCausalLM
model_name = "bigscience/bloom-2b5"
model_8bit = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto", load_in_8bit=True)
```
If you're loading a model in 8-bit for text generation, you should use the [`~transformers.GenerationMixin.generate`] method instead of the [`Pipeline`] function which is not optimized for 8-bit models and will be slower. Some sampling strategies, like nucleus sampling, are also not supported by the [`Pipeline`] for 8-bit models. You should also place all inputs on the same device as the model:
```py
from transformers import AutoModelForCausalLM, AutoTokenizer
model_name = "bigscience/bloom-2b5"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model_8bit = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto", load_in_8bit=True)
prompt = "Hello, my llama is cute"
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
generated_ids = model.generate(**inputs)
outputs = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)
```
To load a model in 4-bit for inference with multiple GPUs, you can control how much GPU RAM you want to allocate to each GPU. For example, to distribute 1GB of memory to the first GPU and 2GB of memory to the second GPU:
```py
max_memory_mapping = {0: "1GB", 1: "2GB"}
model_name = "bigscience/bloom-3b"
model_8bit = AutoModelForCausalLM.from_pretrained(
model_name, device_map="auto", load_in_8bit=True, max_memory=max_memory_mapping
)
```
<Tip>
Feel free to try running a 11 billion parameter [T5 model](https://colab.research.google.com/drive/1YORPWx4okIHXnjW7MSAidXN29mPVNT7F?usp=sharing) or the 3 billion parameter [BLOOM model](https://colab.research.google.com/drive/1qOjXfQIAULfKvZqwCen8-MoWKGdSatZ4?usp=sharing) for inference on Google Colab's free tier GPUs!
</Tip>
## 🤗 Optimum
<Tip>
Learn more details about using ORT with 🤗 Optimum in the [Accelerated inference on NVIDIA GPUs](https://huggingface.co/docs/optimum/onnxruntime/usage_guides/gpu#accelerated-inference-on-nvidia-gpus) and [Accelerated inference on AMD GPUs](https://huggingface.co/docs/optimum/onnxruntime/usage_guides/amdgpu#accelerated-inference-on-amd-gpus) guides. This section only provides a brief and simple example.
</Tip>
ONNX Runtime (ORT) is a model accelerator that supports accelerated inference on Nvidia GPUs, and AMD GPUs that use [ROCm](https://www.amd.com/en/products/software/rocm.html) stack. ORT uses optimization techniques like fusing common operations into a single node and constant folding to reduce the number of computations performed and speedup inference. ORT also places the most computationally intensive operations on the GPU and the rest on the CPU to intelligently distribute the workload between the two devices.
ORT is supported by 🤗 Optimum which can be used in 🤗 Transformers. You'll need to use an [`~optimum.onnxruntime.ORTModel`] for the task you're solving, and specify the `provider` parameter which can be set to either [`CUDAExecutionProvider`](https://huggingface.co/docs/optimum/onnxruntime/usage_guides/gpu#cudaexecutionprovider), [`ROCMExecutionProvider`](https://huggingface.co/docs/optimum/onnxruntime/usage_guides/amdgpu) or [`TensorrtExecutionProvider`](https://huggingface.co/docs/optimum/onnxruntime/usage_guides/gpu#tensorrtexecutionprovider). If you want to load a model that was not yet exported to ONNX, you can set `export=True` to convert your model on-the-fly to the ONNX format:
```py
from optimum.onnxruntime import ORTModelForSequenceClassification
ort_model = ORTModelForSequenceClassification.from_pretrained(
"distilbert-base-uncased-finetuned-sst-2-english",
export=True,
provider="CUDAExecutionProvider",
)
```
Now you're free to use the model for inference:
```py
from optimum.pipelines import pipeline
from transformers import AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("distilbert-base-uncased-finetuned-sst-2-english")
pipeline = pipeline(task="text-classification", model=ort_model, tokenizer=tokenizer, device="cuda:0")
result = pipeline("Both the music and visual were astounding, not to mention the actors performance.")
```
## Combine optimizations
It is often possible to combine several of the optimization techniques described above to get the best inference performance possible for your model. For example, you can load a model in 4-bit, and then enable BetterTransformer with FlashAttention:
```py
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
# load model in 4-bit
quantization_config = BitsAndBytesConfig(
load_in_4bit=True,
bnb_4bit_compute_dtype=torch.float16
)
tokenizer = AutoTokenizer.from_pretrained("facebook/opt-350m")
model = AutoModelForCausalLM.from_pretrained("facebook/opt-350m", quantization_config=quantization_config)
# enable BetterTransformer
model = model.to_bettertransformer()
input_text = "Hello my dog is cute and"
inputs = tokenizer(input_text, return_tensors="pt").to("cuda")
# enable FlashAttention
with torch.backends.cuda.sdp_kernel(enable_flash=True, enable_math=False, enable_mem_efficient=False):
outputs = model.generate(**inputs)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
```
| huggingface/transformers/blob/main/docs/source/en/perf_infer_gpu_one.md |
!--Copyright 2020 The HuggingFace Team. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
the License. You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
specific language governing permissions and limitations under the License.
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# Summary of the tokenizers
[[open-in-colab]]
On this page, we will have a closer look at tokenization.
<Youtube id="VFp38yj8h3A"/>
As we saw in [the preprocessing tutorial](preprocessing), tokenizing a text is splitting it into words or
subwords, which then are converted to ids through a look-up table. Converting words or subwords to ids is
straightforward, so in this summary, we will focus on splitting a text into words or subwords (i.e. tokenizing a text).
More specifically, we will look at the three main types of tokenizers used in 🤗 Transformers: [Byte-Pair Encoding
(BPE)](#byte-pair-encoding), [WordPiece](#wordpiece), and [SentencePiece](#sentencepiece), and show examples
of which tokenizer type is used by which model.
Note that on each model page, you can look at the documentation of the associated tokenizer to know which tokenizer
type was used by the pretrained model. For instance, if we look at [`BertTokenizer`], we can see
that the model uses [WordPiece](#wordpiece).
## Introduction
Splitting a text into smaller chunks is a task that is harder than it looks, and there are multiple ways of doing so.
For instance, let's look at the sentence `"Don't you love 🤗 Transformers? We sure do."`
<Youtube id="nhJxYji1aho"/>
A simple way of tokenizing this text is to split it by spaces, which would give:
```
["Don't", "you", "love", "🤗", "Transformers?", "We", "sure", "do."]
```
This is a sensible first step, but if we look at the tokens `"Transformers?"` and `"do."`, we notice that the
punctuation is attached to the words `"Transformer"` and `"do"`, which is suboptimal. We should take the
punctuation into account so that a model does not have to learn a different representation of a word and every possible
punctuation symbol that could follow it, which would explode the number of representations the model has to learn.
Taking punctuation into account, tokenizing our exemplary text would give:
```
["Don", "'", "t", "you", "love", "🤗", "Transformers", "?", "We", "sure", "do", "."]
```
Better. However, it is disadvantageous, how the tokenization dealt with the word `"Don't"`. `"Don't"` stands for
`"do not"`, so it would be better tokenized as `["Do", "n't"]`. This is where things start getting complicated, and
part of the reason each model has its own tokenizer type. Depending on the rules we apply for tokenizing a text, a
different tokenized output is generated for the same text. A pretrained model only performs properly if you feed it an
input that was tokenized with the same rules that were used to tokenize its training data.
[spaCy](https://spacy.io/) and [Moses](http://www.statmt.org/moses/?n=Development.GetStarted) are two popular
rule-based tokenizers. Applying them on our example, *spaCy* and *Moses* would output something like:
```
["Do", "n't", "you", "love", "🤗", "Transformers", "?", "We", "sure", "do", "."]
```
As can be seen space and punctuation tokenization, as well as rule-based tokenization, is used here. Space and
punctuation tokenization and rule-based tokenization are both examples of word tokenization, which is loosely defined
as splitting sentences into words. While it's the most intuitive way to split texts into smaller chunks, this
tokenization method can lead to problems for massive text corpora. In this case, space and punctuation tokenization
usually generates a very big vocabulary (the set of all unique words and tokens used). *E.g.*, [Transformer XL](model_doc/transformerxl) uses space and punctuation tokenization, resulting in a vocabulary size of 267,735!
Such a big vocabulary size forces the model to have an enormous embedding matrix as the input and output layer, which
causes both an increased memory and time complexity. In general, transformers models rarely have a vocabulary size
greater than 50,000, especially if they are pretrained only on a single language.
So if simple space and punctuation tokenization is unsatisfactory, why not simply tokenize on characters?
<Youtube id="ssLq_EK2jLE"/>
While character tokenization is very simple and would greatly reduce memory and time complexity it makes it much harder
for the model to learn meaningful input representations. *E.g.* learning a meaningful context-independent
representation for the letter `"t"` is much harder than learning a context-independent representation for the word
`"today"`. Therefore, character tokenization is often accompanied by a loss of performance. So to get the best of
both worlds, transformers models use a hybrid between word-level and character-level tokenization called **subword**
tokenization.
## Subword tokenization
<Youtube id="zHvTiHr506c"/>
Subword tokenization algorithms rely on the principle that frequently used words should not be split into smaller
subwords, but rare words should be decomposed into meaningful subwords. For instance `"annoyingly"` might be
considered a rare word and could be decomposed into `"annoying"` and `"ly"`. Both `"annoying"` and `"ly"` as
stand-alone subwords would appear more frequently while at the same time the meaning of `"annoyingly"` is kept by the
composite meaning of `"annoying"` and `"ly"`. This is especially useful in agglutinative languages such as Turkish,
where you can form (almost) arbitrarily long complex words by stringing together subwords.
Subword tokenization allows the model to have a reasonable vocabulary size while being able to learn meaningful
context-independent representations. In addition, subword tokenization enables the model to process words it has never
seen before, by decomposing them into known subwords. For instance, the [`~transformers.BertTokenizer`] tokenizes
`"I have a new GPU!"` as follows:
```py
>>> from transformers import BertTokenizer
>>> tokenizer = BertTokenizer.from_pretrained("bert-base-uncased")
>>> tokenizer.tokenize("I have a new GPU!")
["i", "have", "a", "new", "gp", "##u", "!"]
```
Because we are considering the uncased model, the sentence was lowercased first. We can see that the words `["i", "have", "a", "new"]` are present in the tokenizer's vocabulary, but the word `"gpu"` is not. Consequently, the
tokenizer splits `"gpu"` into known subwords: `["gp" and "##u"]`. `"##"` means that the rest of the token should
be attached to the previous one, without space (for decoding or reversal of the tokenization).
As another example, [`~transformers.XLNetTokenizer`] tokenizes our previously exemplary text as follows:
```py
>>> from transformers import XLNetTokenizer
>>> tokenizer = XLNetTokenizer.from_pretrained("xlnet-base-cased")
>>> tokenizer.tokenize("Don't you love 🤗 Transformers? We sure do.")
["▁Don", "'", "t", "▁you", "▁love", "▁", "🤗", "▁", "Transform", "ers", "?", "▁We", "▁sure", "▁do", "."]
```
We'll get back to the meaning of those `"▁"` when we look at [SentencePiece](#sentencepiece). As one can see,
the rare word `"Transformers"` has been split into the more frequent subwords `"Transform"` and `"ers"`.
Let's now look at how the different subword tokenization algorithms work. Note that all of those tokenization
algorithms rely on some form of training which is usually done on the corpus the corresponding model will be trained
on.
<a id='byte-pair-encoding'></a>
### Byte-Pair Encoding (BPE)
Byte-Pair Encoding (BPE) was introduced in [Neural Machine Translation of Rare Words with Subword Units (Sennrich et
al., 2015)](https://arxiv.org/abs/1508.07909). BPE relies on a pre-tokenizer that splits the training data into
words. Pretokenization can be as simple as space tokenization, e.g. [GPT-2](model_doc/gpt2), [RoBERTa](model_doc/roberta). More advanced pre-tokenization include rule-based tokenization, e.g. [XLM](model_doc/xlm),
[FlauBERT](model_doc/flaubert) which uses Moses for most languages, or [GPT](model_doc/gpt) which uses
Spacy and ftfy, to count the frequency of each word in the training corpus.
After pre-tokenization, a set of unique words has been created and the frequency with which each word occurred in the
training data has been determined. Next, BPE creates a base vocabulary consisting of all symbols that occur in the set
of unique words and learns merge rules to form a new symbol from two symbols of the base vocabulary. It does so until
the vocabulary has attained the desired vocabulary size. Note that the desired vocabulary size is a hyperparameter to
define before training the tokenizer.
As an example, let's assume that after pre-tokenization, the following set of words including their frequency has been
determined:
```
("hug", 10), ("pug", 5), ("pun", 12), ("bun", 4), ("hugs", 5)
```
Consequently, the base vocabulary is `["b", "g", "h", "n", "p", "s", "u"]`. Splitting all words into symbols of the
base vocabulary, we obtain:
```
("h" "u" "g", 10), ("p" "u" "g", 5), ("p" "u" "n", 12), ("b" "u" "n", 4), ("h" "u" "g" "s", 5)
```
BPE then counts the frequency of each possible symbol pair and picks the symbol pair that occurs most frequently. In
the example above `"h"` followed by `"u"` is present _10 + 5 = 15_ times (10 times in the 10 occurrences of
`"hug"`, 5 times in the 5 occurrences of `"hugs"`). However, the most frequent symbol pair is `"u"` followed by
`"g"`, occurring _10 + 5 + 5 = 20_ times in total. Thus, the first merge rule the tokenizer learns is to group all
`"u"` symbols followed by a `"g"` symbol together. Next, `"ug"` is added to the vocabulary. The set of words then
becomes
```
("h" "ug", 10), ("p" "ug", 5), ("p" "u" "n", 12), ("b" "u" "n", 4), ("h" "ug" "s", 5)
```
BPE then identifies the next most common symbol pair. It's `"u"` followed by `"n"`, which occurs 16 times. `"u"`,
`"n"` is merged to `"un"` and added to the vocabulary. The next most frequent symbol pair is `"h"` followed by
`"ug"`, occurring 15 times. Again the pair is merged and `"hug"` can be added to the vocabulary.
At this stage, the vocabulary is `["b", "g", "h", "n", "p", "s", "u", "ug", "un", "hug"]` and our set of unique words
is represented as
```
("hug", 10), ("p" "ug", 5), ("p" "un", 12), ("b" "un", 4), ("hug" "s", 5)
```
Assuming, that the Byte-Pair Encoding training would stop at this point, the learned merge rules would then be applied
to new words (as long as those new words do not include symbols that were not in the base vocabulary). For instance,
the word `"bug"` would be tokenized to `["b", "ug"]` but `"mug"` would be tokenized as `["<unk>", "ug"]` since
the symbol `"m"` is not in the base vocabulary. In general, single letters such as `"m"` are not replaced by the
`"<unk>"` symbol because the training data usually includes at least one occurrence of each letter, but it is likely
to happen for very special characters like emojis.
As mentioned earlier, the vocabulary size, *i.e.* the base vocabulary size + the number of merges, is a hyperparameter
to choose. For instance [GPT](model_doc/gpt) has a vocabulary size of 40,478 since they have 478 base characters
and chose to stop training after 40,000 merges.
#### Byte-level BPE
A base vocabulary that includes all possible base characters can be quite large if *e.g.* all unicode characters are
considered as base characters. To have a better base vocabulary, [GPT-2](https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf) uses bytes
as the base vocabulary, which is a clever trick to force the base vocabulary to be of size 256 while ensuring that
every base character is included in the vocabulary. With some additional rules to deal with punctuation, the GPT2's
tokenizer can tokenize every text without the need for the <unk> symbol. [GPT-2](model_doc/gpt) has a vocabulary
size of 50,257, which corresponds to the 256 bytes base tokens, a special end-of-text token and the symbols learned
with 50,000 merges.
<a id='wordpiece'></a>
### WordPiece
WordPiece is the subword tokenization algorithm used for [BERT](model_doc/bert), [DistilBERT](model_doc/distilbert), and [Electra](model_doc/electra). The algorithm was outlined in [Japanese and Korean
Voice Search (Schuster et al., 2012)](https://static.googleusercontent.com/media/research.google.com/ja//pubs/archive/37842.pdf) and is very similar to
BPE. WordPiece first initializes the vocabulary to include every character present in the training data and
progressively learns a given number of merge rules. In contrast to BPE, WordPiece does not choose the most frequent
symbol pair, but the one that maximizes the likelihood of the training data once added to the vocabulary.
So what does this mean exactly? Referring to the previous example, maximizing the likelihood of the training data is
equivalent to finding the symbol pair, whose probability divided by the probabilities of its first symbol followed by
its second symbol is the greatest among all symbol pairs. *E.g.* `"u"`, followed by `"g"` would have only been
merged if the probability of `"ug"` divided by `"u"`, `"g"` would have been greater than for any other symbol
pair. Intuitively, WordPiece is slightly different to BPE in that it evaluates what it _loses_ by merging two symbols
to ensure it's _worth it_.
<a id='unigram'></a>
### Unigram
Unigram is a subword tokenization algorithm introduced in [Subword Regularization: Improving Neural Network Translation
Models with Multiple Subword Candidates (Kudo, 2018)](https://arxiv.org/pdf/1804.10959.pdf). In contrast to BPE or
WordPiece, Unigram initializes its base vocabulary to a large number of symbols and progressively trims down each
symbol to obtain a smaller vocabulary. The base vocabulary could for instance correspond to all pre-tokenized words and
the most common substrings. Unigram is not used directly for any of the models in the transformers, but it's used in
conjunction with [SentencePiece](#sentencepiece).
At each training step, the Unigram algorithm defines a loss (often defined as the log-likelihood) over the training
data given the current vocabulary and a unigram language model. Then, for each symbol in the vocabulary, the algorithm
computes how much the overall loss would increase if the symbol was to be removed from the vocabulary. Unigram then
removes p (with p usually being 10% or 20%) percent of the symbols whose loss increase is the lowest, *i.e.* those
symbols that least affect the overall loss over the training data. This process is repeated until the vocabulary has
reached the desired size. The Unigram algorithm always keeps the base characters so that any word can be tokenized.
Because Unigram is not based on merge rules (in contrast to BPE and WordPiece), the algorithm has several ways of
tokenizing new text after training. As an example, if a trained Unigram tokenizer exhibits the vocabulary:
```
["b", "g", "h", "n", "p", "s", "u", "ug", "un", "hug"],
```
`"hugs"` could be tokenized both as `["hug", "s"]`, `["h", "ug", "s"]` or `["h", "u", "g", "s"]`. So which one
to choose? Unigram saves the probability of each token in the training corpus on top of saving the vocabulary so that
the probability of each possible tokenization can be computed after training. The algorithm simply picks the most
likely tokenization in practice, but also offers the possibility to sample a possible tokenization according to their
probabilities.
Those probabilities are defined by the loss the tokenizer is trained on. Assuming that the training data consists of
the words \\(x_{1}, \dots, x_{N}\\) and that the set of all possible tokenizations for a word \\(x_{i}\\) is
defined as \\(S(x_{i})\\), then the overall loss is defined as
$$\mathcal{L} = -\sum_{i=1}^{N} \log \left ( \sum_{x \in S(x_{i})} p(x) \right )$$
<a id='sentencepiece'></a>
### SentencePiece
All tokenization algorithms described so far have the same problem: It is assumed that the input text uses spaces to
separate words. However, not all languages use spaces to separate words. One possible solution is to use language
specific pre-tokenizers, *e.g.* [XLM](model_doc/xlm) uses a specific Chinese, Japanese, and Thai pre-tokenizer).
To solve this problem more generally, [SentencePiece: A simple and language independent subword tokenizer and
detokenizer for Neural Text Processing (Kudo et al., 2018)](https://arxiv.org/pdf/1808.06226.pdf) treats the input
as a raw input stream, thus including the space in the set of characters to use. It then uses the BPE or unigram
algorithm to construct the appropriate vocabulary.
The [`XLNetTokenizer`] uses SentencePiece for example, which is also why in the example earlier the
`"▁"` character was included in the vocabulary. Decoding with SentencePiece is very easy since all tokens can just be
concatenated and `"▁"` is replaced by a space.
All transformers models in the library that use SentencePiece use it in combination with unigram. Examples of models
using SentencePiece are [ALBERT](model_doc/albert), [XLNet](model_doc/xlnet), [Marian](model_doc/marian), and [T5](model_doc/t5).
| huggingface/transformers/blob/main/docs/source/en/tokenizer_summary.md |
!--Copyright 2020 The HuggingFace Team. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
the License. You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
specific language governing permissions and limitations under the License.
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rendered properly in your Markdown viewer.
-->
# ConvBERT
<div class="flex flex-wrap space-x-1">
<a href="https://huggingface.co/models?filter=convbert">
<img alt="Models" src="https://img.shields.io/badge/All_model_pages-convbert-blueviolet">
</a>
<a href="https://huggingface.co/spaces/docs-demos/conv-bert-base">
<img alt="Spaces" src="https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Spaces-blue">
</a>
</div>
## Overview
The ConvBERT model was proposed in [ConvBERT: Improving BERT with Span-based Dynamic Convolution](https://arxiv.org/abs/2008.02496) by Zihang Jiang, Weihao Yu, Daquan Zhou, Yunpeng Chen, Jiashi Feng, Shuicheng
Yan.
The abstract from the paper is the following:
*Pre-trained language models like BERT and its variants have recently achieved impressive performance in various
natural language understanding tasks. However, BERT heavily relies on the global self-attention block and thus suffers
large memory footprint and computation cost. Although all its attention heads query on the whole input sequence for
generating the attention map from a global perspective, we observe some heads only need to learn local dependencies,
which means the existence of computation redundancy. We therefore propose a novel span-based dynamic convolution to
replace these self-attention heads to directly model local dependencies. The novel convolution heads, together with the
rest self-attention heads, form a new mixed attention block that is more efficient at both global and local context
learning. We equip BERT with this mixed attention design and build a ConvBERT model. Experiments have shown that
ConvBERT significantly outperforms BERT and its variants in various downstream tasks, with lower training cost and
fewer model parameters. Remarkably, ConvBERTbase model achieves 86.4 GLUE score, 0.7 higher than ELECTRAbase, while
using less than 1/4 training cost. Code and pre-trained models will be released.*
This model was contributed by [abhishek](https://huggingface.co/abhishek). The original implementation can be found
here: https://github.com/yitu-opensource/ConvBert
## Usage tips
ConvBERT training tips are similar to those of BERT. For usage tips refer to [BERT documentation](bert).
## Resources
- [Text classification task guide](../tasks/sequence_classification)
- [Token classification task guide](../tasks/token_classification)
- [Question answering task guide](../tasks/question_answering)
- [Masked language modeling task guide](../tasks/masked_language_modeling)
- [Multiple choice task guide](../tasks/multiple_choice)
## ConvBertConfig
[[autodoc]] ConvBertConfig
## ConvBertTokenizer
[[autodoc]] ConvBertTokenizer
- build_inputs_with_special_tokens
- get_special_tokens_mask
- create_token_type_ids_from_sequences
- save_vocabulary
## ConvBertTokenizerFast
[[autodoc]] ConvBertTokenizerFast
<frameworkcontent>
<pt>
## ConvBertModel
[[autodoc]] ConvBertModel
- forward
## ConvBertForMaskedLM
[[autodoc]] ConvBertForMaskedLM
- forward
## ConvBertForSequenceClassification
[[autodoc]] ConvBertForSequenceClassification
- forward
## ConvBertForMultipleChoice
[[autodoc]] ConvBertForMultipleChoice
- forward
## ConvBertForTokenClassification
[[autodoc]] ConvBertForTokenClassification
- forward
## ConvBertForQuestionAnswering
[[autodoc]] ConvBertForQuestionAnswering
- forward
</pt>
<tf>
## TFConvBertModel
[[autodoc]] TFConvBertModel
- call
## TFConvBertForMaskedLM
[[autodoc]] TFConvBertForMaskedLM
- call
## TFConvBertForSequenceClassification
[[autodoc]] TFConvBertForSequenceClassification
- call
## TFConvBertForMultipleChoice
[[autodoc]] TFConvBertForMultipleChoice
- call
## TFConvBertForTokenClassification
[[autodoc]] TFConvBertForTokenClassification
- call
## TFConvBertForQuestionAnswering
[[autodoc]] TFConvBertForQuestionAnswering
- call
</tf>
</frameworkcontent>
| huggingface/transformers/blob/main/docs/source/en/model_doc/convbert.md |
!--Copyright 2021 The HuggingFace Team. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
the License. You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
specific language governing permissions and limitations under the License.
⚠️ Note that this file is in Markdown but contain specific syntax for our doc-builder (similar to MDX) that may not be
rendered properly in your Markdown viewer.
-->
# Vision Encoder Decoder Models
## Overview
The [`VisionEncoderDecoderModel`] can be used to initialize an image-to-text model with any
pretrained Transformer-based vision model as the encoder (*e.g.* [ViT](vit), [BEiT](beit), [DeiT](deit), [Swin](swin))
and any pretrained language model as the decoder (*e.g.* [RoBERTa](roberta), [GPT2](gpt2), [BERT](bert), [DistilBERT](distilbert)).
The effectiveness of initializing image-to-text-sequence models with pretrained checkpoints has been shown in (for
example) [TrOCR: Transformer-based Optical Character Recognition with Pre-trained Models](https://arxiv.org/abs/2109.10282) by Minghao Li, Tengchao Lv, Lei Cui, Yijuan Lu, Dinei Florencio, Cha Zhang,
Zhoujun Li, Furu Wei.
After such a [`VisionEncoderDecoderModel`] has been trained/fine-tuned, it can be saved/loaded just like any other models (see the examples below
for more information).
An example application is image captioning, in which the encoder is used to encode the image, after which an autoregressive language model generates
the caption. Another example is optical character recognition. Refer to [TrOCR](trocr), which is an instance of [`VisionEncoderDecoderModel`].
## Randomly initializing `VisionEncoderDecoderModel` from model configurations.
[`VisionEncoderDecoderModel`] can be randomly initialized from an encoder and a decoder config. In the following example, we show how to do this using the default [`ViTModel`] configuration for the encoder
and the default [`BertForCausalLM`] configuration for the decoder.
```python
>>> from transformers import BertConfig, ViTConfig, VisionEncoderDecoderConfig, VisionEncoderDecoderModel
>>> config_encoder = ViTConfig()
>>> config_decoder = BertConfig()
>>> config = VisionEncoderDecoderConfig.from_encoder_decoder_configs(config_encoder, config_decoder)
>>> model = VisionEncoderDecoderModel(config=config)
```
## Initialising `VisionEncoderDecoderModel` from a pretrained encoder and a pretrained decoder.
[`VisionEncoderDecoderModel`] can be initialized from a pretrained encoder checkpoint and a pretrained decoder checkpoint. Note that any pretrained Transformer-based vision model, *e.g.* [Swin](swin), can serve as the encoder and both pretrained auto-encoding models, *e.g.* BERT, pretrained causal language models, *e.g.* GPT2, as well as the pretrained decoder part of sequence-to-sequence models, *e.g.* decoder of BART, can be used as the decoder.
Depending on which architecture you choose as the decoder, the cross-attention layers might be randomly initialized.
Initializing [`VisionEncoderDecoderModel`] from a pretrained encoder and decoder checkpoint requires the model to be fine-tuned on a downstream task, as has been shown in [the *Warm-starting-encoder-decoder blog post*](https://huggingface.co/blog/warm-starting-encoder-decoder).
To do so, the `VisionEncoderDecoderModel` class provides a [`VisionEncoderDecoderModel.from_encoder_decoder_pretrained`] method.
```python
>>> from transformers import VisionEncoderDecoderModel
>>> model = VisionEncoderDecoderModel.from_encoder_decoder_pretrained(
... "microsoft/swin-base-patch4-window7-224-in22k", "bert-base-uncased"
... )
```
## Loading an existing `VisionEncoderDecoderModel` checkpoint and perform inference.
To load fine-tuned checkpoints of the `VisionEncoderDecoderModel` class, [`VisionEncoderDecoderModel`] provides the `from_pretrained(...)` method just like any other model architecture in Transformers.
To perform inference, one uses the [`generate`] method, which allows to autoregressively generate text. This method supports various forms of decoding, such as greedy, beam search and multinomial sampling.
```python
>>> import requests
>>> from PIL import Image
>>> from transformers import GPT2TokenizerFast, ViTImageProcessor, VisionEncoderDecoderModel
>>> # load a fine-tuned image captioning model and corresponding tokenizer and image processor
>>> model = VisionEncoderDecoderModel.from_pretrained("nlpconnect/vit-gpt2-image-captioning")
>>> tokenizer = GPT2TokenizerFast.from_pretrained("nlpconnect/vit-gpt2-image-captioning")
>>> image_processor = ViTImageProcessor.from_pretrained("nlpconnect/vit-gpt2-image-captioning")
>>> # let's perform inference on an image
>>> url = "http://images.cocodataset.org/val2017/000000039769.jpg"
>>> image = Image.open(requests.get(url, stream=True).raw)
>>> pixel_values = image_processor(image, return_tensors="pt").pixel_values
>>> # autoregressively generate caption (uses greedy decoding by default)
>>> generated_ids = model.generate(pixel_values)
>>> generated_text = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
>>> print(generated_text)
a cat laying on a blanket next to a cat laying on a bed
```
## Loading a PyTorch checkpoint into `TFVisionEncoderDecoderModel`.
[`TFVisionEncoderDecoderModel.from_pretrained`] currently doesn't support initializing the model from a
PyTorch checkpoint. Passing `from_pt=True` to this method will throw an exception. If there are only PyTorch
checkpoints for a particular vision encoder-decoder model, a workaround is:
```python
>>> from transformers import VisionEncoderDecoderModel, TFVisionEncoderDecoderModel
>>> _model = VisionEncoderDecoderModel.from_pretrained("nlpconnect/vit-gpt2-image-captioning")
>>> _model.encoder.save_pretrained("./encoder")
>>> _model.decoder.save_pretrained("./decoder")
>>> model = TFVisionEncoderDecoderModel.from_encoder_decoder_pretrained(
... "./encoder", "./decoder", encoder_from_pt=True, decoder_from_pt=True
... )
>>> # This is only for copying some specific attributes of this particular model.
>>> model.config = _model.config
```
## Training
Once the model is created, it can be fine-tuned similar to BART, T5 or any other encoder-decoder model on a dataset of (image, text) pairs.
As you can see, only 2 inputs are required for the model in order to compute a loss: `pixel_values` (which are the
images) and `labels` (which are the `input_ids` of the encoded target sequence).
```python
>>> from transformers import ViTImageProcessor, BertTokenizer, VisionEncoderDecoderModel
>>> from datasets import load_dataset
>>> image_processor = ViTImageProcessor.from_pretrained("google/vit-base-patch16-224-in21k")
>>> tokenizer = BertTokenizer.from_pretrained("bert-base-uncased")
>>> model = VisionEncoderDecoderModel.from_encoder_decoder_pretrained(
... "google/vit-base-patch16-224-in21k", "bert-base-uncased"
... )
>>> model.config.decoder_start_token_id = tokenizer.cls_token_id
>>> model.config.pad_token_id = tokenizer.pad_token_id
>>> dataset = load_dataset("huggingface/cats-image")
>>> image = dataset["test"]["image"][0]
>>> pixel_values = image_processor(image, return_tensors="pt").pixel_values
>>> labels = tokenizer(
... "an image of two cats chilling on a couch",
... return_tensors="pt",
... ).input_ids
>>> # the forward function automatically creates the correct decoder_input_ids
>>> loss = model(pixel_values=pixel_values, labels=labels).loss
```
This model was contributed by [nielsr](https://github.com/nielsrogge). This model's TensorFlow and Flax versions
were contributed by [ydshieh](https://github.com/ydshieh).
## VisionEncoderDecoderConfig
[[autodoc]] VisionEncoderDecoderConfig
<frameworkcontent>
<pt>
## VisionEncoderDecoderModel
[[autodoc]] VisionEncoderDecoderModel
- forward
- from_encoder_decoder_pretrained
</pt>
<tf>
## TFVisionEncoderDecoderModel
[[autodoc]] TFVisionEncoderDecoderModel
- call
- from_encoder_decoder_pretrained
</tf>
<jax>
## FlaxVisionEncoderDecoderModel
[[autodoc]] FlaxVisionEncoderDecoderModel
- __call__
- from_encoder_decoder_pretrained
</jax>
</frameworkcontent>
| huggingface/transformers/blob/main/docs/source/en/model_doc/vision-encoder-decoder.md |
Distil*
Author: @VictorSanh
This folder contains the original code used to train Distil* as well as examples showcasing how to use DistilBERT, DistilRoBERTa and DistilGPT2.
**January 20, 2020 - Bug fixing** We have recently discovered and fixed [a bug](https://github.com/huggingface/transformers/commit/48cbf267c988b56c71a2380f748a3e6092ccaed3) in the evaluation of our `run_*.py` scripts that caused the reported metrics to be over-estimated on average. We have updated all the metrics with the latest runs.
**December 6, 2019 - Update** We release **DistilmBERT**: 92% of `bert-base-multilingual-cased` on XNLI. The model supports 104 different languages listed [here](https://github.com/google-research/bert/blob/master/multilingual.md#list-of-languages).
**November 19, 2019 - Update** We release German **DistilBERT**: 98.8% of `bert-base-german-dbmdz-cased` on NER tasks.
**October 23, 2019 - Update** We release **DistilRoBERTa**: 95% of `RoBERTa-base`'s performance on GLUE, twice as fast as RoBERTa while being 35% smaller.
**October 3, 2019 - Update** We release our [NeurIPS workshop paper](https://arxiv.org/abs/1910.01108) explaining our approach on **DistilBERT**. It includes updated results and further experiments. We applied the same method to GPT2 and release the weights of **DistilGPT2**. DistilGPT2 is two times faster and 33% smaller than GPT2. **The paper supersedes our [previous blogpost](https://medium.com/huggingface/distilbert-8cf3380435b5) with a different distillation loss and better performances. Please use the paper as a reference when comparing/reporting results on DistilBERT.**
**September 19, 2019 - Update:** We fixed bugs in the code and released an updated version of the weights trained with a modification of the distillation loss. DistilBERT now reaches 99% of `BERT-base`'s performance on GLUE, and 86.9 F1 score on SQuAD v1.1 dev set (compared to 88.5 for `BERT-base`). We will publish a formal write-up of our approach in the near future!
## What is Distil*
Distil* is a class of compressed models that started with DistilBERT. DistilBERT stands for Distilled-BERT. DistilBERT is a small, fast, cheap and light Transformer model based on Bert architecture. It has 40% less parameters than `bert-base-uncased`, runs 60% faster while preserving 97% of BERT's performances as measured on the GLUE language understanding benchmark. DistilBERT is trained using knowledge distillation, a technique to compress a large model called the teacher into a smaller model called the student. By distillating Bert, we obtain a smaller Transformer model that bears a lot of similarities with the original BERT model while being lighter, smaller and faster to run. DistilBERT is thus an interesting option to put large-scaled trained Transformer model into production.
We have applied the same method to other Transformer architectures and released the weights:
- GPT2: on the [WikiText-103](https://blog.einstein.ai/the-wikitext-long-term-dependency-language-modeling-dataset/) benchmark, GPT2 reaches a perplexity on the test set of 16.3 compared to 21.1 for **DistilGPT2** (after fine-tuning on the train set).
- RoBERTa: **DistilRoBERTa** reaches 95% of `RoBERTa-base`'s performance on GLUE while being twice faster and 35% smaller.
- German BERT: **German DistilBERT** reaches 99% of `bert-base-german-dbmdz-cased`'s performance on German NER (CoNLL-2003).
- Multilingual BERT: **DistilmBERT** reaches 92% of Multilingual BERT's performance on XNLI while being twice faster and 25% smaller. The model supports 104 languages listed [here](https://github.com/google-research/bert/blob/master/multilingual.md#list-of-languages).
For more information on DistilBERT, please refer to our [NeurIPS workshop paper](https://arxiv.org/abs/1910.01108).
Here are the results on the dev sets of GLUE:
| Model | Macro-score | CoLA | MNLI | MRPC | QNLI | QQP | RTE | SST-2| STS-B| WNLI |
| :---: | :---: | :---:| :---:| :---:| :---:| :---:| :---:| :---:| :---:| :---: |
| BERT-base-uncased | **79.5** | 56.3 | 84.7 | 88.6 | 91.8 | 89.6 | 69.3 | 92.7 | 89.0 | 53.5 |
| DistilBERT-base-uncased | **77.0** | 51.3 | 82.1 | 87.5 | 89.2 | 88.5 | 59.9 | 91.3 | 86.9 | 56.3 |
| BERT-base-cased | **78.2** | 58.2 | 83.9 | 87.8 | 91.0 | 89.2 | 66.1 | 91.7 | 89.2 | 46.5 |
| DistilBERT-base-cased | **75.9** | 47.2 | 81.5 | 85.6 | 88.2 | 87.8 | 60.6 | 90.4 | 85.5 | 56.3 |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| RoBERTa-base (reported) | **83.2**/**86.4**<sup>2</sup> | 63.6 | 87.6 | 90.2 | 92.8 | 91.9 | 78.7 | 94.8 | 91.2 | 57.7<sup>3</sup> |
| DistilRoBERTa<sup>1</sup> | **79.0**/**82.3**<sup>2</sup> | 59.3 | 84.0 | 86.6 | 90.8 | 89.4 | 67.9 | 92.5 | 88.3 | 52.1 |
<sup>1</sup> We did not use the MNLI checkpoint for fine-tuning but directly perform transfer learning on the pre-trained DistilRoBERTa.
<sup>2</sup> Macro-score computed without WNLI.
<sup>3</sup> We compute this score ourselves for completeness.
Here are the results on the *test* sets for 6 of the languages available in XNLI. The results are computed in the zero shot setting (trained on the English portion and evaluated on the target language portion):
| Model | English | Spanish | Chinese | German | Arabic | Urdu |
| :---: | :---: | :---: | :---: | :---: | :---: | :---:|
| mBERT base cased (computed) | 82.1 | 74.6 | 69.1 | 72.3 | 66.4 | 58.5 |
| mBERT base uncased (reported)| 81.4 | 74.3 | 63.8 | 70.5 | 62.1 | 58.3 |
| DistilmBERT | 78.2 | 69.1 | 64.0 | 66.3 | 59.1 | 54.7 |
## Setup
This part of the library has only be tested with Python3.6+. There are few specific dependencies to install before launching a distillation, you can install them with the command `pip install -r requirements.txt`.
**Important note:** The training scripts have been updated to support PyTorch v1.2.0 (there are breaking changes compared to v1.1.0).
## How to use DistilBERT
Transformers includes five pre-trained Distil* models, currently only provided for English and German (we are investigating the possibility to train and release a multilingual version of DistilBERT):
- `distilbert-base-uncased`: DistilBERT English language model pretrained on the same data used to pretrain Bert (concatenation of the Toronto Book Corpus and full English Wikipedia) using distillation with the supervision of the `bert-base-uncased` version of Bert. The model has 6 layers, 768 dimension and 12 heads, totalizing 66M parameters.
- `distilbert-base-uncased-distilled-squad`: A finetuned version of `distilbert-base-uncased` finetuned using (a second step of) knowledge distillation on SQuAD 1.0. This model reaches a F1 score of 86.9 on the dev set (for comparison, Bert `bert-base-uncased` version reaches a 88.5 F1 score).
- `distilbert-base-cased`: DistilBERT English language model pretrained on the same data used to pretrain Bert (concatenation of the Toronto Book Corpus and full English Wikipedia) using distillation with the supervision of the `bert-base-cased` version of Bert. The model has 6 layers, 768 dimension and 12 heads, totalizing 65M parameters.
- `distilbert-base-cased-distilled-squad`: A finetuned version of `distilbert-base-cased` finetuned using (a second step of) knowledge distillation on SQuAD 1.0. This model reaches a F1 score of 87.1 on the dev set (for comparison, Bert `bert-base-cased` version reaches a 88.7 F1 score).
- `distilbert-base-german-cased`: DistilBERT German language model pretrained on 1/2 of the data used to pretrain Bert using distillation with the supervision of the `bert-base-german-dbmdz-cased` version of German DBMDZ Bert. For NER tasks the model reaches a F1 score of 83.49 on the CoNLL-2003 test set (for comparison, `bert-base-german-dbmdz-cased` reaches a 84.52 F1 score), and a F1 score of 85.23 on the GermEval 2014 test set (`bert-base-german-dbmdz-cased` reaches a 86.89 F1 score).
- `distilgpt2`: DistilGPT2 English language model pretrained with the supervision of `gpt2` (the smallest version of GPT2) on [OpenWebTextCorpus](https://skylion007.github.io/OpenWebTextCorpus/), a reproduction of OpenAI's WebText dataset. The model has 6 layers, 768 dimension and 12 heads, totalizing 82M parameters (compared to 124M parameters for GPT2). On average, DistilGPT2 is two times faster than GPT2.
- `distilroberta-base`: DistilRoBERTa English language model pretrained with the supervision of `roberta-base` solely on [OpenWebTextCorpus](https://skylion007.github.io/OpenWebTextCorpus/), a reproduction of OpenAI's WebText dataset (it is ~4 times less training data than the teacher RoBERTa). The model has 6 layers, 768 dimension and 12 heads, totalizing 82M parameters (compared to 125M parameters for RoBERTa-base). On average DistilRoBERTa is twice as fast as Roberta-base.
- `distilbert-base-multilingual-cased`: DistilmBERT multilingual model pretrained with the supervision of `bert-base-multilingual-cased` on the concatenation of Wikipedia in 104 different languages. The model supports the 104 languages listed [here](https://github.com/google-research/bert/blob/master/multilingual.md#list-of-languages). The model has 6 layers, 768 dimension and 12 heads, totalizing 134M parameters (compared to 177M parameters for mBERT-base). On average DistilmBERT is twice as fast as mBERT-base.
Using DistilBERT is very similar to using BERT. DistilBERT share the same tokenizer as BERT's `bert-base-uncased` even though we provide a link to this tokenizer under the `DistilBertTokenizer` name to have a consistent naming between the library models.
```python
tokenizer = DistilBertTokenizer.from_pretrained('distilbert-base-cased')
model = DistilBertModel.from_pretrained('distilbert-base-cased')
input_ids = torch.tensor(tokenizer.encode("Hello, my dog is cute")).unsqueeze(0)
outputs = model(input_ids)
last_hidden_states = outputs[0] # The last hidden-state is the first element of the output tuple
```
Similarly, using the other Distil* models simply consists in calling the base classes with a different pretrained checkpoint:
- DistilBERT uncased: `model = DistilBertModel.from_pretrained('distilbert-base-uncased')`
- DistilGPT2: `model = GPT2Model.from_pretrained('distilgpt2')`
- DistilRoBERTa: `model = RobertaModel.from_pretrained('distilroberta-base')`
- DistilmBERT: `model = DistilBertModel.from_pretrained('distilbert-base-multilingual-cased')`
## How to train Distil*
In the following, we will explain how you can train DistilBERT.
### A. Preparing the data
The weights we release are trained using a concatenation of Toronto Book Corpus and English Wikipedia (same training data as the English version of BERT).
To avoid processing the data several time, we do it once and for all before the training. From now on, will suppose that you have a text file `dump.txt` which contains one sequence per line (a sequence being composed of one of several coherent sentences).
First, we will binarize the data, i.e. tokenize the data and convert each token in an index in our model's vocabulary.
```bash
python scripts/binarized_data.py \
--file_path data/dump.txt \
--tokenizer_type bert \
--tokenizer_name bert-base-uncased \
--dump_file data/binarized_text
```
Our implementation of masked language modeling loss follows [XLM](https://github.com/facebookresearch/XLM)'s one and smooths the probability of masking with a factor that put more emphasis on rare words. Thus we count the occurrences of each tokens in the data:
```bash
python scripts/token_counts.py \
--data_file data/binarized_text.bert-base-uncased.pickle \
--token_counts_dump data/token_counts.bert-base-uncased.pickle \
--vocab_size 30522
```
### B. Training
Training with distillation is really simple once you have pre-processed the data:
```bash
python train.py \
--student_type distilbert \
--student_config training_configs/distilbert-base-uncased.json \
--teacher_type bert \
--teacher_name bert-base-uncased \
--alpha_ce 5.0 --alpha_mlm 2.0 --alpha_cos 1.0 --alpha_clm 0.0 --mlm \
--freeze_pos_embs \
--dump_path serialization_dir/my_first_training \
--data_file data/binarized_text.bert-base-uncased.pickle \
--token_counts data/token_counts.bert-base-uncased.pickle \
--force # overwrites the `dump_path` if it already exists.
```
By default, this will launch a training on a single GPU (even if more are available on the cluster). Other parameters are available in the command line, please look in `train.py` or run `python train.py --help` to list them.
We highly encourage you to use distributed training for training DistilBERT as the training corpus is quite large. Here's an example that runs a distributed training on a single node having 4 GPUs:
```bash
export NODE_RANK=0
export N_NODES=1
export N_GPU_NODE=4
export WORLD_SIZE=4
export MASTER_PORT=<AN_OPEN_PORT>
export MASTER_ADDR=<I.P.>
pkill -f 'python -u train.py'
python -m torch.distributed.launch \
--nproc_per_node=$N_GPU_NODE \
--nnodes=$N_NODES \
--node_rank $NODE_RANK \
--master_addr $MASTER_ADDR \
--master_port $MASTER_PORT \
train.py \
--force \
--n_gpu $WORLD_SIZE \
--student_type distilbert \
--student_config training_configs/distilbert-base-uncased.json \
--teacher_type bert \
--teacher_name bert-base-uncased \
--alpha_ce 0.33 --alpha_mlm 0.33 --alpha_cos 0.33 --alpha_clm 0.0 --mlm \
--freeze_pos_embs \
--dump_path serialization_dir/my_first_training \
--data_file data/binarized_text.bert-base-uncased.pickle \
--token_counts data/token_counts.bert-base-uncased.pickle
```
**Tips:** Starting distilled training with good initialization of the model weights is crucial to reach decent performance. In our experiments, we initialized our model from a few layers of the teacher (Bert) itself! Please refer to `scripts/extract.py` and `scripts/extract_distilbert.py` to create a valid initialization checkpoint and use `--student_pretrained_weights` argument to use this initialization for the distilled training!
Happy distillation!
## Citation
If you find the resource useful, you should cite the following paper:
```
@inproceedings{sanh2019distilbert,
title={DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter},
author={Sanh, Victor and Debut, Lysandre and Chaumond, Julien and Wolf, Thomas},
booktitle={NeurIPS EMC^2 Workshop},
year={2019}
}
```
| huggingface/transformers/blob/main/examples/research_projects/distillation/README.md |
How to propose a Flax/JAX + Transformers project
Great that you've opened this document!
While we at 🤗 are proposing a couple of projects, we strongly
believe that the community can come up with much more **creative**, **fun**, and
**impactful** projects on their own. This being said, we are really looking forward
to seeing your project proposal!
## What a project should be about
The proposed project should fall into the machine learning fields of **Natural Language Processing (NLP)** and/or **Computer Vision (CV)** (possibly also **Speech Recognition (ASR)** depending on whether Speech Recognition models are available in Flax in due time) and aim at solving a specific task.
Possible tasks can belong to:
* text classification
* text generation
* image recognition
* image processing
* image captioning
* audio classification
* and other tasks you can think of!
The clearer a task is defined, the better your project proposal is.
*E.g.* "Using a T5 model to learn grammar correction in French" or "Adapting a pre-trained CLIP model for zero-shot image classification in Spanish" are **well-defined and clear** project proposals, while something like "Train a language model" or "Image classification" are **too vague**.
There is no limit to your creativity as long as the project is feasible and ethical.
The more creative & specific your project proposal, the more interesting it will be,
and the more likely will you find motivated team members to work on your project!
To get an idea of how to formulate your project proposals, you can browse through
existing project proposals on the [forum](https://discuss.huggingface.co/c/flax-jax-projects/22).
## How to submit a project proposal
First, you should make sure that you are [logged in](https://huggingface.co/login?sso=bm9uY2U9OTRlNjZjZmZhYjMwMmJmMWMyYjc5MmFiMTMyMzY5ODYmcmV0dXJuX3Nzb191cmw9aHR0cHMlM0ElMkYlMkZkaXNjdXNzLmh1Z2dpbmdmYWNlLmNvJTJGc2Vzc2lvbiUyRnNzb19sb2dpbg%3D%3D&sig=429ad8924bcb33c40f9823027ea749abb55d393f4f58924f36a2dba3ab0a48da) with your Hugging Face account on the forum.
Second, make sure that your project idea doesn't already exist by checking [existing projects](https://discuss.huggingface.co/c/flax-jax-projects/22).
If your project already exists - great! This means that you can comment and improve
the existing idea and join the project to form a team! If your project idea already
exists for a different language, feel free to submit the same project idea, just in
a different language.
Third, having ensured that your project doesn't exist, click on the *"New Topic"*
button on the [Flax/JAX Projects Forum category](https://discuss.huggingface.co/c/flax-jax-projects/22) to create a new project proposal.
Fourth, make sure that your project proposal includes the following information:
1. *A clear description of the project*
2. *In which language should the project be conducted?* English, German, Chinese, ...? It can also be a multi-lingual project
3. *Which model should be used?* If you want to adapt an existing model, you can add the link to one of the 4000 available checkpoints in JAX [here](https://huggingface.co/models?filter=jax) If you want to train a model from scratch, you can simply state the model architecture to be used, *e.g.* BERT, CLIP, etc. You can also base your project on a model that is not part of transformers. For an overview of libraries based on JAX, you can take a look at [awesome-jax](https://github.com/n2cholas/awesome-jax#awesome-jax-). **Note** that for a project that is not based on Transformers it will be more difficult for the 🤗 team to help you. Also have a look at the section [Quickstart Flax & Jax in Transformers](https://github.com/huggingface/transformers/tree/main/examples/research_projects/jax-projects#quickstart-flax-and-jax-in-transformers) to see what model architectures are currently supported in 🤗 Transformers.
4. *What data should be used?* It is important to state at least what kind of data you would like to use. Ideally, you can already point to publicly available data or a dataset in the 🤗 Datasets library.
5. *Are similar training scripts available in Flax/JAX?* It would be important to find similar training scripts that already exist in Flax/JAX. *E.g.* if you are working on a Seq-to-Seq task, you can make use of the [`run_summarization_flax.py`](https://github.com/huggingface/transformers/blob/main/examples/flax/summarization/run_summarization_flax.py) script which is very similar to any seq2seq training. Also have a look at the section [Quickstart Flax & Jax in Transformers](https://github.com/huggingface/transformers/tree/main/examples/research_projects/jax-projects#quickstart-flax-and-jax-in-transformers) to see what training scripts are currently supported in 🤗 Transformers.
6. *(Optionally) What are possible challenges?* List possible difficulties with your project. *E.g.* If you know that training convergence usually takes a lot of time, it is worth stating this here!
7. *(Optionally) What is the desired project outcome?* - How would you like to demo your project? One could *e.g.* create a Streamlit application.
8. *(Optionally) Links to read upon* - Can you provide any links that would help the reader to better understand your project idea?
Feel free to copy-paste the following format for your project proposal and fill out the respective sections:
```
# <FILL ME: Name of project>
<FILL ME: A clear description of the project>
## 2. Language
The model will be trained in <FILL ME: which language?>.
## 3. Model
<FILL ME: 3. Which model should be used?>
## 4. Datasets
<FILL ME: 4. Which data should be used?>
Possible links to publicly available datasets include:
- <FILL ME: Link 1 to dataset>
- <FILL ME: Link 2 to dataset>
- <FILL ME: Link 3 to dataset>
## 5. Training scripts
<FILL ME: 5. Are there publicly available training scripts that can be used/tweaked for the project?>
We can make use of <FILL ME: link to training script> to train the model.>
## 6. (Optional) Challenges
<(Optionally) FILL ME: 6. What are possible challenges?>
## 7. (Optional) Desired project outcome
<(Optionally) FILL ME: 7. What is the desired project outcome? A demo?>
## 8. (Optional) Reads
The following links can be useful to better understand the project and
what has previously been done.
- <FILL ME: Link 1 to read>
- <FILL ME: Link 2 to read>
- <FILL ME: Link 3 to read>
```
To see how a proposed project looks like, please have a look at submitted project
proposals [here](https://discuss.huggingface.co/c/flax-jax-projects/22).
## Will my project proposal be selected?
Having submitted a project proposal, you can now promote your idea in the Slack channel `#flax-jax-community-week` to try to convince other participants to join your project!
Once other people have joined your project, one of the organizers (`@Suzana, @valhalla, @osanseviero, @patrickvonplaten`) will officially create a team for your project and add your project to [this google sheet](https://docs.google.com/spreadsheets/d/1GpHebL7qrwJOc9olTpIPgjf8vOS0jNb6zR_B8x_Jtik/edit?usp=sharing).
| huggingface/transformers/blob/main/examples/research_projects/jax-projects/HOW_TO_PROPOSE_PROJECT.md |
!--Copyright 2022 The HuggingFace Team. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
the License. You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
specific language governing permissions and limitations under the License.
⚠️ Note that this file is in Markdown but contain specific syntax for our doc-builder (similar to MDX) that may not be
rendered properly in your Markdown viewer.
-->
# MVP
## Overview
The MVP model was proposed in [MVP: Multi-task Supervised Pre-training for Natural Language Generation](https://arxiv.org/abs/2206.12131) by Tianyi Tang, Junyi Li, Wayne Xin Zhao and Ji-Rong Wen.
According to the abstract,
- MVP follows a standard Transformer encoder-decoder architecture.
- MVP is supervised pre-trained using labeled datasets.
- MVP also has task-specific soft prompts to stimulate the model's capacity in performing a certain task.
- MVP is specially designed for natural language generation and can be adapted to a wide range of generation tasks, including but not limited to summarization, data-to-text generation, open-ended dialogue system, story generation, question answering, question generation, task-oriented dialogue system, commonsense generation, paraphrase generation, text style transfer, and text simplification. Our model can also be adapted to natural language understanding tasks such as sequence classification and (extractive) question answering.
This model was contributed by [Tianyi Tang](https://huggingface.co/StevenTang). The detailed information and instructions can be found [here](https://github.com/RUCAIBox/MVP).
## Usage tips
- We have released a series of models [here](https://huggingface.co/models?filter=mvp), including MVP, MVP with task-specific prompts, and multi-task pre-trained variants.
- If you want to use a model without prompts (standard Transformer), you can load it through `MvpForConditionalGeneration.from_pretrained('RUCAIBox/mvp')`.
- If you want to use a model with task-specific prompts, such as summarization, you can load it through `MvpForConditionalGeneration.from_pretrained('RUCAIBox/mvp-summarization')`.
- Our model supports lightweight prompt tuning following [Prefix-tuning](https://arxiv.org/abs/2101.00190) with method `set_lightweight_tuning()`.
## Usage examples
For summarization, it is an example to use MVP and MVP with summarization-specific prompts.
```python
>>> from transformers import MvpTokenizer, MvpForConditionalGeneration
>>> tokenizer = MvpTokenizer.from_pretrained("RUCAIBox/mvp")
>>> model = MvpForConditionalGeneration.from_pretrained("RUCAIBox/mvp")
>>> model_with_prompt = MvpForConditionalGeneration.from_pretrained("RUCAIBox/mvp-summarization")
>>> inputs = tokenizer(
... "Summarize: You may want to stick it to your boss and leave your job, but don't do it if these are your reasons.",
... return_tensors="pt",
... )
>>> generated_ids = model.generate(**inputs)
>>> tokenizer.batch_decode(generated_ids, skip_special_tokens=True)
["Why You Shouldn't Quit Your Job"]
>>> generated_ids = model_with_prompt.generate(**inputs)
>>> tokenizer.batch_decode(generated_ids, skip_special_tokens=True)
["Don't do it if these are your reasons"]
```
For data-to-text generation, it is an example to use MVP and multi-task pre-trained variants.
```python
>>> from transformers import MvpTokenizerFast, MvpForConditionalGeneration
>>> tokenizer = MvpTokenizerFast.from_pretrained("RUCAIBox/mvp")
>>> model = MvpForConditionalGeneration.from_pretrained("RUCAIBox/mvp")
>>> model_with_mtl = MvpForConditionalGeneration.from_pretrained("RUCAIBox/mtl-data-to-text")
>>> inputs = tokenizer(
... "Describe the following data: Iron Man | instance of | Superhero [SEP] Stan Lee | creator | Iron Man",
... return_tensors="pt",
... )
>>> generated_ids = model.generate(**inputs)
>>> tokenizer.batch_decode(generated_ids, skip_special_tokens=True)
['Stan Lee created the character of Iron Man, a fictional superhero appearing in American comic']
>>> generated_ids = model_with_mtl.generate(**inputs)
>>> tokenizer.batch_decode(generated_ids, skip_special_tokens=True)
['Iron Man is a fictional superhero appearing in American comic books published by Marvel Comics.']
```
For lightweight tuning, *i.e.*, fixing the model and only tuning prompts, you can load MVP with randomly initialized prompts or with task-specific prompts. Our code also supports Prefix-tuning with BART following the [original paper](https://arxiv.org/abs/2101.00190).
```python
>>> from transformers import MvpForConditionalGeneration
>>> model = MvpForConditionalGeneration.from_pretrained("RUCAIBox/mvp", use_prompt=True)
>>> # the number of trainable parameters (full tuning)
>>> sum(p.numel() for p in model.parameters() if p.requires_grad)
468116832
>>> # lightweight tuning with randomly initialized prompts
>>> model.set_lightweight_tuning()
>>> # the number of trainable parameters (lightweight tuning)
>>> sum(p.numel() for p in model.parameters() if p.requires_grad)
61823328
>>> # lightweight tuning with task-specific prompts
>>> model = MvpForConditionalGeneration.from_pretrained("RUCAIBox/mtl-data-to-text")
>>> model.set_lightweight_tuning()
>>> # original lightweight Prefix-tuning
>>> model = MvpForConditionalGeneration.from_pretrained("facebook/bart-large", use_prompt=True)
>>> model.set_lightweight_tuning()
```
## Resources
- [Text classification task guide](../tasks/sequence_classification)
- [Question answering task guide](../tasks/question_answering)
- [Causal language modeling task guide](../tasks/language_modeling)
- [Masked language modeling task guide](../tasks/masked_language_modeling)
- [Translation task guide](../tasks/translation)
- [Summarization task guide](../tasks/summarization)
## MvpConfig
[[autodoc]] MvpConfig
## MvpTokenizer
[[autodoc]] MvpTokenizer
## MvpTokenizerFast
[[autodoc]] MvpTokenizerFast
## MvpModel
[[autodoc]] MvpModel
- forward
## MvpForConditionalGeneration
[[autodoc]] MvpForConditionalGeneration
- forward
## MvpForSequenceClassification
[[autodoc]] MvpForSequenceClassification
- forward
## MvpForQuestionAnswering
[[autodoc]] MvpForQuestionAnswering
- forward
## MvpForCausalLM
[[autodoc]] MvpForCausalLM
- forward
| huggingface/transformers/blob/main/docs/source/en/model_doc/mvp.md |
!--Copyright 2021 The HuggingFace Team. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
the License. You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
specific language governing permissions and limitations under the License.
⚠️ Note that this file is in Markdown but contain specific syntax for our doc-builder (similar to MDX) that may not be
rendered properly in your Markdown viewer.
-->
# BigBirdPegasus
## Overview
The BigBird model was proposed in [Big Bird: Transformers for Longer Sequences](https://arxiv.org/abs/2007.14062) by
Zaheer, Manzil and Guruganesh, Guru and Dubey, Kumar Avinava and Ainslie, Joshua and Alberti, Chris and Ontanon,
Santiago and Pham, Philip and Ravula, Anirudh and Wang, Qifan and Yang, Li and others. BigBird, is a sparse-attention
based transformer which extends Transformer based models, such as BERT to much longer sequences. In addition to sparse
attention, BigBird also applies global attention as well as random attention to the input sequence. Theoretically, it
has been shown that applying sparse, global, and random attention approximates full attention, while being
computationally much more efficient for longer sequences. As a consequence of the capability to handle longer context,
BigBird has shown improved performance on various long document NLP tasks, such as question answering and
summarization, compared to BERT or RoBERTa.
The abstract from the paper is the following:
*Transformers-based models, such as BERT, have been one of the most successful deep learning models for NLP.
Unfortunately, one of their core limitations is the quadratic dependency (mainly in terms of memory) on the sequence
length due to their full attention mechanism. To remedy this, we propose, BigBird, a sparse attention mechanism that
reduces this quadratic dependency to linear. We show that BigBird is a universal approximator of sequence functions and
is Turing complete, thereby preserving these properties of the quadratic, full attention model. Along the way, our
theoretical analysis reveals some of the benefits of having O(1) global tokens (such as CLS), that attend to the entire
sequence as part of the sparse attention mechanism. The proposed sparse attention can handle sequences of length up to
8x of what was previously possible using similar hardware. As a consequence of the capability to handle longer context,
BigBird drastically improves performance on various NLP tasks such as question answering and summarization. We also
propose novel applications to genomics data.*
The original code can be found [here](https://github.com/google-research/bigbird).
## Usage tips
- For an in-detail explanation on how BigBird's attention works, see [this blog post](https://huggingface.co/blog/big-bird).
- BigBird comes with 2 implementations: **original_full** & **block_sparse**. For the sequence length < 1024, using
**original_full** is advised as there is no benefit in using **block_sparse** attention.
- The code currently uses window size of 3 blocks and 2 global blocks.
- Sequence length must be divisible by block size.
- Current implementation supports only **ITC**.
- Current implementation doesn't support **num_random_blocks = 0**.
- BigBirdPegasus uses the [PegasusTokenizer](https://github.com/huggingface/transformers/blob/main/src/transformers/models/pegasus/tokenization_pegasus.py).
- BigBird is a model with absolute position embeddings so it's usually advised to pad the inputs on the right rather than
the left.
## Resources
- [Text classification task guide](../tasks/sequence_classification)
- [Question answering task guide](../tasks/question_answering)
- [Causal language modeling task guide](../tasks/language_modeling)
- [Translation task guide](../tasks/translation)
- [Summarization task guide](../tasks/summarization)
## BigBirdPegasusConfig
[[autodoc]] BigBirdPegasusConfig
- all
## BigBirdPegasusModel
[[autodoc]] BigBirdPegasusModel
- forward
## BigBirdPegasusForConditionalGeneration
[[autodoc]] BigBirdPegasusForConditionalGeneration
- forward
## BigBirdPegasusForSequenceClassification
[[autodoc]] BigBirdPegasusForSequenceClassification
- forward
## BigBirdPegasusForQuestionAnswering
[[autodoc]] BigBirdPegasusForQuestionAnswering
- forward
## BigBirdPegasusForCausalLM
[[autodoc]] BigBirdPegasusForCausalLM
- forward
| huggingface/transformers/blob/main/docs/source/en/model_doc/bigbird_pegasus.md |
!--Copyright 2023 The HuggingFace Team. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
the License. You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
specific language governing permissions and limitations under the License.
⚠️ Note that this file is in Markdown but contain specific syntax for our doc-builder (similar to MDX) that may not be
rendered properly in your Markdown viewer.
-->
# Automatic speech recognition
[[open-in-colab]]
<Youtube id="TksaY_FDgnk"/>
Automatic speech recognition (ASR) converts a speech signal to text, mapping a sequence of audio inputs to text outputs. Virtual assistants like Siri and Alexa use ASR models to help users everyday, and there are many other useful user-facing applications like live captioning and note-taking during meetings.
This guide will show you how to:
1. Finetune [Wav2Vec2](https://huggingface.co/facebook/wav2vec2-base) on the [MInDS-14](https://huggingface.co/datasets/PolyAI/minds14) dataset to transcribe audio to text.
2. Use your finetuned model for inference.
<Tip>
The task illustrated in this tutorial is supported by the following model architectures:
<!--This tip is automatically generated by `make fix-copies`, do not fill manually!-->
[Data2VecAudio](../model_doc/data2vec-audio), [Hubert](../model_doc/hubert), [M-CTC-T](../model_doc/mctct), [SEW](../model_doc/sew), [SEW-D](../model_doc/sew-d), [UniSpeech](../model_doc/unispeech), [UniSpeechSat](../model_doc/unispeech-sat), [Wav2Vec2](../model_doc/wav2vec2), [Wav2Vec2-Conformer](../model_doc/wav2vec2-conformer), [WavLM](../model_doc/wavlm)
<!--End of the generated tip-->
</Tip>
Before you begin, make sure you have all the necessary libraries installed:
```bash
pip install transformers datasets evaluate jiwer
```
We encourage you to login to your Hugging Face account so you can upload and share your model with the community. When prompted, enter your token to login:
```py
>>> from huggingface_hub import notebook_login
>>> notebook_login()
```
## Load MInDS-14 dataset
Start by loading a smaller subset of the [MInDS-14](https://huggingface.co/datasets/PolyAI/minds14) dataset from the 🤗 Datasets library. This'll give you a chance to experiment and make sure everything works before spending more time training on the full dataset.
```py
>>> from datasets import load_dataset, Audio
>>> minds = load_dataset("PolyAI/minds14", name="en-US", split="train[:100]")
```
Split the dataset's `train` split into a train and test set with the [`~Dataset.train_test_split`] method:
```py
>>> minds = minds.train_test_split(test_size=0.2)
```
Then take a look at the dataset:
```py
>>> minds
DatasetDict({
train: Dataset({
features: ['path', 'audio', 'transcription', 'english_transcription', 'intent_class', 'lang_id'],
num_rows: 16
})
test: Dataset({
features: ['path', 'audio', 'transcription', 'english_transcription', 'intent_class', 'lang_id'],
num_rows: 4
})
})
```
While the dataset contains a lot of useful information, like `lang_id` and `english_transcription`, you'll focus on the `audio` and `transcription` in this guide. Remove the other columns with the [`~datasets.Dataset.remove_columns`] method:
```py
>>> minds = minds.remove_columns(["english_transcription", "intent_class", "lang_id"])
```
Take a look at the example again:
```py
>>> minds["train"][0]
{'audio': {'array': array([-0.00024414, 0. , 0. , ..., 0.00024414,
0.00024414, 0.00024414], dtype=float32),
'path': '/root/.cache/huggingface/datasets/downloads/extracted/f14948e0e84be638dd7943ac36518a4cf3324e8b7aa331c5ab11541518e9368c/en-US~APP_ERROR/602ba9e2963e11ccd901cd4f.wav',
'sampling_rate': 8000},
'path': '/root/.cache/huggingface/datasets/downloads/extracted/f14948e0e84be638dd7943ac36518a4cf3324e8b7aa331c5ab11541518e9368c/en-US~APP_ERROR/602ba9e2963e11ccd901cd4f.wav',
'transcription': "hi I'm trying to use the banking app on my phone and currently my checking and savings account balance is not refreshing"}
```
There are two fields:
- `audio`: a 1-dimensional `array` of the speech signal that must be called to load and resample the audio file.
- `transcription`: the target text.
## Preprocess
The next step is to load a Wav2Vec2 processor to process the audio signal:
```py
>>> from transformers import AutoProcessor
>>> processor = AutoProcessor.from_pretrained("facebook/wav2vec2-base")
```
The MInDS-14 dataset has a sampling rate of 8000kHz (you can find this information in its [dataset card](https://huggingface.co/datasets/PolyAI/minds14)), which means you'll need to resample the dataset to 16000kHz to use the pretrained Wav2Vec2 model:
```py
>>> minds = minds.cast_column("audio", Audio(sampling_rate=16_000))
>>> minds["train"][0]
{'audio': {'array': array([-2.38064706e-04, -1.58618059e-04, -5.43987835e-06, ...,
2.78103951e-04, 2.38446111e-04, 1.18740834e-04], dtype=float32),
'path': '/root/.cache/huggingface/datasets/downloads/extracted/f14948e0e84be638dd7943ac36518a4cf3324e8b7aa331c5ab11541518e9368c/en-US~APP_ERROR/602ba9e2963e11ccd901cd4f.wav',
'sampling_rate': 16000},
'path': '/root/.cache/huggingface/datasets/downloads/extracted/f14948e0e84be638dd7943ac36518a4cf3324e8b7aa331c5ab11541518e9368c/en-US~APP_ERROR/602ba9e2963e11ccd901cd4f.wav',
'transcription': "hi I'm trying to use the banking app on my phone and currently my checking and savings account balance is not refreshing"}
```
As you can see in the `transcription` above, the text contains a mix of upper and lowercase characters. The Wav2Vec2 tokenizer is only trained on uppercase characters so you'll need to make sure the text matches the tokenizer's vocabulary:
```py
>>> def uppercase(example):
... return {"transcription": example["transcription"].upper()}
>>> minds = minds.map(uppercase)
```
Now create a preprocessing function that:
1. Calls the `audio` column to load and resample the audio file.
2. Extracts the `input_values` from the audio file and tokenize the `transcription` column with the processor.
```py
>>> def prepare_dataset(batch):
... audio = batch["audio"]
... batch = processor(audio["array"], sampling_rate=audio["sampling_rate"], text=batch["transcription"])
... batch["input_length"] = len(batch["input_values"][0])
... return batch
```
To apply the preprocessing function over the entire dataset, use 🤗 Datasets [`~datasets.Dataset.map`] function. You can speed up `map` by increasing the number of processes with the `num_proc` parameter. Remove the columns you don't need with the [`~datasets.Dataset.remove_columns`] method:
```py
>>> encoded_minds = minds.map(prepare_dataset, remove_columns=minds.column_names["train"], num_proc=4)
```
🤗 Transformers doesn't have a data collator for ASR, so you'll need to adapt the [`DataCollatorWithPadding`] to create a batch of examples. It'll also dynamically pad your text and labels to the length of the longest element in its batch (instead of the entire dataset) so they are a uniform length. While it is possible to pad your text in the `tokenizer` function by setting `padding=True`, dynamic padding is more efficient.
Unlike other data collators, this specific data collator needs to apply a different padding method to `input_values` and `labels`:
```py
>>> import torch
>>> from dataclasses import dataclass, field
>>> from typing import Any, Dict, List, Optional, Union
>>> @dataclass
... class DataCollatorCTCWithPadding:
... processor: AutoProcessor
... padding: Union[bool, str] = "longest"
... def __call__(self, features: List[Dict[str, Union[List[int], torch.Tensor]]]) -> Dict[str, torch.Tensor]:
... # split inputs and labels since they have to be of different lengths and need
... # different padding methods
... input_features = [{"input_values": feature["input_values"][0]} for feature in features]
... label_features = [{"input_ids": feature["labels"]} for feature in features]
... batch = self.processor.pad(input_features, padding=self.padding, return_tensors="pt")
... labels_batch = self.processor.pad(labels=label_features, padding=self.padding, return_tensors="pt")
... # replace padding with -100 to ignore loss correctly
... labels = labels_batch["input_ids"].masked_fill(labels_batch.attention_mask.ne(1), -100)
... batch["labels"] = labels
... return batch
```
Now instantiate your `DataCollatorForCTCWithPadding`:
```py
>>> data_collator = DataCollatorCTCWithPadding(processor=processor, padding="longest")
```
## Evaluate
Including a metric during training is often helpful for evaluating your model's performance. You can quickly load a evaluation method with the 🤗 [Evaluate](https://huggingface.co/docs/evaluate/index) library. For this task, load the [word error rate](https://huggingface.co/spaces/evaluate-metric/wer) (WER) metric (see the 🤗 Evaluate [quick tour](https://huggingface.co/docs/evaluate/a_quick_tour) to learn more about how to load and compute a metric):
```py
>>> import evaluate
>>> wer = evaluate.load("wer")
```
Then create a function that passes your predictions and labels to [`~evaluate.EvaluationModule.compute`] to calculate the WER:
```py
>>> import numpy as np
>>> def compute_metrics(pred):
... pred_logits = pred.predictions
... pred_ids = np.argmax(pred_logits, axis=-1)
... pred.label_ids[pred.label_ids == -100] = processor.tokenizer.pad_token_id
... pred_str = processor.batch_decode(pred_ids)
... label_str = processor.batch_decode(pred.label_ids, group_tokens=False)
... wer = wer.compute(predictions=pred_str, references=label_str)
... return {"wer": wer}
```
Your `compute_metrics` function is ready to go now, and you'll return to it when you setup your training.
## Train
<frameworkcontent>
<pt>
<Tip>
If you aren't familiar with finetuning a model with the [`Trainer`], take a look at the basic tutorial [here](../training#train-with-pytorch-trainer)!
</Tip>
You're ready to start training your model now! Load Wav2Vec2 with [`AutoModelForCTC`]. Specify the reduction to apply with the `ctc_loss_reduction` parameter. It is often better to use the average instead of the default summation:
```py
>>> from transformers import AutoModelForCTC, TrainingArguments, Trainer
>>> model = AutoModelForCTC.from_pretrained(
... "facebook/wav2vec2-base",
... ctc_loss_reduction="mean",
... pad_token_id=processor.tokenizer.pad_token_id,
... )
```
At this point, only three steps remain:
1. Define your training hyperparameters in [`TrainingArguments`]. The only required parameter is `output_dir` which specifies where to save your model. You'll push this model to the Hub by setting `push_to_hub=True` (you need to be signed in to Hugging Face to upload your model). At the end of each epoch, the [`Trainer`] will evaluate the WER and save the training checkpoint.
2. Pass the training arguments to [`Trainer`] along with the model, dataset, tokenizer, data collator, and `compute_metrics` function.
3. Call [`~Trainer.train`] to finetune your model.
```py
>>> training_args = TrainingArguments(
... output_dir="my_awesome_asr_mind_model",
... per_device_train_batch_size=8,
... gradient_accumulation_steps=2,
... learning_rate=1e-5,
... warmup_steps=500,
... max_steps=2000,
... gradient_checkpointing=True,
... fp16=True,
... group_by_length=True,
... evaluation_strategy="steps",
... per_device_eval_batch_size=8,
... save_steps=1000,
... eval_steps=1000,
... logging_steps=25,
... load_best_model_at_end=True,
... metric_for_best_model="wer",
... greater_is_better=False,
... push_to_hub=True,
... )
>>> trainer = Trainer(
... model=model,
... args=training_args,
... train_dataset=encoded_minds["train"],
... eval_dataset=encoded_minds["test"],
... tokenizer=processor,
... data_collator=data_collator,
... compute_metrics=compute_metrics,
... )
>>> trainer.train()
```
Once training is completed, share your model to the Hub with the [`~transformers.Trainer.push_to_hub`] method so everyone can use your model:
```py
>>> trainer.push_to_hub()
```
</pt>
</frameworkcontent>
<Tip>
For a more in-depth example of how to finetune a model for automatic speech recognition, take a look at this blog [post](https://huggingface.co/blog/fine-tune-wav2vec2-english) for English ASR and this [post](https://huggingface.co/blog/fine-tune-xlsr-wav2vec2) for multilingual ASR.
</Tip>
## Inference
Great, now that you've finetuned a model, you can use it for inference!
Load an audio file you'd like to run inference on. Remember to resample the sampling rate of the audio file to match the sampling rate of the model if you need to!
```py
>>> from datasets import load_dataset, Audio
>>> dataset = load_dataset("PolyAI/minds14", "en-US", split="train")
>>> dataset = dataset.cast_column("audio", Audio(sampling_rate=16000))
>>> sampling_rate = dataset.features["audio"].sampling_rate
>>> audio_file = dataset[0]["audio"]["path"]
```
The simplest way to try out your finetuned model for inference is to use it in a [`pipeline`]. Instantiate a `pipeline` for automatic speech recognition with your model, and pass your audio file to it:
```py
>>> from transformers import pipeline
>>> transcriber = pipeline("automatic-speech-recognition", model="stevhliu/my_awesome_asr_minds_model")
>>> transcriber(audio_file)
{'text': 'I WOUD LIKE O SET UP JOINT ACOUNT WTH Y PARTNER'}
```
<Tip>
The transcription is decent, but it could be better! Try finetuning your model on more examples to get even better results!
</Tip>
You can also manually replicate the results of the `pipeline` if you'd like:
<frameworkcontent>
<pt>
Load a processor to preprocess the audio file and transcription and return the `input` as PyTorch tensors:
```py
>>> from transformers import AutoProcessor
>>> processor = AutoProcessor.from_pretrained("stevhliu/my_awesome_asr_mind_model")
>>> inputs = processor(dataset[0]["audio"]["array"], sampling_rate=sampling_rate, return_tensors="pt")
```
Pass your inputs to the model and return the logits:
```py
>>> from transformers import AutoModelForCTC
>>> model = AutoModelForCTC.from_pretrained("stevhliu/my_awesome_asr_mind_model")
>>> with torch.no_grad():
... logits = model(**inputs).logits
```
Get the predicted `input_ids` with the highest probability, and use the processor to decode the predicted `input_ids` back into text:
```py
>>> import torch
>>> predicted_ids = torch.argmax(logits, dim=-1)
>>> transcription = processor.batch_decode(predicted_ids)
>>> transcription
['I WOUL LIKE O SET UP JOINT ACOUNT WTH Y PARTNER']
```
</pt>
</frameworkcontent> | huggingface/transformers/blob/main/docs/source/en/tasks/asr.md |
!--Copyright 2021 The HuggingFace Team. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
the License. You may obtain a copy of the License at
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# RoFormer
## Overview
The RoFormer model was proposed in [RoFormer: Enhanced Transformer with Rotary Position Embedding](https://arxiv.org/pdf/2104.09864v1.pdf) by Jianlin Su and Yu Lu and Shengfeng Pan and Bo Wen and Yunfeng Liu.
The abstract from the paper is the following:
*Position encoding in transformer architecture provides supervision for dependency modeling between elements at
different positions in the sequence. We investigate various methods to encode positional information in
transformer-based language models and propose a novel implementation named Rotary Position Embedding(RoPE). The
proposed RoPE encodes absolute positional information with rotation matrix and naturally incorporates explicit relative
position dependency in self-attention formulation. Notably, RoPE comes with valuable properties such as flexibility of
being expand to any sequence lengths, decaying inter-token dependency with increasing relative distances, and
capability of equipping the linear self-attention with relative position encoding. As a result, the enhanced
transformer with rotary position embedding, or RoFormer, achieves superior performance in tasks with long texts. We
release the theoretical analysis along with some preliminary experiment results on Chinese data. The undergoing
experiment for English benchmark will soon be updated.*
This model was contributed by [junnyu](https://huggingface.co/junnyu). The original code can be found [here](https://github.com/ZhuiyiTechnology/roformer).
## Usage tips
RoFormer is a BERT-like autoencoding model with rotary position embeddings. Rotary position embeddings have shown
improved performance on classification tasks with long texts.
## Resources
- [Text classification task guide](../tasks/sequence_classification)
- [Token classification task guide](../tasks/token_classification)
- [Question answering task guide](../tasks/question_answering)
- [Causal language modeling task guide](../tasks/language_modeling)
- [Masked language modeling task guide](../tasks/masked_language_modeling)
- [Multiple choice task guide](../tasks/multiple_choice)
## RoFormerConfig
[[autodoc]] RoFormerConfig
## RoFormerTokenizer
[[autodoc]] RoFormerTokenizer
- build_inputs_with_special_tokens
- get_special_tokens_mask
- create_token_type_ids_from_sequences
- save_vocabulary
## RoFormerTokenizerFast
[[autodoc]] RoFormerTokenizerFast
- build_inputs_with_special_tokens
<frameworkcontent>
<pt>
## RoFormerModel
[[autodoc]] RoFormerModel
- forward
## RoFormerForCausalLM
[[autodoc]] RoFormerForCausalLM
- forward
## RoFormerForMaskedLM
[[autodoc]] RoFormerForMaskedLM
- forward
## RoFormerForSequenceClassification
[[autodoc]] RoFormerForSequenceClassification
- forward
## RoFormerForMultipleChoice
[[autodoc]] RoFormerForMultipleChoice
- forward
## RoFormerForTokenClassification
[[autodoc]] RoFormerForTokenClassification
- forward
## RoFormerForQuestionAnswering
[[autodoc]] RoFormerForQuestionAnswering
- forward
</pt>
<tf>
## TFRoFormerModel
[[autodoc]] TFRoFormerModel
- call
## TFRoFormerForMaskedLM
[[autodoc]] TFRoFormerForMaskedLM
- call
## TFRoFormerForCausalLM
[[autodoc]] TFRoFormerForCausalLM
- call
## TFRoFormerForSequenceClassification
[[autodoc]] TFRoFormerForSequenceClassification
- call
## TFRoFormerForMultipleChoice
[[autodoc]] TFRoFormerForMultipleChoice
- call
## TFRoFormerForTokenClassification
[[autodoc]] TFRoFormerForTokenClassification
- call
## TFRoFormerForQuestionAnswering
[[autodoc]] TFRoFormerForQuestionAnswering
- call
</tf>
<jax>
## FlaxRoFormerModel
[[autodoc]] FlaxRoFormerModel
- __call__
## FlaxRoFormerForMaskedLM
[[autodoc]] FlaxRoFormerForMaskedLM
- __call__
## FlaxRoFormerForSequenceClassification
[[autodoc]] FlaxRoFormerForSequenceClassification
- __call__
## FlaxRoFormerForMultipleChoice
[[autodoc]] FlaxRoFormerForMultipleChoice
- __call__
## FlaxRoFormerForTokenClassification
[[autodoc]] FlaxRoFormerForTokenClassification
- __call__
## FlaxRoFormerForQuestionAnswering
[[autodoc]] FlaxRoFormerForQuestionAnswering
- __call__
</jax>
</frameworkcontent>
| huggingface/transformers/blob/main/docs/source/en/model_doc/roformer.md |
!--Copyright 2021 The HuggingFace Team. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
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an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
specific language governing permissions and limitations under the License.
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-->
# XLSR-Wav2Vec2
## Overview
The XLSR-Wav2Vec2 model was proposed in [Unsupervised Cross-Lingual Representation Learning For Speech Recognition](https://arxiv.org/abs/2006.13979) by Alexis Conneau, Alexei Baevski, Ronan Collobert, Abdelrahman Mohamed, Michael
Auli.
The abstract from the paper is the following:
*This paper presents XLSR which learns cross-lingual speech representations by pretraining a single model from the raw
waveform of speech in multiple languages. We build on wav2vec 2.0 which is trained by solving a contrastive task over
masked latent speech representations and jointly learns a quantization of the latents shared across languages. The
resulting model is fine-tuned on labeled data and experiments show that cross-lingual pretraining significantly
outperforms monolingual pretraining. On the CommonVoice benchmark, XLSR shows a relative phoneme error rate reduction
of 72% compared to the best known results. On BABEL, our approach improves word error rate by 16% relative compared to
a comparable system. Our approach enables a single multilingual speech recognition model which is competitive to strong
individual models. Analysis shows that the latent discrete speech representations are shared across languages with
increased sharing for related languages. We hope to catalyze research in low-resource speech understanding by releasing
XLSR-53, a large model pretrained in 53 languages.*
The original code can be found [here](https://github.com/pytorch/fairseq/tree/master/fairseq/models/wav2vec).
## Usage tips
- XLSR-Wav2Vec2 is a speech model that accepts a float array corresponding to the raw waveform of the speech signal.
- XLSR-Wav2Vec2 model was trained using connectionist temporal classification (CTC) so the model output has to be
decoded using [`Wav2Vec2CTCTokenizer`].
<Tip>
XLSR-Wav2Vec2's architecture is based on the Wav2Vec2 model, so one can refer to [Wav2Vec2's documentation page](wav2vec2).
</Tip>
| huggingface/transformers/blob/main/docs/source/en/model_doc/xlsr_wav2vec2.md |
!--Copyright 2022 The HuggingFace Team. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
the License. You may obtain a copy of the License at
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Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
specific language governing permissions and limitations under the License.
⚠️ Note that this file is in Markdown but contain specific syntax for our doc-builder (similar to MDX) that may not be
rendered properly in your Markdown viewer.
-->
# CodeGen
## Overview
The CodeGen model was proposed in [A Conversational Paradigm for Program Synthesis](https://arxiv.org/abs/2203.13474) by Erik Nijkamp, Bo Pang, Hiroaki Hayashi, Lifu Tu, Huan Wang, Yingbo Zhou, Silvio Savarese, and Caiming Xiong.
CodeGen is an autoregressive language model for program synthesis trained sequentially on [The Pile](https://pile.eleuther.ai/), BigQuery, and BigPython.
The abstract from the paper is the following:
*Program synthesis strives to generate a computer program as a solution to a given problem specification. We propose a conversational program synthesis approach via large language models, which addresses the challenges of searching over a vast program space and user intent specification faced in prior approaches. Our new approach casts the process of writing a specification and program as a multi-turn conversation between a user and a system. It treats program synthesis as a sequence prediction problem, in which the specification is expressed in natural language and the desired program is conditionally sampled. We train a family of large language models, called CodeGen, on natural language and programming language data. With weak supervision in the data and the scaling up of data size and model size, conversational capacities emerge from the simple autoregressive language modeling. To study the model behavior on conversational program synthesis, we develop a multi-turn programming benchmark (MTPB), where solving each problem requires multi-step synthesis via multi-turn conversation between the user and the model. Our findings show the emergence of conversational capabilities and the effectiveness of the proposed conversational program synthesis paradigm. In addition, our model CodeGen (with up to 16B parameters trained on TPU-v4) outperforms OpenAI's Codex on the HumanEval benchmark. We make the training library JaxFormer including checkpoints available as open source contribution: [this https URL](https://github.com/salesforce/codegen).*
This model was contributed by [Hiroaki Hayashi](https://huggingface.co/rooa).
The original code can be found [here](https://github.com/salesforce/codegen).
## Checkpoint Naming
* CodeGen model [checkpoints](https://huggingface.co/models?other=codegen) are available on different pre-training data with variable sizes.
* The format is: `Salesforce/codegen-{size}-{data}`, where
* `size`: `350M`, `2B`, `6B`, `16B`
* `data`:
* `nl`: Pre-trained on the Pile
* `multi`: Initialized with `nl`, then further pre-trained on multiple programming languages data
* `mono`: Initialized with `multi`, then further pre-trained on Python data
* For example, `Salesforce/codegen-350M-mono` offers a 350 million-parameter checkpoint pre-trained sequentially on the Pile, multiple programming languages, and Python.
## Usage example
```python
>>> from transformers import AutoModelForCausalLM, AutoTokenizer
>>> checkpoint = "Salesforce/codegen-350M-mono"
>>> model = AutoModelForCausalLM.from_pretrained(checkpoint)
>>> tokenizer = AutoTokenizer.from_pretrained(checkpoint)
>>> text = "def hello_world():"
>>> completion = model.generate(**tokenizer(text, return_tensors="pt"))
>>> print(tokenizer.decode(completion[0]))
def hello_world():
print("Hello World")
hello_world()
```
## Resources
- [Causal language modeling task guide](../tasks/language_modeling)
## CodeGenConfig
[[autodoc]] CodeGenConfig
- all
## CodeGenTokenizer
[[autodoc]] CodeGenTokenizer
- save_vocabulary
## CodeGenTokenizerFast
[[autodoc]] CodeGenTokenizerFast
## CodeGenModel
[[autodoc]] CodeGenModel
- forward
## CodeGenForCausalLM
[[autodoc]] CodeGenForCausalLM
- forward
| huggingface/transformers/blob/main/docs/source/en/model_doc/codegen.md |
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rendered properly in your Markdown viewer.
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# How to convert a 🤗 Transformers model to TensorFlow?
Having multiple frameworks available to use with 🤗 Transformers gives you flexibility to play their strengths when
designing your application, but it implies that compatibility must be added on a per-model basis. The good news is that
adding TensorFlow compatibility to an existing model is simpler than [adding a new model from scratch](add_new_model)!
Whether you wish to have a deeper understanding of large TensorFlow models, make a major open-source contribution, or
enable TensorFlow for your model of choice, this guide is for you.
This guide empowers you, a member of our community, to contribute TensorFlow model weights and/or
architectures to be used in 🤗 Transformers, with minimal supervision from the Hugging Face team. Writing a new model
is no small feat, but hopefully this guide will make it less of a rollercoaster 🎢 and more of a walk in the park 🚶.
Harnessing our collective experiences is absolutely critical to make this process increasingly easier, and thus we
highly encourage that you suggest improvements to this guide!
Before you dive deeper, it is recommended that you check the following resources if you're new to 🤗 Transformers:
- [General overview of 🤗 Transformers](add_new_model#general-overview-of-transformers)
- [Hugging Face's TensorFlow Philosophy](https://huggingface.co/blog/tensorflow-philosophy)
In the remainder of this guide, you will learn what's needed to add a new TensorFlow model architecture, the
procedure to convert PyTorch into TensorFlow model weights, and how to efficiently debug mismatches across ML
frameworks. Let's get started!
<Tip>
Are you unsure whether the model you wish to use already has a corresponding TensorFlow architecture?
Check the `model_type` field of the `config.json` of your model of choice
([example](https://huggingface.co/bert-base-uncased/blob/main/config.json#L14)). If the corresponding model folder in
🤗 Transformers has a file whose name starts with "modeling_tf", it means that it has a corresponding TensorFlow
architecture ([example](https://github.com/huggingface/transformers/tree/main/src/transformers/models/bert)).
</Tip>
## Step-by-step guide to add TensorFlow model architecture code
There are many ways to design a large model architecture, and multiple ways of implementing said design. However,
you might recall from our [general overview of 🤗 Transformers](add_new_model#general-overview-of-transformers)
that we are an opinionated bunch - the ease of use of 🤗 Transformers relies on consistent design choices. From
experience, we can tell you a few important things about adding TensorFlow models:
- Don't reinvent the wheel! More often than not, there are at least two reference implementations you should check: the
PyTorch equivalent of the model you are implementing and other TensorFlow models for the same class of problems.
- Great model implementations survive the test of time. This doesn't happen because the code is pretty, but rather
because the code is clear, easy to debug and build upon. If you make the life of the maintainers easy with your
TensorFlow implementation, by replicating the same patterns as in other TensorFlow models and minimizing the mismatch
to the PyTorch implementation, you ensure your contribution will be long lived.
- Ask for help when you're stuck! The 🤗 Transformers team is here to help, and we've probably found solutions to the same
problems you're facing.
Here's an overview of the steps needed to add a TensorFlow model architecture:
1. Select the model you wish to convert
2. Prepare transformers dev environment
3. (Optional) Understand theoretical aspects and the existing implementation
4. Implement the model architecture
5. Implement model tests
6. Submit the pull request
7. (Optional) Build demos and share with the world
### 1.-3. Prepare your model contribution
**1. Select the model you wish to convert**
Let's start off with the basics: the first thing you need to know is the architecture you want to convert. If you
don't have your eyes set on a specific architecture, asking the 🤗 Transformers team for suggestions is a great way to
maximize your impact - we will guide you towards the most prominent architectures that are missing on the TensorFlow
side. If the specific model you want to use with TensorFlow already has a TensorFlow architecture implementation in
🤗 Transformers but is lacking weights, feel free to jump straight into the
[weight conversion section](#adding-tensorflow-weights-to-hub)
of this page.
For simplicity, the remainder of this guide assumes you've decided to contribute with the TensorFlow version of
*BrandNewBert* (the same example as in the [guide](add_new_model) to add a new model from scratch).
<Tip>
Before starting the work on a TensorFlow model architecture, double-check that there is no ongoing effort to do so.
You can search for `BrandNewBert` on the
[pull request GitHub page](https://github.com/huggingface/transformers/pulls?q=is%3Apr) to confirm that there is no
TensorFlow-related pull request.
</Tip>
**2. Prepare transformers dev environment**
Having selected the model architecture, open a draft PR to signal your intention to work on it. Follow the
instructions below to set up your environment and open a draft PR.
1. Fork the [repository](https://github.com/huggingface/transformers) by clicking on the 'Fork' button on the
repository's page. This creates a copy of the code under your GitHub user account.
2. Clone your `transformers` fork to your local disk, and add the base repository as a remote:
```bash
git clone https://github.com/[your Github handle]/transformers.git
cd transformers
git remote add upstream https://github.com/huggingface/transformers.git
```
3. Set up a development environment, for instance by running the following command:
```bash
python -m venv .env
source .env/bin/activate
pip install -e ".[dev]"
```
Depending on your OS, and since the number of optional dependencies of Transformers is growing, you might get a
failure with this command. If that's the case make sure to install TensorFlow then do:
```bash
pip install -e ".[quality]"
```
**Note:** You don't need to have CUDA installed. Making the new model work on CPU is sufficient.
4. Create a branch with a descriptive name from your main branch
```bash
git checkout -b add_tf_brand_new_bert
```
5. Fetch and rebase to current main
```bash
git fetch upstream
git rebase upstream/main
```
6. Add an empty `.py` file in `transformers/src/models/brandnewbert/` named `modeling_tf_brandnewbert.py`. This will
be your TensorFlow model file.
7. Push the changes to your account using:
```bash
git add .
git commit -m "initial commit"
git push -u origin add_tf_brand_new_bert
```
8. Once you are satisfied, go to the webpage of your fork on GitHub. Click on “Pull request”. Make sure to add the
GitHub handle of some members of the Hugging Face team as reviewers, so that the Hugging Face team gets notified for
future changes.
9. Change the PR into a draft by clicking on “Convert to draft” on the right of the GitHub pull request web page.
Now you have set up a development environment to port *BrandNewBert* to TensorFlow in 🤗 Transformers.
**3. (Optional) Understand theoretical aspects and the existing implementation**
You should take some time to read *BrandNewBert's* paper, if such descriptive work exists. There might be large
sections of the paper that are difficult to understand. If this is the case, this is fine - don't worry! The goal is
not to get a deep theoretical understanding of the paper, but to extract the necessary information required to
effectively re-implement the model in 🤗 Transformers using TensorFlow. That being said, you don't have to spend too
much time on the theoretical aspects, but rather focus on the practical ones, namely the existing model documentation
page (e.g. [model docs for BERT](model_doc/bert)).
After you've grasped the basics of the models you are about to implement, it's important to understand the existing
implementation. This is a great chance to confirm that a working implementation matches your expectations for the
model, as well as to foresee technical challenges on the TensorFlow side.
It's perfectly natural that you feel overwhelmed with the amount of information that you've just absorbed. It is
definitely not a requirement that you understand all facets of the model at this stage. Nevertheless, we highly
encourage you to clear any pressing questions in our [forum](https://discuss.huggingface.co/).
### 4. Model implementation
Now it's time to finally start coding. Our suggested starting point is the PyTorch file itself: copy the contents of
`modeling_brand_new_bert.py` inside `src/transformers/models/brand_new_bert/` into
`modeling_tf_brand_new_bert.py`. The goal of this section is to modify the file and update the import structure of
🤗 Transformers such that you can import `TFBrandNewBert` and
`TFBrandNewBert.from_pretrained(model_repo, from_pt=True)` successfully loads a working TensorFlow *BrandNewBert* model.
Sadly, there is no prescription to convert a PyTorch model into TensorFlow. You can, however, follow our selection of
tips to make the process as smooth as possible:
- Prepend `TF` to the name of all classes (e.g. `BrandNewBert` becomes `TFBrandNewBert`).
- Most PyTorch operations have a direct TensorFlow replacement. For example, `torch.nn.Linear` corresponds to
`tf.keras.layers.Dense`, `torch.nn.Dropout` corresponds to `tf.keras.layers.Dropout`, etc. If you're not sure
about a specific operation, you can use the [TensorFlow documentation](https://www.tensorflow.org/api_docs/python/tf)
or the [PyTorch documentation](https://pytorch.org/docs/stable/).
- Look for patterns in the 🤗 Transformers codebase. If you come across a certain operation that doesn't have a direct
replacement, the odds are that someone else already had the same problem.
- By default, keep the same variable names and structure as in PyTorch. This will make it easier to debug, track
issues, and add fixes down the line.
- Some layers have different default values in each framework. A notable example is the batch normalization layer's
epsilon (`1e-5` in [PyTorch](https://pytorch.org/docs/stable/generated/torch.nn.BatchNorm2d.html#torch.nn.BatchNorm2d)
and `1e-3` in [TensorFlow](https://www.tensorflow.org/api_docs/python/tf/keras/layers/BatchNormalization)).
Double-check the documentation!
- PyTorch's `nn.Parameter` variables typically need to be initialized within TF Layer's `build()`. See the following
example: [PyTorch](https://github.com/huggingface/transformers/blob/655f72a6896c0533b1bdee519ed65a059c2425ac/src/transformers/models/vit_mae/modeling_vit_mae.py#L212) /
[TensorFlow](https://github.com/huggingface/transformers/blob/655f72a6896c0533b1bdee519ed65a059c2425ac/src/transformers/models/vit_mae/modeling_tf_vit_mae.py#L220)
- If the PyTorch model has a `#copied from ...` on top of a function, the odds are that your TensorFlow model can also
borrow that function from the architecture it was copied from, assuming it has a TensorFlow architecture.
- Assigning the `name` attribute correctly in TensorFlow functions is critical to do the `from_pt=True` weight
cross-loading. `name` is almost always the name of the corresponding variable in the PyTorch code. If `name` is not
properly set, you will see it in the error message when loading the model weights.
- The logic of the base model class, `BrandNewBertModel`, will actually reside in `TFBrandNewBertMainLayer`, a Keras
layer subclass ([example](https://github.com/huggingface/transformers/blob/4fd32a1f499e45f009c2c0dea4d81c321cba7e02/src/transformers/models/bert/modeling_tf_bert.py#L719)).
`TFBrandNewBertModel` will simply be a wrapper around this layer.
- Keras models need to be built in order to load pretrained weights. For that reason, `TFBrandNewBertPreTrainedModel`
will need to hold an example of inputs to the model, the `dummy_inputs`
([example](https://github.com/huggingface/transformers/blob/4fd32a1f499e45f009c2c0dea4d81c321cba7e02/src/transformers/models/bert/modeling_tf_bert.py#L916)).
- If you get stuck, ask for help - we're here to help you! 🤗
In addition to the model file itself, you will also need to add the pointers to the model classes and related
documentation pages. You can complete this part entirely following the patterns in other PRs
([example](https://github.com/huggingface/transformers/pull/18020/files)). Here's a list of the needed manual
changes:
- Include all public classes of *BrandNewBert* in `src/transformers/__init__.py`
- Add *BrandNewBert* classes to the corresponding Auto classes in `src/transformers/models/auto/modeling_tf_auto.py`
- Add the lazy loading classes related to *BrandNewBert* in `src/transformers/utils/dummy_tf_objects.py`
- Update the import structures for the public classes in `src/transformers/models/brand_new_bert/__init__.py`
- Add the documentation pointers to the public methods of *BrandNewBert* in `docs/source/en/model_doc/brand_new_bert.md`
- Add yourself to the list of contributors to *BrandNewBert* in `docs/source/en/model_doc/brand_new_bert.md`
- Finally, add a green tick ✅ to the TensorFlow column of *BrandNewBert* in `docs/source/en/index.md`
When you're happy with your implementation, run the following checklist to confirm that your model architecture is
ready:
1. All layers that behave differently at train time (e.g. Dropout) are called with a `training` argument, which is
propagated all the way from the top-level classes
2. You have used `#copied from ...` whenever possible
3. `TFBrandNewBertMainLayer` and all classes that use it have their `call` function decorated with `@unpack_inputs`
4. `TFBrandNewBertMainLayer` is decorated with `@keras_serializable`
5. A TensorFlow model can be loaded from PyTorch weights using `TFBrandNewBert.from_pretrained(model_repo, from_pt=True)`
6. You can call the TensorFlow model using the expected input format
### 5. Add model tests
Hurray, you've implemented a TensorFlow model! Now it's time to add tests to make sure that your model behaves as
expected. As in the previous section, we suggest you start by copying the `test_modeling_brand_new_bert.py` file in
`tests/models/brand_new_bert/` into `test_modeling_tf_brand_new_bert.py`, and continue by making the necessary
TensorFlow replacements. For now, in all `.from_pretrained()` calls, you should use the `from_pt=True` flag to load
the existing PyTorch weights.
After you're done, it's time for the moment of truth: run the tests! 😬
```bash
NVIDIA_TF32_OVERRIDE=0 RUN_SLOW=1 RUN_PT_TF_CROSS_TESTS=1 \
py.test -vv tests/models/brand_new_bert/test_modeling_tf_brand_new_bert.py
```
The most likely outcome is that you'll see a bunch of errors. Don't worry, this is expected! Debugging ML models is
notoriously hard, and the key ingredient to success is patience (and `breakpoint()`). In our experience, the hardest
problems arise from subtle mismatches between ML frameworks, for which we have a few pointers at the end of this guide.
In other cases, a general test might not be directly applicable to your model, in which case we suggest an override
at the model test class level. Regardless of the issue, don't hesitate to ask for help in your draft pull request if
you're stuck.
When all tests pass, congratulations, your model is nearly ready to be added to the 🤗 Transformers library! 🎉
### 6.-7. Ensure everyone can use your model
**6. Submit the pull request**
Once you're done with the implementation and the tests, it's time to submit a pull request. Before pushing your code,
run our code formatting utility, `make fixup` 🪄. This will automatically fix any formatting issues, which would cause
our automatic checks to fail.
It's now time to convert your draft pull request into a real pull request. To do so, click on the "Ready for
review" button and add Joao (`@gante`) and Matt (`@Rocketknight1`) as reviewers. A model pull request will need
at least 3 reviewers, but they will take care of finding appropriate additional reviewers for your model.
After all reviewers are happy with the state of your PR, the final action point is to remove the `from_pt=True` flag in
`.from_pretrained()` calls. Since there are no TensorFlow weights, you will have to add them! Check the section
below for instructions on how to do it.
Finally, when the TensorFlow weights get merged, you have at least 3 reviewer approvals, and all CI checks are
green, double-check the tests locally one last time
```bash
NVIDIA_TF32_OVERRIDE=0 RUN_SLOW=1 RUN_PT_TF_CROSS_TESTS=1 \
py.test -vv tests/models/brand_new_bert/test_modeling_tf_brand_new_bert.py
```
and we will merge your PR! Congratulations on the milestone 🎉
**7. (Optional) Build demos and share with the world**
One of the hardest parts about open-source is discovery. How can the other users learn about the existence of your
fabulous TensorFlow contribution? With proper communication, of course! 📣
There are two main ways to share your model with the community:
- Build demos. These include Gradio demos, notebooks, and other fun ways to show off your model. We highly
encourage you to add a notebook to our [community-driven demos](https://huggingface.co/docs/transformers/community).
- Share stories on social media like Twitter and LinkedIn. You should be proud of your work and share
your achievement with the community - your model can now be used by thousands of engineers and researchers around
the world 🌍! We will be happy to retweet your posts and help you share your work with the community.
## Adding TensorFlow weights to 🤗 Hub
Assuming that the TensorFlow model architecture is available in 🤗 Transformers, converting PyTorch weights into
TensorFlow weights is a breeze!
Here's how to do it:
1. Make sure you are logged into your Hugging Face account in your terminal. You can log in using the command
`huggingface-cli login` (you can find your access tokens [here](https://huggingface.co/settings/tokens))
2. Run `transformers-cli pt-to-tf --model-name foo/bar`, where `foo/bar` is the name of the model repository
containing the PyTorch weights you want to convert
3. Tag `@joaogante` and `@Rocketknight1` in the 🤗 Hub PR the command above has just created
That's it! 🎉
## Debugging mismatches across ML frameworks 🐛
At some point, when adding a new architecture or when creating TensorFlow weights for an existing architecture, you
might come across errors complaining about mismatches between PyTorch and TensorFlow. You might even decide to open the
model architecture code for the two frameworks, and find that they look identical. What's going on? 🤔
First of all, let's talk about why understanding these mismatches matters. Many community members will use 🤗
Transformers models out of the box, and trust that our models behave as expected. When there is a large mismatch
between the two frameworks, it implies that the model is not following the reference implementation for at least one
of the frameworks. This might lead to silent failures, in which the model runs but has poor performance. This is
arguably worse than a model that fails to run at all! To that end, we aim at having a framework mismatch smaller than
`1e-5` at all stages of the model.
As in other numerical problems, the devil is in the details. And as in any detail-oriented craft, the secret
ingredient here is patience. Here is our suggested workflow for when you come across this type of issues:
1. Locate the source of mismatches. The model you're converting probably has near identical inner variables up to a
certain point. Place `breakpoint()` statements in the two frameworks' architectures, and compare the values of the
numerical variables in a top-down fashion until you find the source of the problems.
2. Now that you've pinpointed the source of the issue, get in touch with the 🤗 Transformers team. It is possible
that we've seen a similar problem before and can promptly provide a solution. As a fallback, scan popular pages
like StackOverflow and GitHub issues.
3. If there is no solution in sight, it means you'll have to go deeper. The good news is that you've located the
issue, so you can focus on the problematic instruction, abstracting away the rest of the model! The bad news is
that you'll have to venture into the source implementation of said instruction. In some cases, you might find an
issue with a reference implementation - don't abstain from opening an issue in the upstream repository.
In some cases, in discussion with the 🤗 Transformers team, we might find that fixing the mismatch is infeasible.
When the mismatch is very small in the output layers of the model (but potentially large in the hidden states), we
might decide to ignore it in favor of distributing the model. The `pt-to-tf` CLI mentioned above has a `--max-error`
flag to override the error message at weight conversion time.
| huggingface/transformers/blob/main/docs/source/en/add_tensorflow_model.md |
Image Captioning (vision-encoder-text-decoder model) training example
The following example showcases how to finetune a vision-encoder-text-decoder model for image captioning
using the JAX/Flax backend, leveraging 🤗 Transformers library's [FlaxVisionEncoderDecoderModel](https://huggingface.co/docs/transformers/model_doc/vision-encoder-decoder#transformers.FlaxVisionEncoderDecoderModel).
JAX/Flax allows you to trace pure functions and compile them into efficient, fused accelerator code on both GPU and TPU.
Models written in JAX/Flax are **immutable** and updated in a purely functional
way which enables simple and efficient model parallelism.
`run_image_captioning_flax.py` is a lightweight example of how to download and preprocess a dataset from the 🤗 Datasets
library or use your own files (jsonlines or csv), then fine-tune one of the architectures above on it.
For custom datasets in `jsonlines` format please see: https://huggingface.co/docs/datasets/loading_datasets#json-files and you also will find examples of these below.
### Download COCO dataset (2017)
This example uses COCO dataset (2017) through a custom dataset script, which requires users to manually download the
COCO dataset before training.
```bash
mkdir data
cd data
wget http://images.cocodataset.org/zips/train2017.zip
wget http://images.cocodataset.org/zips/val2017.zip
wget http://images.cocodataset.org/zips/test2017.zip
wget http://images.cocodataset.org/annotations/annotations_trainval2017.zip
wget http://images.cocodataset.org/annotations/image_info_test2017.zip
cd ..
```
### Create a model from a vision encoder model and a text decoder model
Next, we create a [FlaxVisionEncoderDecoderModel](https://huggingface.co/docs/transformers/model_doc/visionencoderdecoder#transformers.FlaxVisionEncoderDecoderModel) instance from a pre-trained vision encoder ([ViT](https://huggingface.co/docs/transformers/model_doc/vit#transformers.FlaxViTModel)) and a pre-trained text decoder ([GPT2](https://huggingface.co/docs/transformers/model_doc/gpt2#transformers.FlaxGPT2Model)):
```bash
python3 create_model_from_encoder_decoder_models.py \
--output_dir model \
--encoder_model_name_or_path google/vit-base-patch16-224-in21k \
--decoder_model_name_or_path gpt2
```
### Train the model
Finally, we can run the example script to train the model:
```bash
python3 run_image_captioning_flax.py \
--output_dir ./image-captioning-training-results \
--model_name_or_path model \
--dataset_name ydshieh/coco_dataset_script \
--dataset_config_name=2017 \
--data_dir $PWD/data \
--image_column image_path \
--caption_column caption \
--do_train --do_eval --predict_with_generate \
--num_train_epochs 1 \
--eval_steps 500 \
--learning_rate 3e-5 --warmup_steps 0 \
--per_device_train_batch_size 32 \
--per_device_eval_batch_size 32 \
--overwrite_output_dir \
--max_target_length 32 \
--num_beams 8 \
--preprocessing_num_workers 16 \
--logging_steps 10 \
--block_size 16384 \
--push_to_hub
```
This should finish in about 1h30 on Cloud TPU, with validation loss and ROUGE2 score of 2.0153 and 14.64 respectively
after 1 epoch. Training statistics can be accessed on [Models](https://huggingface.co/ydshieh/image-captioning-training-results/tensorboard).
| huggingface/transformers/blob/main/examples/flax/image-captioning/README.md |
!---
Copyright 2022 The HuggingFace Team. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
-->
# Image pretraining examples
This directory contains Python scripts that allow you to pre-train Transformer-based vision models (like [ViT](https://huggingface.co/docs/transformers/model_doc/vit), [Swin Transformer](https://huggingface.co/docs/transformers/model_doc/swin)) on your own data, after which you can easily load the weights into a [`AutoModelForImageClassification`](https://huggingface.co/docs/transformers/model_doc/auto#transformers.AutoModelForImageClassification). It currently includes scripts for:
- [SimMIM](#simmim) (by Microsoft Research)
- [MAE](#mae) (by Facebook AI).
NOTE: If you encounter problems/have suggestions for improvement, open an issue on Github and tag @NielsRogge.
## SimMIM
The `run_mim.py` script can be used to pre-train any Transformer-based vision model in the library (concretly, any model supported by the `AutoModelForMaskedImageModeling` API) for masked image modeling as proposed in [SimMIM: A Simple Framework for Masked Image Modeling](https://arxiv.org/abs/2111.09886) using PyTorch.
<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/simmim_architecture.jpg"
alt="drawing" width="300"/>
<small> SimMIM framework. Taken from the <a href="https://arxiv.org/abs/2111.09886">original paper</a>. </small>
The goal for the model is to predict raw pixel values for the masked patches, using just a linear layer as prediction head. The model is trained using a simple L1 loss.
### Using datasets from 🤗 datasets
Here we show how to pre-train a `ViT` from scratch for masked image modeling on the [cifar10](https://huggingface.co/datasets/cifar10) dataset.
Alternatively, one can decide to further pre-train an already pre-trained (or fine-tuned) checkpoint from the [hub](https://huggingface.co/). This can be done by setting the `model_name_or_path` argument to "google/vit-base-patch16-224-in21k" for example (and not specifying the `model_type` argument).
```bash
!python run_mim.py \
--model_type vit \
--output_dir ./outputs/ \
--overwrite_output_dir \
--remove_unused_columns False \
--label_names bool_masked_pos \
--do_train \
--do_eval \
--learning_rate 2e-5 \
--weight_decay 0.05 \
--num_train_epochs 100 \
--per_device_train_batch_size 8 \
--per_device_eval_batch_size 8 \
--logging_strategy steps \
--logging_steps 10 \
--evaluation_strategy epoch \
--save_strategy epoch \
--load_best_model_at_end True \
--save_total_limit 3 \
--seed 1337
```
Here, we train for 100 epochs with a learning rate of 2e-5. Note that the SimMIM authors used a more sophisticated learning rate schedule, see the [config files](https://github.com/microsoft/SimMIM/blob/main/configs/vit_base__800ep/simmim_pretrain__vit_base__img224__800ep.yaml) for more info. One can easily tweak the script to include this learning rate schedule (several learning rate schedulers are supported via the [training arguments](https://huggingface.co/docs/transformers/main_classes/trainer#transformers.TrainingArguments)).
We can also for instance replicate the pre-training of a Swin Transformer using the same architecture as used by the SimMIM authors. For this, we first create a custom configuration and save it locally:
```python
from transformers import SwinConfig
IMAGE_SIZE = 192
PATCH_SIZE = 4
EMBED_DIM = 128
DEPTHS = [2, 2, 18, 2]
NUM_HEADS = [4, 8, 16, 32]
WINDOW_SIZE = 6
config = SwinConfig(
image_size=IMAGE_SIZE,
patch_size=PATCH_SIZE,
embed_dim=EMBED_DIM,
depths=DEPTHS,
num_heads=NUM_HEADS,
window_size=WINDOW_SIZE,
)
config.save_pretrained("path_to_config")
```
Next, we can run the script by providing the path to this custom configuration (replace `path_to_config` below with your path):
```bash
!python run_mim.py \
--config_name_or_path path_to_config \
--model_type swin \
--output_dir ./outputs/ \
--overwrite_output_dir \
--remove_unused_columns False \
--label_names bool_masked_pos \
--do_train \
--do_eval \
--learning_rate 2e-5 \
--num_train_epochs 5 \
--per_device_train_batch_size 8 \
--per_device_eval_batch_size 8 \
--logging_strategy steps \
--logging_steps 10 \
--evaluation_strategy epoch \
--save_strategy epoch \
--load_best_model_at_end True \
--save_total_limit 3 \
--seed 1337
```
This will train a Swin Transformer from scratch.
### Using your own data
To use your own dataset, the training script expects the following directory structure:
```bash
root/dog/xxx.png
root/dog/xxy.png
root/dog/[...]/xxz.png
root/cat/123.png
root/cat/nsdf3.png
root/cat/[...]/asd932_.png
```
Note that you can put images in dummy subfolders, whose names will be ignored by default (as labels aren't required). You can also just place all images into a single dummy subfolder. Once you've prepared your dataset, you can run the script like this:
```bash
python run_mim.py \
--model_type vit \
--dataset_name nateraw/image-folder \
--train_dir <path-to-train-root> \
--output_dir ./outputs/ \
--remove_unused_columns False \
--label_names bool_masked_pos \
--do_train \
--do_eval
```
## MAE
The `run_mae.py` script can be used to pre-train a Vision Transformer as a masked autoencoder (MAE), as proposed in [Masked Autoencoders Are Scalable Vision Learners](https://arxiv.org/abs/2111.06377). The script can be used to train a `ViTMAEForPreTraining` model in the Transformers library, using PyTorch. After self-supervised pre-training, one can load the weights of the encoder directly into a `ViTForImageClassification`. The MAE method allows for learning high-capacity models that generalize well: e.g., a vanilla ViT-Huge model achieves the best accuracy (87.8%) among methods that use only ImageNet-1K data.
The goal for the model is to predict raw pixel values for the masked patches. As the model internally masks patches and learns to reconstruct them, there's no need for any labels. The model uses the mean squared error (MSE) between the reconstructed and original images in the pixel space.
### Using datasets from 🤗 `datasets`
One can use the following command to pre-train a `ViTMAEForPreTraining` model from scratch on the [cifar10](https://huggingface.co/datasets/cifar10) dataset:
```bash
python run_mae.py \
--dataset_name cifar10 \
--output_dir ./vit-mae-demo \
--remove_unused_columns False \
--label_names pixel_values \
--mask_ratio 0.75 \
--norm_pix_loss \
--do_train \
--do_eval \
--base_learning_rate 1.5e-4 \
--lr_scheduler_type cosine \
--weight_decay 0.05 \
--num_train_epochs 800 \
--warmup_ratio 0.05 \
--per_device_train_batch_size 8 \
--per_device_eval_batch_size 8 \
--logging_strategy steps \
--logging_steps 10 \
--evaluation_strategy epoch \
--save_strategy epoch \
--load_best_model_at_end True \
--save_total_limit 3 \
--seed 1337
```
Here we set:
- `mask_ratio` to 0.75 (to mask 75% of the patches for each image)
- `norm_pix_loss` to use normalized pixel values as target (the authors reported better representations with this enabled)
- `base_learning_rate` to 1.5e-4. Note that the effective learning rate is computed by the [linear schedule](https://arxiv.org/abs/1706.02677): `lr` = `blr` * total training batch size / 256. The total training batch size is computed as `training_args.train_batch_size` * `training_args.gradient_accumulation_steps` * `training_args.world_size`.
This replicates the same hyperparameters as used in the original implementation, as shown in the table below.
<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/mae_pretraining_setting.png"
alt="drawing" width="300"/>
<small> Original hyperparameters. Taken from the <a href="https://arxiv.org/abs/2111.06377">original paper</a>. </small>
Alternatively, one can decide to further pre-train an already pre-trained (or fine-tuned) checkpoint from the [hub](https://huggingface.co/). This can be done by setting the `model_name_or_path` argument to "facebook/vit-mae-base" for example.
### Using your own data
To use your own dataset, the training script expects the following directory structure:
```bash
root/dog/xxx.png
root/dog/xxy.png
root/dog/[...]/xxz.png
root/cat/123.png
root/cat/nsdf3.png
root/cat/[...]/asd932_.png
```
Note that you can put images in dummy subfolders, whose names will be ignored by default (as labels aren't required). You can also just place all images into a single dummy subfolder. Once you've prepared your dataset, you can run the script like this:
```bash
python run_mae.py \
--model_type vit_mae \
--dataset_name nateraw/image-folder \
--train_dir <path-to-train-root> \
--output_dir ./outputs/ \
--remove_unused_columns False \
--label_names pixel_values \
--do_train \
--do_eval
```
#### 💡 The above will split the train dir into training and evaluation sets
- To control the split amount, use the `--train_val_split` flag.
- To provide your own validation split in its own directory, you can pass the `--validation_dir <path-to-val-root>` flag.
## Sharing your model on 🤗 Hub
0. If you haven't already, [sign up](https://huggingface.co/join) for a 🤗 account
1. Make sure you have `git-lfs` installed and git set up.
```bash
$ apt install git-lfs
$ git config --global user.email "[email protected]"
$ git config --global user.name "Your Name"
```
2. Log in with your HuggingFace account credentials using `huggingface-cli`
```bash
$ huggingface-cli login
# ...follow the prompts
```
3. When running the script, pass the following arguments:
```bash
python run_xxx.py \
--push_to_hub \
--push_to_hub_model_id <name-of-your-model> \
...
``` | huggingface/transformers/blob/main/examples/pytorch/image-pretraining/README.md |
!--Copyright 2022 The HuggingFace Team. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
the License. You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
specific language governing permissions and limitations under the License.
⚠️ Note that this file is in Markdown but contain specific syntax for our doc-builder (similar to MDX) that may not be
rendered properly in your Markdown viewer.
-->
# Nezha
## Overview
The Nezha model was proposed in [NEZHA: Neural Contextualized Representation for Chinese Language Understanding](https://arxiv.org/abs/1909.00204) by Junqiu Wei et al.
The abstract from the paper is the following:
*The pre-trained language models have achieved great successes in various natural language understanding (NLU) tasks
due to its capacity to capture the deep contextualized information in text by pre-training on large-scale corpora.
In this technical report, we present our practice of pre-training language models named NEZHA (NEural contextualiZed
representation for CHinese lAnguage understanding) on Chinese corpora and finetuning for the Chinese NLU tasks.
The current version of NEZHA is based on BERT with a collection of proven improvements, which include Functional
Relative Positional Encoding as an effective positional encoding scheme, Whole Word Masking strategy,
Mixed Precision Training and the LAMB Optimizer in training the models. The experimental results show that NEZHA
achieves the state-of-the-art performances when finetuned on several representative Chinese tasks, including
named entity recognition (People's Daily NER), sentence matching (LCQMC), Chinese sentiment classification (ChnSenti)
and natural language inference (XNLI).*
This model was contributed by [sijunhe](https://huggingface.co/sijunhe). The original code can be found [here](https://github.com/huawei-noah/Pretrained-Language-Model/tree/master/NEZHA-PyTorch).
## Resources
- [Text classification task guide](../tasks/sequence_classification)
- [Token classification task guide](../tasks/token_classification)
- [Question answering task guide](../tasks/question_answering)
- [Masked language modeling task guide](../tasks/masked_language_modeling)
- [Multiple choice task guide](../tasks/multiple_choice)
## NezhaConfig
[[autodoc]] NezhaConfig
## NezhaModel
[[autodoc]] NezhaModel
- forward
## NezhaForPreTraining
[[autodoc]] NezhaForPreTraining
- forward
## NezhaForMaskedLM
[[autodoc]] NezhaForMaskedLM
- forward
## NezhaForNextSentencePrediction
[[autodoc]] NezhaForNextSentencePrediction
- forward
## NezhaForSequenceClassification
[[autodoc]] NezhaForSequenceClassification
- forward
## NezhaForMultipleChoice
[[autodoc]] NezhaForMultipleChoice
- forward
## NezhaForTokenClassification
[[autodoc]] NezhaForTokenClassification
- forward
## NezhaForQuestionAnswering
[[autodoc]] NezhaForQuestionAnswering
- forward | huggingface/transformers/blob/main/docs/source/en/model_doc/nezha.md |
!---
Copyright 2023 The HuggingFace Team. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
-->
# 🤗 Transformers Notebooks
You can find here a list of the official notebooks provided by Hugging Face.
Also, we would like to list here interesting content created by the community.
If you wrote some notebook(s) leveraging 🤗 Transformers and would like to be listed here, please open a
Pull Request so it can be included under the Community notebooks.
## Hugging Face's notebooks 🤗
### Documentation notebooks
You can open any page of the documentation as a notebook in Colab (there is a button directly on said pages) but they are also listed here if you need them:
| Notebook | Description | | |
|:----------|:-------------|:-------------|------:|
| [Quicktour of the library](https://github.com/huggingface/notebooks/blob/main/transformers_doc/en/quicktour.ipynb) | A presentation of the various APIs in Transformers |[](https://colab.research.google.com/github/huggingface/notebooks/blob/main/transformers_doc/en/quicktour.ipynb)| [](https://studiolab.sagemaker.aws/import/github/huggingface/notebooks/blob/main/en/transformers_doc/quicktour.ipynb)|
| [Summary of the tasks](https://github.com/huggingface/notebooks/blob/main/transformers_doc/en/task_summary.ipynb) | How to run the models of the Transformers library task by task |[](https://colab.research.google.com/github/huggingface/notebooks/blob/main/transformers_doc/en/task_summary.ipynb)| [](https://studiolab.sagemaker.aws/import/github/huggingface/notebooks/blob/main/transformers_doc/en/task_summary.ipynb)|
| [Preprocessing data](https://github.com/huggingface/notebooks/blob/main/transformers_doc/en/preprocessing.ipynb) | How to use a tokenizer to preprocess your data |[](https://colab.research.google.com/github/huggingface/notebooks/blob/main/transformers_doc/en/preprocessing.ipynb)| [](https://studiolab.sagemaker.aws/import/github/huggingface/notebooks/blob/main/transformers_doc/en/preprocessing.ipynb)|
| [Fine-tuning a pretrained model](https://github.com/huggingface/notebooks/blob/main/transformers_doc/en/training.ipynb) | How to use the Trainer to fine-tune a pretrained model |[](https://colab.research.google.com/github/huggingface/notebooks/blob/main/transformers_doc/en/training.ipynb)| [](https://studiolab.sagemaker.aws/import/github/huggingface/notebooks/blob/main/transformers_doc/en/training.ipynb)|
| [Summary of the tokenizers](https://github.com/huggingface/notebooks/blob/main/transformers_doc/en/tokenizer_summary.ipynb) | The differences between the tokenizers algorithm |[](https://colab.research.google.com/github/huggingface/notebooks/blob/main/transformers_doc/en/tokenizer_summary.ipynb)| [](https://studiolab.sagemaker.aws/import/github/huggingface/notebooks/blob/main/transformers_doc/en/tokenizer_summary.ipynb)|
| [Multilingual models](https://github.com/huggingface/notebooks/blob/main/transformers_doc/en/multilingual.ipynb) | How to use the multilingual models of the library |[](https://colab.research.google.com/github/huggingface/notebooks/blob/main/transformers_doc/en/multilingual.ipynb)| [](https://studiolab.sagemaker.aws/import/github/huggingface/notebooks/blob/main/transformers_doc/en/multilingual.ipynb)|
### PyTorch Examples
#### Natural Language Processing[[pytorch-nlp]]
| Notebook | Description | | |
|:----------|:-------------|:-------------|------:|
| [Train your tokenizer](https://github.com/huggingface/notebooks/blob/main/examples/tokenizer_training.ipynb) | How to train and use your very own tokenizer |[](https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/tokenizer_training.ipynb)| [](https://studiolab.sagemaker.aws/import/github/huggingface/notebooks/blob/main/examples/tokenizer_training.ipynb)|
| [Train your language model](https://github.com/huggingface/notebooks/blob/main/examples/language_modeling_from_scratch.ipynb) | How to easily start using transformers |[](https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/language_modeling_from_scratch.ipynb)| [](https://studiolab.sagemaker.aws/import/github/huggingface/notebooks/blob/main/examples/language_modeling_from_scratch.ipynb)|
| [How to fine-tune a model on text classification](https://github.com/huggingface/notebooks/blob/main/examples/text_classification.ipynb)| Show how to preprocess the data and fine-tune a pretrained model on any GLUE task. | [](https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/text_classification.ipynb)| [](https://studiolab.sagemaker.aws/import/github/huggingface/notebooks/blob/main/examples/text_classification.ipynb)|
| [How to fine-tune a model on language modeling](https://github.com/huggingface/notebooks/blob/main/examples/language_modeling.ipynb)| Show how to preprocess the data and fine-tune a pretrained model on a causal or masked LM task. | [](https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/language_modeling.ipynb)| [](https://studiolab.sagemaker.aws/import/github/huggingface/notebooks/blob/main/examples/language_modeling.ipynb)|
| [How to fine-tune a model on token classification](https://github.com/huggingface/notebooks/blob/main/examples/token_classification.ipynb)| Show how to preprocess the data and fine-tune a pretrained model on a token classification task (NER, PoS). | [](https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/token_classification.ipynb)| [](https://studiolab.sagemaker.aws/import/github/huggingface/notebooks/blob/main/examples/token_classification.ipynb)|
| [How to fine-tune a model on question answering](https://github.com/huggingface/notebooks/blob/main/examples/question_answering.ipynb)| Show how to preprocess the data and fine-tune a pretrained model on SQUAD. | [](https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/question_answering.ipynb)| [](https://studiolab.sagemaker.aws/import/github/huggingface/notebooks/blob/main/examples/question_answering.ipynb)|
| [How to fine-tune a model on multiple choice](https://github.com/huggingface/notebooks/blob/main/examples/multiple_choice.ipynb)| Show how to preprocess the data and fine-tune a pretrained model on SWAG. | [](https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/multiple_choice.ipynb)| [](https://studiolab.sagemaker.aws/import/github/huggingface/notebooks/blob/main/examples/multiple_choice.ipynb)|
| [How to fine-tune a model on translation](https://github.com/huggingface/notebooks/blob/main/examples/translation.ipynb)| Show how to preprocess the data and fine-tune a pretrained model on WMT. | [](https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/translation.ipynb)| [](https://studiolab.sagemaker.aws/import/github/huggingface/notebooks/blob/main/examples/translation.ipynb)|
| [How to fine-tune a model on summarization](https://github.com/huggingface/notebooks/blob/main/examples/summarization.ipynb)| Show how to preprocess the data and fine-tune a pretrained model on XSUM. | [](https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/summarization.ipynb)| [](https://studiolab.sagemaker.aws/import/github/huggingface/notebooks/blob/main/examples/summarization.ipynb)|
| [How to train a language model from scratch](https://github.com/huggingface/blog/blob/main/notebooks/01_how_to_train.ipynb)| Highlight all the steps to effectively train Transformer model on custom data | [](https://colab.research.google.com/github/huggingface/blog/blob/main/notebooks/01_how_to_train.ipynb)| [](https://studiolab.sagemaker.aws/import/github/huggingface/blog/blob/main/notebooks/01_how_to_train.ipynb)|
| [How to generate text](https://github.com/huggingface/blog/blob/main/notebooks/02_how_to_generate.ipynb)| How to use different decoding methods for language generation with transformers | [](https://colab.research.google.com/github/huggingface/blog/blob/main/notebooks/02_how_to_generate.ipynb)| [](https://studiolab.sagemaker.aws/import/github/huggingface/blog/blob/main/notebooks/02_how_to_generate.ipynb)|
| [How to generate text (with constraints)](https://github.com/huggingface/blog/blob/main/notebooks/53_constrained_beam_search.ipynb)| How to guide language generation with user-provided constraints | [](https://colab.research.google.com/github/huggingface/blog/blob/main/notebooks/53_constrained_beam_search.ipynb)| [](https://studiolab.sagemaker.aws/import/github/huggingface/blog/blob/main/notebooks/53_constrained_beam_search.ipynb)|
| [Reformer](https://github.com/huggingface/blog/blob/main/notebooks/03_reformer.ipynb)| How Reformer pushes the limits of language modeling | [](https://colab.research.google.com/github/patrickvonplaten/blog/blob/main/notebooks/03_reformer.ipynb)| [](https://studiolab.sagemaker.aws/import/github/patrickvonplaten/blog/blob/main/notebooks/03_reformer.ipynb)|
#### Computer Vision[[pytorch-cv]]
| Notebook | Description | | |
|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|------:|
| [How to fine-tune a model on image classification (Torchvision)](https://github.com/huggingface/notebooks/blob/main/examples/image_classification.ipynb) | Show how to preprocess the data using Torchvision and fine-tune any pretrained Vision model on Image Classification | [](https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/image_classification.ipynb) | [](https://studiolab.sagemaker.aws/import/github/huggingface/notebooks/blob/main/examples/image_classification.ipynb)|
| [How to fine-tune a model on image classification (Albumentations)](https://github.com/huggingface/notebooks/blob/main/examples/image_classification_albumentations.ipynb) | Show how to preprocess the data using Albumentations and fine-tune any pretrained Vision model on Image Classification | [](https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/image_classification_albumentations.ipynb) | [](https://studiolab.sagemaker.aws/import/github/huggingface/notebooks/blob/main/examples/image_classification_albumentations.ipynb)|
| [How to fine-tune a model on image classification (Kornia)](https://github.com/huggingface/notebooks/blob/main/examples/image_classification_kornia.ipynb) | Show how to preprocess the data using Kornia and fine-tune any pretrained Vision model on Image Classification | [](https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/image_classification_kornia.ipynb) | [](https://studiolab.sagemaker.aws/import/github/huggingface/notebooks/blob/main/examples/image_classification_kornia.ipynb)|
| [How to perform zero-shot object detection with OWL-ViT](https://github.com/huggingface/notebooks/blob/main/examples/zeroshot_object_detection_with_owlvit.ipynb) | Show how to perform zero-shot object detection on images with text queries | [](https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/zeroshot_object_detection_with_owlvit.ipynb)| [](https://studiolab.sagemaker.aws/import/github/huggingface/notebooks/blob/main/examples/zeroshot_object_detection_with_owlvit.ipynb)|
| [How to fine-tune an image captioning model](https://github.com/huggingface/notebooks/blob/main/examples/image_captioning_blip.ipynb) | Show how to fine-tune BLIP for image captioning on a custom dataset | [](https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/image_captioning_blip.ipynb) | [](https://studiolab.sagemaker.aws/import/github/huggingface/notebooks/blob/main/examples/image_captioning_blip.ipynb)|
| [How to build an image similarity system with Transformers](https://github.com/huggingface/notebooks/blob/main/examples/image_similarity.ipynb) | Show how to build an image similarity system | [](https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/image_similarity.ipynb) | [](https://studiolab.sagemaker.aws/import/github/huggingface/notebooks/blob/main/examples/image_similarity.ipynb)|
| [How to fine-tune a SegFormer model on semantic segmentation](https://github.com/huggingface/notebooks/blob/main/examples/semantic_segmentation.ipynb) | Show how to preprocess the data and fine-tune a pretrained SegFormer model on Semantic Segmentation | [](https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/semantic_segmentation.ipynb) | [](https://studiolab.sagemaker.aws/import/github/huggingface/notebooks/blob/main/examples/semantic_segmentation.ipynb)|
| [How to fine-tune a VideoMAE model on video classification](https://github.com/huggingface/notebooks/blob/main/examples/video_classification.ipynb) | Show how to preprocess the data and fine-tune a pretrained VideoMAE model on Video Classification | [](https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/video_classification.ipynb) | [](https://studiolab.sagemaker.aws/import/github/huggingface/notebooks/blob/main/examples/video_classification.ipynb)|
#### Audio[[pytorch-audio]]
| Notebook | Description | | |
|:----------|:-------------|:-------------|------:|
| [How to fine-tune a speech recognition model in English](https://github.com/huggingface/notebooks/blob/main/examples/speech_recognition.ipynb)| Show how to preprocess the data and fine-tune a pretrained Speech model on TIMIT | [](https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/speech_recognition.ipynb)| [](https://studiolab.sagemaker.aws/import/github/huggingface/notebooks/blob/main/examples/speech_recognition.ipynb)|
| [How to fine-tune a speech recognition model in any language](https://github.com/huggingface/notebooks/blob/main/examples/multi_lingual_speech_recognition.ipynb)| Show how to preprocess the data and fine-tune a multi-lingually pretrained speech model on Common Voice | [](https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/multi_lingual_speech_recognition.ipynb)| [](https://studiolab.sagemaker.aws/import/github/huggingface/notebooks/blob/main/examples/multi_lingual_speech_recognition.ipynb)|
| [How to fine-tune a model on audio classification](https://github.com/huggingface/notebooks/blob/main/examples/audio_classification.ipynb)| Show how to preprocess the data and fine-tune a pretrained Speech model on Keyword Spotting | [](https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/audio_classification.ipynb)| [](https://studiolab.sagemaker.aws/import/github/huggingface/notebooks/blob/main/examples/audio_classification.ipynb)|
#### Biological Sequences[[pytorch-bio]]
| Notebook | Description | | |
|:----------|:----------------------------------------------------------------------------------------|:-------------|------:|
| [How to fine-tune a pre-trained protein model](https://github.com/huggingface/notebooks/blob/main/examples/protein_language_modeling.ipynb) | See how to tokenize proteins and fine-tune a large pre-trained protein "language" model | [](https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/protein_language_modeling.ipynb) | [](https://studiolab.sagemaker.aws/import/github/huggingface/notebooks/blob/main/examples/protein_language_modeling.ipynb) |
| [How to generate protein folds](https://github.com/huggingface/notebooks/blob/main/examples/protein_folding.ipynb) | See how to go from protein sequence to a full protein model and PDB file | [](https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/protein_folding.ipynb) | [](https://studiolab.sagemaker.aws/import/github/huggingface/notebooks/blob/main/examples/protein_folding.ipynb) |
| [How to fine-tune a Nucleotide Transformer model](https://github.com/huggingface/notebooks/blob/main/examples/nucleotide_transformer_dna_sequence_modelling.ipynb) | See how to tokenize DNA and fine-tune a large pre-trained DNA "language" model | [](https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/nucleotide_transformer_dna_sequence_modelling.ipynb) | [](https://studiolab.sagemaker.aws/import/github/huggingface/notebooks/blob/main/examples/nucleotide_transformer_dna_sequence_modelling.ipynb) |
| [Fine-tune a Nucleotide Transformer model with LoRA](https://github.com/huggingface/notebooks/blob/main/examples/nucleotide_transformer_dna_sequence_modelling_with_peft.ipynb) | Train even larger DNA models in a memory-efficient way | [](https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/nucleotide_transformer_dna_sequence_modelling_with_peft.ipynb) | [](https://studiolab.sagemaker.aws/import/github/huggingface/notebooks/blob/main/examples/nucleotide_transformer_dna_sequence_modelling_with_peft.ipynb) |
#### Other modalities[[pytorch-other]]
| Notebook | Description | | |
|:----------|:----------------------------------------------------------------------------------------|:-------------|------:|
| [Probabilistic Time Series Forecasting](https://github.com/huggingface/notebooks/blob/main/examples/time-series-transformers.ipynb) | See how to train Time Series Transformer on a custom dataset | [](https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/time-series-transformers.ipynb) | [](https://studiolab.sagemaker.aws/import/github/huggingface/notebooks/blob/main/examples/time-series-transformers.ipynb) |
#### Utility notebooks[[pytorch-utility]]
| Notebook | Description | | |
|:----------|:-------------|:-------------|------:|
| [How to export model to ONNX](https://github.com/huggingface/notebooks/blob/main/examples/onnx-export.ipynb)| Highlight how to export and run inference workloads through ONNX |
| [How to use Benchmarks](https://github.com/huggingface/notebooks/blob/main/examples/benchmark.ipynb)| How to benchmark models with transformers | [](https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/benchmark.ipynb)| [](https://studiolab.sagemaker.aws/import/github/huggingface/notebooks/blob/main/examples/benchmark.ipynb)|
### TensorFlow Examples
#### Natural Language Processing[[tensorflow-nlp]]
| Notebook | Description | | |
|:----------|:-------------|:-------------|------:|
| [Train your tokenizer](https://github.com/huggingface/notebooks/blob/main/examples/tokenizer_training.ipynb) | How to train and use your very own tokenizer |[](https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/tokenizer_training.ipynb)| [](https://studiolab.sagemaker.aws/import/github/huggingface/notebooks/blob/main/examples/tokenizer_training.ipynb)|
| [Train your language model](https://github.com/huggingface/notebooks/blob/main/examples/language_modeling_from_scratch-tf.ipynb) | How to easily start using transformers |[](https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/language_modeling_from_scratch-tf.ipynb)| [](https://studiolab.sagemaker.aws/import/github/huggingface/notebooks/blob/main/examples/language_modeling_from_scratch-tf.ipynb)|
| [How to fine-tune a model on text classification](https://github.com/huggingface/notebooks/blob/main/examples/text_classification-tf.ipynb)| Show how to preprocess the data and fine-tune a pretrained model on any GLUE task. | [](https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/text_classification-tf.ipynb)| [](https://studiolab.sagemaker.aws/import/github/huggingface/notebooks/blob/main/examples/text_classification-tf.ipynb)|
| [How to fine-tune a model on language modeling](https://github.com/huggingface/notebooks/blob/main/examples/language_modeling-tf.ipynb)| Show how to preprocess the data and fine-tune a pretrained model on a causal or masked LM task. | [](https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/language_modeling-tf.ipynb)| [](https://studiolab.sagemaker.aws/import/github/huggingface/notebooks/blob/main/examples/language_modeling-tf.ipynb)|
| [How to fine-tune a model on token classification](https://github.com/huggingface/notebooks/blob/main/examples/token_classification-tf.ipynb)| Show how to preprocess the data and fine-tune a pretrained model on a token classification task (NER, PoS). | [](https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/token_classification-tf.ipynb)| [](https://studiolab.sagemaker.aws/import/github/huggingface/notebooks/blob/main/examples/token_classification-tf.ipynb)|
| [How to fine-tune a model on question answering](https://github.com/huggingface/notebooks/blob/main/examples/question_answering-tf.ipynb)| Show how to preprocess the data and fine-tune a pretrained model on SQUAD. | [](https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/question_answering-tf.ipynb)| [](https://studiolab.sagemaker.aws/import/github/huggingface/notebooks/blob/main/examples/question_answering-tf.ipynb)|
| [How to fine-tune a model on multiple choice](https://github.com/huggingface/notebooks/blob/main/examples/multiple_choice-tf.ipynb)| Show how to preprocess the data and fine-tune a pretrained model on SWAG. | [](https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/multiple_choice-tf.ipynb)| [](https://studiolab.sagemaker.aws/import/github/huggingface/notebooks/blob/main/examples/multiple_choice-tf.ipynb)|
| [How to fine-tune a model on translation](https://github.com/huggingface/notebooks/blob/main/examples/translation-tf.ipynb)| Show how to preprocess the data and fine-tune a pretrained model on WMT. | [](https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/translation-tf.ipynb)| [](https://studiolab.sagemaker.aws/import/github/huggingface/notebooks/blob/main/examples/translation-tf.ipynb)|
| [How to fine-tune a model on summarization](https://github.com/huggingface/notebooks/blob/main/examples/summarization-tf.ipynb)| Show how to preprocess the data and fine-tune a pretrained model on XSUM. | [](https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/summarization-tf.ipynb)| [](https://studiolab.sagemaker.aws/import/github/huggingface/notebooks/blob/main/examples/summarization-tf.ipynb)|
#### Computer Vision[[tensorflow-cv]]
| Notebook | Description | | |
|:---------------------------------------------------------------------------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------|:-------------|------:|
| [How to fine-tune a model on image classification](https://github.com/huggingface/notebooks/blob/main/examples/image_classification-tf.ipynb) | Show how to preprocess the data and fine-tune any pretrained Vision model on Image Classification | [](https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/image_classification-tf.ipynb)| [](https://studiolab.sagemaker.aws/import/github/huggingface/notebooks/blob/main/examples/image_classification-tf.ipynb)|
| [How to fine-tune a SegFormer model on semantic segmentation](https://github.com/huggingface/notebooks/blob/main/examples/semantic_segmentation-tf.ipynb) | Show how to preprocess the data and fine-tune a pretrained SegFormer model on Semantic Segmentation | [](https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/semantic_segmentation-tf.ipynb)| [](https://studiolab.sagemaker.aws/import/github/huggingface/notebooks/blob/main/examples/semantic_segmentation-tf.ipynb)|
#### Biological Sequences[[tensorflow-bio]]
| Notebook | Description | | |
|:----------|:-------------|:-------------|------:|
| [How to fine-tune a pre-trained protein model](https://github.com/huggingface/notebooks/blob/main/examples/protein_language_modeling-tf.ipynb) | See how to tokenize proteins and fine-tune a large pre-trained protein "language" model | [](https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/protein_language_modeling-tf.ipynb) | [](https://studiolab.sagemaker.aws/import/github/huggingface/notebooks/blob/main/examples/protein_language_modeling-tf.ipynb) |
#### Utility notebooks[[tensorflow-utility]]
| Notebook | Description | | |
|:----------|:-------------|:-------------|------:|
| [How to train TF/Keras models on TPU](https://github.com/huggingface/notebooks/blob/main/examples/tpu_training-tf.ipynb) | See how to train at high speed on Google's TPU hardware | [](https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/tpu_training-tf.ipynb) | [](https://studiolab.sagemaker.aws/import/github/huggingface/notebooks/blob/main/examples/tpu_training-tf.ipynb) |
### Optimum notebooks
🤗 [Optimum](https://github.com/huggingface/optimum) is an extension of 🤗 Transformers, providing a set of performance optimization tools enabling maximum efficiency to train and run models on targeted hardwares.
| Notebook | Description | | |
|:----------|:-------------|:-------------|------:|
| [How to quantize a model with ONNX Runtime for text classification](https://github.com/huggingface/notebooks/blob/main/examples/text_classification_quantization_ort.ipynb)| Show how to apply static and dynamic quantization on a model using [ONNX Runtime](https://github.com/microsoft/onnxruntime) for any GLUE task. | [](https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/text_classification_quantization_ort.ipynb)| [](https://studiolab.sagemaker.aws/import/github/huggingface/notebooks/blob/main/examples/text_classification_quantization_ort.ipynb)|
| [How to quantize a model with Intel Neural Compressor for text classification](https://github.com/huggingface/notebooks/blob/main/examples/text_classification_quantization_inc.ipynb)| Show how to apply static, dynamic and aware training quantization on a model using [Intel Neural Compressor (INC)](https://github.com/intel/neural-compressor) for any GLUE task. | [](https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/text_classification_quantization_inc.ipynb)| [](https://studiolab.sagemaker.aws/import/github/huggingface/notebooks/blob/main/examples/text_classification_quantization_inc.ipynb)|
| [How to fine-tune a model on text classification with ONNX Runtime](https://github.com/huggingface/notebooks/blob/main/examples/text_classification_ort.ipynb)| Show how to preprocess the data and fine-tune a model on any GLUE task using [ONNX Runtime](https://github.com/microsoft/onnxruntime). | [](https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/text_classification_ort.ipynb)| [](https://studiolab.sagemaker.aws/import/github/huggingface/notebooks/blob/main/examples/text_classification_ort.ipynb)|
| [How to fine-tune a model on summarization with ONNX Runtime](https://github.com/huggingface/notebooks/blob/main/examples/summarization_ort.ipynb)| Show how to preprocess the data and fine-tune a model on XSUM using [ONNX Runtime](https://github.com/microsoft/onnxruntime). | [](https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/summarization_ort.ipynb)| [](https://studiolab.sagemaker.aws/import/github/huggingface/notebooks/blob/main/examples/summarization_ort.ipynb)|
## Community notebooks:
More notebooks developed by the community are available [here](https://hf.co/docs/transformers/community#community-notebooks).
| huggingface/transformers/blob/main/docs/source/en/notebooks.md |
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# SwitchTransformers
## Overview
The SwitchTransformers model was proposed in [Switch Transformers: Scaling to Trillion Parameter Models with Simple and Efficient Sparsity](https://arxiv.org/abs/2101.03961) by William Fedus, Barret Zoph, Noam Shazeer.
The Switch Transformer model uses a sparse T5 encoder-decoder architecture, where the MLP are replaced by a Mixture of Experts (MoE). A routing mechanism (top 1 in this case) associates each token to one of the expert, where each expert is a dense MLP. While switch transformers have a lot more weights than their equivalent dense models, the sparsity allows better scaling and better finetuning performance at scale.
During a forward pass, only a fraction of the weights are used. The routing mechanism allows the model to select relevant weights on the fly which increases the model capacity without increasing the number of operations.
The abstract from the paper is the following:
*In deep learning, models typically reuse the same parameters for all inputs. Mixture of Experts (MoE) defies this and instead selects different parameters for each incoming example. The result is a sparsely-activated model -- with outrageous numbers of parameters -- but a constant computational cost. However, despite several notable successes of MoE, widespread adoption has been hindered by complexity, communication costs and training instability -- we address these with the Switch Transformer. We simplify the MoE routing algorithm and design intuitive improved models with reduced communication and computational costs. Our proposed training techniques help wrangle the instabilities and we show large sparse models may be trained, for the first time, with lower precision (bfloat16) formats. We design models based off T5-Base and T5-Large to obtain up to 7x increases in pre-training speed with the same computational resources. These improvements extend into multilingual settings where we measure gains over the mT5-Base version across all 101 languages. Finally, we advance the current scale of language models by pre-training up to trillion parameter models on the "Colossal Clean Crawled Corpus" and achieve a 4x speedup over the T5-XXL model.*
This model was contributed by [Younes Belkada](https://huggingface.co/ybelkada) and [Arthur Zucker](https://huggingface.co/ArthurZ).
The original code can be found [here](https://github.com/google/flaxformer/tree/main/flaxformer/architectures/moe).
## Usage tips
- SwitchTransformers uses the [`T5Tokenizer`], which can be loaded directly from each model's repository.
- The released weights are pretrained on English [Masked Language Modeling](https://moon-ci-docs.huggingface.co/docs/transformers/pr_19323/en/glossary#general-terms) task, and should be finetuned.
## Resources
- [Translation task guide](../tasks/translation)
- [Summarization task guide](../tasks/summarization)
## SwitchTransformersConfig
[[autodoc]] SwitchTransformersConfig
## SwitchTransformersTop1Router
[[autodoc]] SwitchTransformersTop1Router
- _compute_router_probabilities
- forward
## SwitchTransformersSparseMLP
[[autodoc]] SwitchTransformersSparseMLP
- forward
## SwitchTransformersModel
[[autodoc]] SwitchTransformersModel
- forward
## SwitchTransformersForConditionalGeneration
[[autodoc]] SwitchTransformersForConditionalGeneration
- forward
## SwitchTransformersEncoderModel
[[autodoc]] SwitchTransformersEncoderModel
- forward
| huggingface/transformers/blob/main/docs/source/en/model_doc/switch_transformers.md |
Simple VQGAN CLIP
Author: @ErwannMillon
This is a very simple VQGAN-CLIP implementation that was built as a part of the <a href= "https://github.com/ErwannMillon/face-editor"> Face Editor project </a> . This simplified version allows you to generate or edit images using text with just three lines of code. For a more full featured implementation with masking, more advanced losses, and a full GUI, check out the Face Editor project.
By default this uses a CelebA checkpoint (for generating/editing faces), but also has an imagenet checkpoint that can be loaded by specifying vqgan_config and vqgan_checkpoint when instantiating VQGAN_CLIP.
Learning rate and iterations can be set by modifying vqgan_clip.lr and vqgan_clip.iterations .
You can edit images by passing `image_path` to the generate function.
See the generate function's docstring to learn more about how to format prompts.
## Usage
The easiest way to test this out is by <a href="https://colab.research.google.com/drive/1Ez4D1J6-hVkmlXeR5jBPWYyu6CLA9Yor?usp=sharing
">using the Colab demo</a>
To install locally:
- Clone this repo
- Install git-lfs (ubuntu: sudo apt-get install git-lfs , MacOS: brew install git-lfs)
In the root of the repo run:
```
conda create -n vqganclip python=3.8
conda activate vqganclip
git-lfs install
git clone https://huggingface.co/datasets/erwann/face_editor_model_ckpt model_checkpoints
pip install -r requirements.txt
```
### Generate new images
```
from VQGAN_CLIP import VQGAN_CLIP
vqgan_clip = VQGAN_CLIP()
vqgan_clip.generate("a picture of a smiling woman")
```
### Edit an image
To get a test image, run
`git clone https://huggingface.co/datasets/erwann/vqgan-clip-pic test_images`
To edit:
```
from VQGAN_CLIP import VQGAN_CLIP
vqgan_clip = VQGAN_CLIP()
vqgan_clip.lr = .07
vqgan_clip.iterations = 15
vqgan_clip.generate(
pos_prompts= ["a picture of a beautiful asian woman", "a picture of a woman from Japan"],
neg_prompts=["a picture of an Indian person", "a picture of a white person"],
image_path="./test_images/face.jpeg",
show_intermediate=True,
save_intermediate=True,
)
```
### Make an animation from the most recent generation
`vqgan_clip.make_animation()`
## Features:
- Positive and negative prompts
- Multiple prompts
- Prompt Weights
- Creating GIF animations of the transformations
- Wandb logging
| huggingface/transformers/blob/main/examples/research_projects/vqgan-clip/README.md |
!--Copyright 2022 The HuggingFace Team. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
the License. You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
specific language governing permissions and limitations under the License.
⚠️ Note that this file is in Markdown but contain specific syntax for our doc-builder (similar to MDX) that may not be
rendered properly in your Markdown viewer.
-->
# LongT5
## Overview
The LongT5 model was proposed in [LongT5: Efficient Text-To-Text Transformer for Long Sequences](https://arxiv.org/abs/2112.07916)
by Mandy Guo, Joshua Ainslie, David Uthus, Santiago Ontanon, Jianmo Ni, Yun-Hsuan Sung and Yinfei Yang. It's an
encoder-decoder transformer pre-trained in a text-to-text denoising generative setting. LongT5 model is an extension of
T5 model, and it enables using one of the two different efficient attention mechanisms - (1) Local attention, or (2)
Transient-Global attention.
The abstract from the paper is the following:
*Recent work has shown that either (1) increasing the input length or (2) increasing model size can improve the
performance of Transformer-based neural models. In this paper, we present a new model, called LongT5, with which we
explore the effects of scaling both the input length and model size at the same time. Specifically, we integrated
attention ideas from long-input transformers (ETC), and adopted pre-training strategies from summarization pre-training
(PEGASUS) into the scalable T5 architecture. The result is a new attention mechanism we call {\em Transient Global}
(TGlobal), which mimics ETC's local/global attention mechanism, but without requiring additional side-inputs. We are
able to achieve state-of-the-art results on several summarization tasks and outperform the original T5 models on
question answering tasks.*
This model was contributed by [stancld](https://huggingface.co/stancld).
The original code can be found [here](https://github.com/google-research/longt5).
## Usage tips
- [`LongT5ForConditionalGeneration`] is an extension of [`T5ForConditionalGeneration`] exchanging the traditional
encoder *self-attention* layer with efficient either *local* attention or *transient-global* (*tglobal*) attention.
- Unlike the T5 model, LongT5 does not use a task prefix. Furthermore, it uses a different pre-training objective
inspired by the pre-training of [`PegasusForConditionalGeneration`].
- LongT5 model is designed to work efficiently and very well on long-range *sequence-to-sequence* tasks where the
input sequence exceeds commonly used 512 tokens. It is capable of handling input sequences of a length up to 16,384 tokens.
- For *Local Attention*, the sparse sliding-window local attention operation allows a given token to attend only `r`
tokens to the left and right of it (with `r=127` by default). *Local Attention* does not introduce any new parameters
to the model. The complexity of the mechanism is linear in input sequence length `l`: `O(l*r)`.
- *Transient Global Attention* is an extension of the *Local Attention*. It, furthermore, allows each input token to
interact with all other tokens in the layer. This is achieved via splitting an input sequence into blocks of a fixed
length `k` (with a default `k=16`). Then, a global token for such a block is obtained via summing and normalizing the embeddings of every token
in the block. Thanks to this, the attention allows each token to attend to both nearby tokens like in Local attention, and
also every global token like in the case of standard global attention (*transient* represents the fact the global tokens
are constructed dynamically within each attention operation). As a consequence, *TGlobal* attention introduces
a few new parameters -- global relative position biases and a layer normalization for global token's embedding.
The complexity of this mechanism is `O(l(r + l/k))`.
- An example showing how to evaluate a fine-tuned LongT5 model on the [pubmed dataset](https://huggingface.co/datasets/scientific_papers) is below.
```python
>>> import evaluate
>>> from datasets import load_dataset
>>> from transformers import AutoTokenizer, LongT5ForConditionalGeneration
>>> dataset = load_dataset("scientific_papers", "pubmed", split="validation")
>>> model = (
... LongT5ForConditionalGeneration.from_pretrained("Stancld/longt5-tglobal-large-16384-pubmed-3k_steps")
... .to("cuda")
... .half()
... )
>>> tokenizer = AutoTokenizer.from_pretrained("Stancld/longt5-tglobal-large-16384-pubmed-3k_steps")
>>> def generate_answers(batch):
... inputs_dict = tokenizer(
... batch["article"], max_length=16384, padding="max_length", truncation=True, return_tensors="pt"
... )
... input_ids = inputs_dict.input_ids.to("cuda")
... attention_mask = inputs_dict.attention_mask.to("cuda")
... output_ids = model.generate(input_ids, attention_mask=attention_mask, max_length=512, num_beams=2)
... batch["predicted_abstract"] = tokenizer.batch_decode(output_ids, skip_special_tokens=True)
... return batch
>>> result = dataset.map(generate_answer, batched=True, batch_size=2)
>>> rouge = evaluate.load("rouge")
>>> rouge.compute(predictions=result["predicted_abstract"], references=result["abstract"])
```
## Resources
- [Translation task guide](../tasks/translation)
- [Summarization task guide](../tasks/summarization)
## LongT5Config
[[autodoc]] LongT5Config
<frameworkcontent>
<pt>
## LongT5Model
[[autodoc]] LongT5Model
- forward
## LongT5ForConditionalGeneration
[[autodoc]] LongT5ForConditionalGeneration
- forward
## LongT5EncoderModel
[[autodoc]] LongT5EncoderModel
- forward
</pt>
<jax>
## FlaxLongT5Model
[[autodoc]] FlaxLongT5Model
- __call__
- encode
- decode
## FlaxLongT5ForConditionalGeneration
[[autodoc]] FlaxLongT5ForConditionalGeneration
- __call__
- encode
- decode
</jax>
</frameworkcontent>
| huggingface/transformers/blob/main/docs/source/en/model_doc/longt5.md |
!--Copyright 2023 The HuggingFace Team. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
the License. You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
specific language governing permissions and limitations under the License.
⚠️ Note that this file is in Markdown but contain specific syntax for our doc-builder (similar to MDX) that may not be
rendered properly in your Markdown viewer.
-->
# MRA
## Overview
The MRA model was proposed in [Multi Resolution Analysis (MRA) for Approximate Self-Attention](https://arxiv.org/abs/2207.10284) by Zhanpeng Zeng, Sourav Pal, Jeffery Kline, Glenn M Fung, and Vikas Singh.
The abstract from the paper is the following:
*Transformers have emerged as a preferred model for many tasks in natural language processing and vision. Recent efforts on training and deploying Transformers more efficiently have identified many strategies to approximate the self-attention matrix, a key module in a Transformer architecture. Effective ideas include various prespecified sparsity patterns, low-rank basis expansions and combinations thereof. In this paper, we revisit classical Multiresolution Analysis (MRA) concepts such as Wavelets, whose potential value in this setting remains underexplored thus far. We show that simple approximations based on empirical feedback and design choices informed by modern hardware and implementation challenges, eventually yield a MRA-based approach for self-attention with an excellent performance profile across most criteria of interest. We undertake an extensive set of experiments and demonstrate that this multi-resolution scheme outperforms most efficient self-attention proposals and is favorable for both short and long sequences. Code is available at https://github.com/mlpen/mra-attention.*
This model was contributed by [novice03](https://huggingface.co/novice03).
The original code can be found [here](https://github.com/mlpen/mra-attention).
## MraConfig
[[autodoc]] MraConfig
## MraModel
[[autodoc]] MraModel
- forward
## MraForMaskedLM
[[autodoc]] MraForMaskedLM
- forward
## MraForSequenceClassification
[[autodoc]] MraForSequenceClassification
- forward
## MraForMultipleChoice
[[autodoc]] MraForMultipleChoice
- forward
## MraForTokenClassification
[[autodoc]] MraForTokenClassification
- forward
## MraForQuestionAnswering
[[autodoc]] MraForQuestionAnswering
- forward | huggingface/transformers/blob/main/docs/source/en/model_doc/mra.md |
!--Copyright 2022 The HuggingFace Team. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
the License. You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
specific language governing permissions and limitations under the License.
⚠️ Note that this file is in Markdown but contain specific syntax for our doc-builder (similar to MDX) that may not be
rendered properly in your Markdown viewer.
-->
# Instantiating a big model
When you want to use a very big pretrained model, one challenge is to minimize the use of the RAM. The usual workflow
from PyTorch is:
1. Create your model with random weights.
2. Load your pretrained weights.
3. Put those pretrained weights in your random model.
Step 1 and 2 both require a full version of the model in memory, which is not a problem in most cases, but if your model starts weighing several GigaBytes, those two copies can make you get out of RAM. Even worse, if you are using `torch.distributed` to launch a distributed training, each process will load the pretrained model and store these two copies in RAM.
<Tip>
Note that the randomly created model is initialized with "empty" tensors, which take the space in memory without filling it (thus the random values are whatever was in this chunk of memory at a given time). The random initialization following the appropriate distribution for the kind of model/parameters instantiated (like a normal distribution for instance) is only performed after step 3 on the non-initialized weights, to be as fast as possible!
</Tip>
In this guide, we explore the solutions Transformers offer to deal with this issue. Note that this is an area of active development, so the APIs explained here may change slightly in the future.
## Sharded checkpoints
Since version 4.18.0, model checkpoints that end up taking more than 10GB of space are automatically sharded in smaller pieces. In terms of having one single checkpoint when you do `model.save_pretrained(save_dir)`, you will end up with several partial checkpoints (each of which being of size < 10GB) and an index that maps parameter names to the files they are stored in.
You can control the maximum size before sharding with the `max_shard_size` parameter, so for the sake of an example, we'll use a normal-size models with a small shard size: let's take a traditional BERT model.
```py
from transformers import AutoModel
model = AutoModel.from_pretrained("bert-base-cased")
```
If you save it using [`~PreTrainedModel.save_pretrained`], you will get a new folder with two files: the config of the model and its weights:
```py
>>> import os
>>> import tempfile
>>> with tempfile.TemporaryDirectory() as tmp_dir:
... model.save_pretrained(tmp_dir)
... print(sorted(os.listdir(tmp_dir)))
['config.json', 'pytorch_model.bin']
```
Now let's use a maximum shard size of 200MB:
```py
>>> with tempfile.TemporaryDirectory() as tmp_dir:
... model.save_pretrained(tmp_dir, max_shard_size="200MB")
... print(sorted(os.listdir(tmp_dir)))
['config.json', 'pytorch_model-00001-of-00003.bin', 'pytorch_model-00002-of-00003.bin', 'pytorch_model-00003-of-00003.bin', 'pytorch_model.bin.index.json']
```
On top of the configuration of the model, we see three different weights files, and an `index.json` file which is our index. A checkpoint like this can be fully reloaded using the [`~PreTrainedModel.from_pretrained`] method:
```py
>>> with tempfile.TemporaryDirectory() as tmp_dir:
... model.save_pretrained(tmp_dir, max_shard_size="200MB")
... new_model = AutoModel.from_pretrained(tmp_dir)
```
The main advantage of doing this for big models is that during step 2 of the workflow shown above, each shard of the checkpoint is loaded after the previous one, capping the memory usage in RAM to the model size plus the size of the biggest shard.
Behind the scenes, the index file is used to determine which keys are in the checkpoint, and where the corresponding weights are stored. We can load that index like any json and get a dictionary:
```py
>>> import json
>>> with tempfile.TemporaryDirectory() as tmp_dir:
... model.save_pretrained(tmp_dir, max_shard_size="200MB")
... with open(os.path.join(tmp_dir, "pytorch_model.bin.index.json"), "r") as f:
... index = json.load(f)
>>> print(index.keys())
dict_keys(['metadata', 'weight_map'])
```
The metadata just consists of the total size of the model for now. We plan to add other information in the future:
```py
>>> index["metadata"]
{'total_size': 433245184}
```
The weights map is the main part of this index, which maps each parameter name (as usually found in a PyTorch model `state_dict`) to the file it's stored in:
```py
>>> index["weight_map"]
{'embeddings.LayerNorm.bias': 'pytorch_model-00001-of-00003.bin',
'embeddings.LayerNorm.weight': 'pytorch_model-00001-of-00003.bin',
...
```
If you want to directly load such a sharded checkpoint inside a model without using [`~PreTrainedModel.from_pretrained`] (like you would do `model.load_state_dict()` for a full checkpoint) you should use [`~modeling_utils.load_sharded_checkpoint`]:
```py
>>> from transformers.modeling_utils import load_sharded_checkpoint
>>> with tempfile.TemporaryDirectory() as tmp_dir:
... model.save_pretrained(tmp_dir, max_shard_size="200MB")
... load_sharded_checkpoint(model, tmp_dir)
```
## Low memory loading
Sharded checkpoints reduce the memory usage during step 2 of the workflow mentioned above, but in order to use that model in a low memory setting, we recommend leveraging our tools based on the Accelerate library.
Please read the following guide for more information: [Large model loading using Accelerate](./main_classes/model#large-model-loading)
| huggingface/transformers/blob/main/docs/source/en/big_models.md |
!--Copyright 2022 The HuggingFace Team. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
the License. You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
specific language governing permissions and limitations under the License.
⚠️ Note that this file is in Markdown but contain specific syntax for our doc-builder (similar to MDX) that may not be
rendered properly in your Markdown viewer.
-->
# MobileNet V1
## Overview
The MobileNet model was proposed in [MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications](https://arxiv.org/abs/1704.04861) by Andrew G. Howard, Menglong Zhu, Bo Chen, Dmitry Kalenichenko, Weijun Wang, Tobias Weyand, Marco Andreetto, Hartwig Adam.
The abstract from the paper is the following:
*We present a class of efficient models called MobileNets for mobile and embedded vision applications. MobileNets are based on a streamlined architecture that uses depth-wise separable convolutions to build light weight deep neural networks. We introduce two simple global hyper-parameters that efficiently trade off between latency and accuracy. These hyper-parameters allow the model builder to choose the right sized model for their application based on the constraints of the problem. We present extensive experiments on resource and accuracy tradeoffs and show strong performance compared to other popular models on ImageNet classification. We then demonstrate the effectiveness of MobileNets across a wide range of applications and use cases including object detection, finegrain classification, face attributes and large scale geo-localization.*
This model was contributed by [matthijs](https://huggingface.co/Matthijs). The original code and weights can be found [here](https://github.com/tensorflow/models/blob/master/research/slim/nets/mobilenet_v1.md).
## Usage tips
- The checkpoints are named **mobilenet\_v1\_*depth*\_*size***, for example **mobilenet\_v1\_1.0\_224**, where **1.0** is the depth multiplier (sometimes also referred to as "alpha" or the width multiplier) and **224** is the resolution of the input images the model was trained on.
- Even though the checkpoint is trained on images of specific size, the model will work on images of any size. The smallest supported image size is 32x32.
- One can use [`MobileNetV1ImageProcessor`] to prepare images for the model.
- The available image classification checkpoints are pre-trained on [ImageNet-1k](https://huggingface.co/datasets/imagenet-1k) (also referred to as ILSVRC 2012, a collection of 1.3 million images and 1,000 classes). However, the model predicts 1001 classes: the 1000 classes from ImageNet plus an extra “background” class (index 0).
- The original TensorFlow checkpoints use different padding rules than PyTorch, requiring the model to determine the padding amount at inference time, since this depends on the input image size. To use native PyTorch padding behavior, create a [`MobileNetV1Config`] with `tf_padding = False`.
Unsupported features:
- The [`MobileNetV1Model`] outputs a globally pooled version of the last hidden state. In the original model it is possible to use a 7x7 average pooling layer with stride 2 instead of global pooling. For larger inputs, this gives a pooled output that is larger than 1x1 pixel. The HuggingFace implementation does not support this.
- It is currently not possible to specify an `output_stride`. For smaller output strides, the original model invokes dilated convolution to prevent the spatial resolution from being reduced further. The output stride of the HuggingFace model is always 32.
- The original TensorFlow checkpoints include quantized models. We do not support these models as they include additional "FakeQuantization" operations to unquantize the weights.
- It's common to extract the output from the pointwise layers at indices 5, 11, 12, 13 for downstream purposes. Using `output_hidden_states=True` returns the output from all intermediate layers. There is currently no way to limit this to specific layers.
## Resources
A list of official Hugging Face and community (indicated by 🌎) resources to help you get started with MobileNetV1.
<PipelineTag pipeline="image-classification"/>
- [`MobileNetV1ForImageClassification`] is supported by this [example script](https://github.com/huggingface/transformers/tree/main/examples/pytorch/image-classification) and [notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/image_classification.ipynb).
- See also: [Image classification task guide](../tasks/image_classification)
If you're interested in submitting a resource to be included here, please feel free to open a Pull Request and we'll review it! The resource should ideally demonstrate something new instead of duplicating an existing resource.
## MobileNetV1Config
[[autodoc]] MobileNetV1Config
## MobileNetV1FeatureExtractor
[[autodoc]] MobileNetV1FeatureExtractor
- preprocess
## MobileNetV1ImageProcessor
[[autodoc]] MobileNetV1ImageProcessor
- preprocess
## MobileNetV1Model
[[autodoc]] MobileNetV1Model
- forward
## MobileNetV1ForImageClassification
[[autodoc]] MobileNetV1ForImageClassification
- forward
| huggingface/transformers/blob/main/docs/source/en/model_doc/mobilenet_v1.md |
!--Copyright 2022 The HuggingFace Team. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
the License. You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
specific language governing permissions and limitations under the License.
⚠️ Note that this file is in Markdown but contain specific syntax for our doc-builder (similar to MDX) that may not be
rendered properly in your Markdown viewer.
-->
# BLOOM
## Overview
The BLOOM model has been proposed with its various versions through the [BigScience Workshop](https://bigscience.huggingface.co/). BigScience is inspired by other open science initiatives where researchers have pooled their time and resources to collectively achieve a higher impact.
The architecture of BLOOM is essentially similar to GPT3 (auto-regressive model for next token prediction), but has been trained on 46 different languages and 13 programming languages.
Several smaller versions of the models have been trained on the same dataset. BLOOM is available in the following versions:
- [bloom-560m](https://huggingface.co/bigscience/bloom-560m)
- [bloom-1b1](https://huggingface.co/bigscience/bloom-1b1)
- [bloom-1b7](https://huggingface.co/bigscience/bloom-1b7)
- [bloom-3b](https://huggingface.co/bigscience/bloom-3b)
- [bloom-7b1](https://huggingface.co/bigscience/bloom-7b1)
- [bloom](https://huggingface.co/bigscience/bloom) (176B parameters)
## Resources
A list of official Hugging Face and community (indicated by 🌎) resources to help you get started with BLOOM. If you're interested in submitting a resource to be included here, please feel free to open a Pull Request and we'll review it! The resource should ideally demonstrate something new instead of duplicating an existing resource.
<PipelineTag pipeline="text-generation"/>
- [`BloomForCausalLM`] is supported by this [causal language modeling example script](https://github.com/huggingface/transformers/tree/main/examples/pytorch/language-modeling#gpt-2gpt-and-causal-language-modeling) and [notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/language_modeling.ipynb).
See also:
- [Causal language modeling task guide](../tasks/language_modeling)
- [Text classification task guide](../tasks/sequence_classification)
- [Token classification task guide](../tasks/token_classification)
- [Question answering task guide](../tasks/question_answering)
⚡️ Inference
- A blog on [Optimization story: Bloom inference](https://huggingface.co/blog/bloom-inference-optimization).
- A blog on [Incredibly Fast BLOOM Inference with DeepSpeed and Accelerate](https://huggingface.co/blog/bloom-inference-pytorch-scripts).
⚙️ Training
- A blog on [The Technology Behind BLOOM Training](https://huggingface.co/blog/bloom-megatron-deepspeed).
## BloomConfig
[[autodoc]] BloomConfig
- all
## BloomTokenizerFast
[[autodoc]] BloomTokenizerFast
- all
<frameworkcontent>
<pt>
## BloomModel
[[autodoc]] BloomModel
- forward
## BloomForCausalLM
[[autodoc]] BloomForCausalLM
- forward
## BloomForSequenceClassification
[[autodoc]] BloomForSequenceClassification
- forward
## BloomForTokenClassification
[[autodoc]] BloomForTokenClassification
- forward
## BloomForQuestionAnswering
[[autodoc]] BloomForQuestionAnswering
- forward
</pt>
<jax>
## FlaxBloomModel
[[autodoc]] FlaxBloomModel
- __call__
## FlaxBloomForCausalLM
[[autodoc]] FlaxBloomForCausalLM
- __call__
</jax>
</frameworkcontent>
| huggingface/transformers/blob/main/docs/source/en/model_doc/bloom.md |
!--Copyright 2023 The HuggingFace Team. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
the License. You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
specific language governing permissions and limitations under the License.
⚠️ Note that this file is in Markdown but contain specific syntax for our doc-builder (similar to MDX) that may not be
rendered properly in your Markdown viewer.
-->
# Quantization
Quantization techniques reduces memory and computational costs by representing weights and activations with lower-precision data types like 8-bit integers (int8). This enables loading larger models you normally wouldn't be able to fit into memory, and speeding up inference. Transformers supports the AWQ and GPTQ quantization algorithms and it supports 8-bit and 4-bit quantization with bitsandbytes.
<Tip>
Learn how to quantize models in the [Quantization](../quantization) guide.
</Tip>
## AwqConfig
[[autodoc]] AwqConfig
## GPTQConfig
[[autodoc]] GPTQConfig
## BitsAndBytesConfig
[[autodoc]] BitsAndBytesConfig
| huggingface/transformers/blob/main/docs/source/en/main_classes/quantization.md |
!--Copyright 2022 The HuggingFace Team. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
the License. You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
specific language governing permissions and limitations under the License.
⚠️ Note that this file is in Markdown but contain specific syntax for our doc-builder (similar to MDX) that may not be
rendered properly in your Markdown viewer.
-->
# REALM
## Overview
The REALM model was proposed in [REALM: Retrieval-Augmented Language Model Pre-Training](https://arxiv.org/abs/2002.08909) by Kelvin Guu, Kenton Lee, Zora Tung, Panupong Pasupat and Ming-Wei Chang. It's a
retrieval-augmented language model that firstly retrieves documents from a textual knowledge corpus and then
utilizes retrieved documents to process question answering tasks.
The abstract from the paper is the following:
*Language model pre-training has been shown to capture a surprising amount of world knowledge, crucial for NLP tasks
such as question answering. However, this knowledge is stored implicitly in the parameters of a neural network,
requiring ever-larger networks to cover more facts. To capture knowledge in a more modular and interpretable way, we
augment language model pre-training with a latent knowledge retriever, which allows the model to retrieve and attend
over documents from a large corpus such as Wikipedia, used during pre-training, fine-tuning and inference. For the
first time, we show how to pre-train such a knowledge retriever in an unsupervised manner, using masked language
modeling as the learning signal and backpropagating through a retrieval step that considers millions of documents. We
demonstrate the effectiveness of Retrieval-Augmented Language Model pre-training (REALM) by fine-tuning on the
challenging task of Open-domain Question Answering (Open-QA). We compare against state-of-the-art models for both
explicit and implicit knowledge storage on three popular Open-QA benchmarks, and find that we outperform all previous
methods by a significant margin (4-16% absolute accuracy), while also providing qualitative benefits such as
interpretability and modularity.*
This model was contributed by [qqaatw](https://huggingface.co/qqaatw). The original code can be found
[here](https://github.com/google-research/language/tree/master/language/realm).
## RealmConfig
[[autodoc]] RealmConfig
## RealmTokenizer
[[autodoc]] RealmTokenizer
- build_inputs_with_special_tokens
- get_special_tokens_mask
- create_token_type_ids_from_sequences
- save_vocabulary
- batch_encode_candidates
## RealmTokenizerFast
[[autodoc]] RealmTokenizerFast
- batch_encode_candidates
## RealmRetriever
[[autodoc]] RealmRetriever
## RealmEmbedder
[[autodoc]] RealmEmbedder
- forward
## RealmScorer
[[autodoc]] RealmScorer
- forward
## RealmKnowledgeAugEncoder
[[autodoc]] RealmKnowledgeAugEncoder
- forward
## RealmReader
[[autodoc]] RealmReader
- forward
## RealmForOpenQA
[[autodoc]] RealmForOpenQA
- block_embedding_to
- forward | huggingface/transformers/blob/main/docs/source/en/model_doc/realm.md |
!---
Copyright 2020 The HuggingFace Team. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
-->
<p align="center">
<picture>
<source media="(prefers-color-scheme: dark)" srcset="https://huggingface.co/datasets/huggingface/documentation-images/raw/main/transformers-logo-dark.svg">
<source media="(prefers-color-scheme: light)" srcset="https://huggingface.co/datasets/huggingface/documentation-images/raw/main/transformers-logo-light.svg">
<img alt="Hugging Face Transformers Library" src="https://huggingface.co/datasets/huggingface/documentation-images/raw/main/transformers-logo-light.svg" width="352" height="59" style="max-width: 100%;">
</picture>
<br/>
<br/>
</p>
<p align="center">
<a href="https://circleci.com/gh/huggingface/transformers">
<img alt="Build" src="https://img.shields.io/circleci/build/github/huggingface/transformers/main">
</a>
<a href="https://github.com/huggingface/transformers/blob/main/LICENSE">
<img alt="GitHub" src="https://img.shields.io/github/license/huggingface/transformers.svg?color=blue">
</a>
<a href="https://huggingface.co/docs/transformers/index">
<img alt="Documentation" src="https://img.shields.io/website/http/huggingface.co/docs/transformers/index.svg?down_color=red&down_message=offline&up_message=online">
</a>
<a href="https://github.com/huggingface/transformers/releases">
<img alt="GitHub release" src="https://img.shields.io/github/release/huggingface/transformers.svg">
</a>
<a href="https://github.com/huggingface/transformers/blob/main/CODE_OF_CONDUCT.md">
<img alt="Contributor Covenant" src="https://img.shields.io/badge/Contributor%20Covenant-v2.0%20adopted-ff69b4.svg">
</a>
<a href="https://zenodo.org/badge/latestdoi/155220641"><img src="https://zenodo.org/badge/155220641.svg" alt="DOI"></a>
</p>
<h4 align="center">
<p>
<a href="https://github.com/huggingface/transformers/">English</a> |
<a href="https://github.com/huggingface/transformers/blob/main/README_zh-hans.md">简体中文</a> |
<a href="https://github.com/huggingface/transformers/blob/main/README_zh-hant.md">繁體中文</a> |
<a href="https://github.com/huggingface/transformers/blob/main/README_ko.md">한국어</a> |
<a href="https://github.com/huggingface/transformers/blob/main/README_es.md">Español</a> |
<a href="https://github.com/huggingface/transformers/blob/main/README_ja.md">日本語</a> |
<a href="https://github.com/huggingface/transformers/blob/main/README_hd.md">हिन्दी</a> |
<a href="https://github.com/huggingface/transformers/blob/main/README_ru.md">Русский</a> |
<a href="https://github.com/huggingface/transformers/blob/main/README_pt-br.md">Рortuguês</a> |
<b>తెలుగు</b> |
</p>
</h4>
<h3 align="center">
<p>JAX, PyTorch మరియు TensorFlow కోసం అత్యాధునిక యంత్ర అభ్యాసం</p>
</h3>
<h3 align="center">
<a href="https://hf.co/course"><img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/course_banner.png"></a>
</h3>
🤗 ట్రాన్స్ఫార్మర్లు టెక్స్ట్, విజన్ మరియు ఆడియో వంటి విభిన్న పద్ధతులపై టాస్క్లను నిర్వహించడానికి వేలాది ముందుగా శిక్షణ పొందిన మోడల్లను అందిస్తాయి.
ఈ నమూనాలు వర్తించవచ్చు:
* 📝 టెక్స్ట్, 100కి పైగా భాషల్లో టెక్స్ట్ క్లాసిఫికేషన్, ఇన్ఫర్మేషన్ ఎక్స్ట్రాక్షన్, ప్రశ్నలకు సమాధానాలు, సారాంశం, అనువాదం, టెక్స్ట్ జనరేషన్ వంటి పనుల కోసం.
* 🖼️ ఇమేజ్లు, ఇమేజ్ వర్గీకరణ, ఆబ్జెక్ట్ డిటెక్షన్ మరియు సెగ్మెంటేషన్ వంటి పనుల కోసం.
* 🗣️ ఆడియో, స్పీచ్ రికగ్నిషన్ మరియు ఆడియో వర్గీకరణ వంటి పనుల కోసం.
ట్రాన్స్ఫార్మర్ మోడల్లు టేబుల్ క్వశ్చన్ ఆన్సర్ చేయడం, ఆప్టికల్ క్యారెక్టర్ రికగ్నిషన్, స్కాన్ చేసిన డాక్యుమెంట్ల నుండి ఇన్ఫర్మేషన్ ఎక్స్ట్రాక్షన్, వీడియో క్లాసిఫికేషన్ మరియు విజువల్ క్వశ్చన్ ఆన్సర్ చేయడం వంటి **అనేక పద్ధతులతో కలిపి** పనులను కూడా చేయగలవు.
🤗 ట్రాన్స్ఫార్మర్లు అందించిన టెక్స్ట్లో ప్రీట్రైన్డ్ మోడల్లను త్వరగా డౌన్లోడ్ చేయడానికి మరియు ఉపయోగించడానికి, వాటిని మీ స్వంత డేటాసెట్లలో ఫైన్-ట్యూన్ చేయడానికి మరియు వాటిని మా [మోడల్ హబ్](https://huggingface.co/models)లో సంఘంతో భాగస్వామ్యం చేయడానికి API లను అందిస్తుంది. అదే సమయంలో, ఆర్కిటెక్చర్ని నిర్వచించే ప్రతి పైథాన్ మాడ్యూల్ పూర్తిగా స్వతంత్రంగా ఉంటుంది మరియు త్వరిత పరిశోధన ప్రయోగాలను ప్రారంభించడానికి సవరించవచ్చు.
🤗 ట్రాన్స్ఫార్మర్లకు మూడు అత్యంత ప్రజాదరణ పొందిన డీప్ లెర్నింగ్ లైబ్రరీలు ఉన్నాయి — [Jax](https://jax.readthedocs.io/en/latest/), [PyTorch](https://pytorch.org/) మరియు [TensorFlow](https://www.tensorflow.org/) — వాటి మధ్య అతుకులు లేని ఏకీకరణతో. మీ మోడల్లను ఒకదానితో మరొకదానితో అనుమితి కోసం లోడ్ చేసే ముందు వాటికి శిక్షణ ఇవ్వడం చాలా సులభం.
## ఆన్లైన్ డెమోలు
మీరు [మోడల్ హబ్](https://huggingface.co/models) నుండి మా మోడళ్లలో చాలా వరకు వాటి పేజీలలో నేరుగా పరీక్షించవచ్చు. మేము పబ్లిక్ మరియు ప్రైవేట్ మోడల్ల కోసం [ప్రైవేట్ మోడల్ హోస్టింగ్, సంస్కరణ & అనుమితి API](https://huggingface.co/pricing)ని కూడా అందిస్తాము.
ఇక్కడ కొన్ని ఉదాహరణలు ఉన్నాయి:
సహజ భాషా ప్రాసెసింగ్లో:
- [BERT తో మాస్క్డ్ వర్డ్ కంప్లీషన్](https://huggingface.co/bert-base-uncased?text=Paris+is+the+%5BMASK%5D+of+France)
- [Electra తో పేరు ఎంటిటీ గుర్తింపు](https://huggingface.co/dbmdz/electra-large-discriminator-finetuned-conll03-english?text=My+name+is+Sarah+and+I+live+in+London+city)
- [GPT-2 తో టెక్స్ట్ జనరేషన్](https://huggingface.co/gpt2?text=A+long+time+ago%2C+)
- [RoBERTa తో సహజ భాషా అనుమితి](https://huggingface.co/roberta-large-mnli?text=The+dog+was+Lost.+Nobody+lost+any+animal)
- [BART తో సారాంశం](https://huggingface.co/facebook/bart-large-cnn?text=The+tower+is+324+metres+%281%2C063+ft%29+tall%2C+about+the+same+height+as+an+81-storey+building%2C+and+the+tallest+structure+in+Paris.+Its+base+is+square%2C+measuring+125+metres+%28410+ft%29+on+each+side.+During+its+construction%2C+the+Eiffel+Tower+surpassed+the+Washington+Monument+to+become+the+tallest+man-made+structure+in+the+world%2C+a+title+it+held+for+41+years+until+the+Chrysler+Building+in+New+York+City+was+finished+in+1930.+It+was+the+first+structure+to+reach+a+height+of+300+metres.+Due+to+the+addition+of+a+broadcasting+aerial+at+the+top+of+the+tower+in+1957%2C+it+is+now+taller+than+the+Chrysler+Building+by+5.2+metres+%2817+ft%29.+Excluding+transmitters%2C+the+Eiffel+Tower+is+the+second+tallest+free-standing+structure+in+France+after+the+Millau+Viaduct)
- [DistilBERT తో ప్రశ్న సమాధానం](https://huggingface.co/distilbert-base-uncased-distilled-squad?text=Which+name+is+also+used+to+describe+the+Amazon+rainforest+in+English%3F&context=The+Amazon+rainforest+%28Portuguese%3A+Floresta+Amaz%C3%B4nica+or+Amaz%C3%B4nia%3B+Spanish%3A+Selva+Amaz%C3%B3nica%2C+Amazon%C3%ADa+or+usually+Amazonia%3B+French%3A+For%C3%AAt+amazonienne%3B+Dutch%3A+Amazoneregenwoud%29%2C+also+known+in+English+as+Amazonia+or+the+Amazon+Jungle%2C+is+a+moist+broadleaf+forest+that+covers+most+of+the+Amazon+basin+of+South+America.+This+basin+encompasses+7%2C000%2C000+square+kilometres+%282%2C700%2C000+sq+mi%29%2C+of+which+5%2C500%2C000+square+kilometres+%282%2C100%2C000+sq+mi%29+are+covered+by+the+rainforest.+This+region+includes+territory+belonging+to+nine+nations.+The+majority+of+the+forest+is+contained+within+Brazil%2C+with+60%25+of+the+rainforest%2C+followed+by+Peru+with+13%25%2C+Colombia+with+10%25%2C+and+with+minor+amounts+in+Venezuela%2C+Ecuador%2C+Bolivia%2C+Guyana%2C+Suriname+and+French+Guiana.+States+or+departments+in+four+nations+contain+%22Amazonas%22+in+their+names.+The+Amazon+represents+over+half+of+the+planet%27s+remaining+rainforests%2C+and+comprises+the+largest+and+most+biodiverse+tract+of+tropical+rainforest+in+the+world%2C+with+an+estimated+390+billion+individual+trees+divided+into+16%2C000+species)
- [T5 తో అనువాదం](https://huggingface.co/t5-base?text=My+name+is+Wolfgang+and+I+live+in+Berlin)
కంప్యూటర్ దృష్టిలో:
- [VIT తో చిత్ర వర్గీకరణ](https://huggingface.co/google/vit-base-patch16-224)
- [DETR తో ఆబ్జెక్ట్ డిటెక్షన్](https://huggingface.co/facebook/detr-resnet-50)
- [SegFormer తో సెమాంటిక్ సెగ్మెంటేషన్](https://huggingface.co/nvidia/segformer-b0-finetuned-ade-512-512)
- [MaskFormer తో పానోప్టిక్ సెగ్మెంటేషన్](https://huggingface.co/facebook/maskformer-swin-small-coco)
- [DPT తో లోతు అంచనా](https://huggingface.co/docs/transformers/model_doc/dpt)
- [VideoMAE తో వీడియో వర్గీకరణ](https://huggingface.co/docs/transformers/model_doc/videomae)
- [OneFormer తో యూనివర్సల్ సెగ్మెంటేషన్](https://huggingface.co/shi-labs/oneformer_ade20k_dinat_large)
ఆడియోలో:
- [Wav2Vec2 తో ఆటోమేటిక్ స్పీచ్ రికగ్నిషన్](https://huggingface.co/facebook/wav2vec2-base-960h)
- [Wav2Vec2 తో కీవర్డ్ స్పాటింగ్](https://huggingface.co/superb/wav2vec2-base-superb-ks)
- [ఆడియో స్పెక్ట్రోగ్రామ్ ట్రాన్స్ఫార్మర్తో ఆడియో వర్గీకరణ](https://huggingface.co/MIT/ast-finetuned-audioset-10-10-0.4593)
మల్టీమోడల్ టాస్క్లలో:
- [TAPAS తో టేబుల్ ప్రశ్న సమాధానాలు](https://huggingface.co/google/tapas-base-finetuned-wtq)
- [ViLT తో దృశ్యమాన ప్రశ్నకు సమాధానం](https://huggingface.co/dandelin/vilt-b32-finetuned-vqa)
- [CLIP తో జీరో-షాట్ ఇమేజ్ వర్గీకరణ](https://huggingface.co/openai/clip-vit-large-patch14)
- [LayoutLM తో డాక్యుమెంట్ ప్రశ్నకు సమాధానం](https://huggingface.co/impira/layoutlm-document-qa)
- [X-CLIP తో జీరో-షాట్ వీడియో వర్గీకరణ](https://huggingface.co/docs/transformers/model_doc/xclip)
## ట్రాన్స్ఫార్మర్లను ఉపయోగించి 100 ప్రాజెక్టులు
ట్రాన్స్ఫార్మర్లు ప్రీట్రైన్డ్ మోడల్లను ఉపయోగించడానికి టూల్కిట్ కంటే ఎక్కువ: ఇది దాని చుట్టూ నిర్మించిన ప్రాజెక్ట్ల సంఘం మరియు
హగ్గింగ్ ఫేస్ హబ్. డెవలపర్లు, పరిశోధకులు, విద్యార్థులు, ప్రొఫెసర్లు, ఇంజనీర్లు మరియు ఎవరినైనా అనుమతించేలా ట్రాన్స్ఫార్మర్లను మేము కోరుకుంటున్నాము
వారి కలల ప్రాజెక్టులను నిర్మించడానికి.
ట్రాన్స్ఫార్మర్ల 100,000 నక్షత్రాలను జరుపుకోవడానికి, మేము స్పాట్లైట్ని ఉంచాలని నిర్ణయించుకున్నాము
సంఘం, మరియు మేము 100 జాబితాలను కలిగి ఉన్న [awesome-transformers](./awesome-transformers.md) పేజీని సృష్టించాము.
ట్రాన్స్ఫార్మర్ల పరిసరాల్లో అద్భుతమైన ప్రాజెక్టులు నిర్మించబడ్డాయి.
జాబితాలో భాగమని మీరు విశ్వసించే ప్రాజెక్ట్ను మీరు కలిగి ఉంటే లేదా ఉపయోగిస్తుంటే, దయచేసి దానిని జోడించడానికి PRని తెరవండి!
## మీరు హగ్గింగ్ ఫేస్ టీమ్ నుండి అనుకూల మద్దతు కోసం చూస్తున్నట్లయితే
<a target="_blank" href="https://huggingface.co/support">
<img alt="HuggingFace Expert Acceleration Program" src="https://cdn-media.huggingface.co/marketing/transformers/new-support-improved.png" style="max-width: 600px; border: 1px solid #eee; border-radius: 4px; box-shadow: 0 1px 2px 0 rgba(0, 0, 0, 0.05);">
</a><br>
## త్వరిత పర్యటన
ఇచ్చిన ఇన్పుట్ (టెక్స్ట్, ఇమేజ్, ఆడియో, ...)పై తక్షణమే మోడల్ను ఉపయోగించడానికి, మేము `pipeline` API ని అందిస్తాము. పైప్లైన్లు ఆ మోడల్ శిక్షణ సమయంలో ఉపయోగించిన ప్రీప్రాసెసింగ్తో కూడిన ప్రీట్రైన్డ్ మోడల్ను సమూహపరుస్తాయి. సానుకూల మరియు ప్రతికూల పాఠాలను వర్గీకరించడానికి పైప్లైన్ను త్వరగా ఎలా ఉపయోగించాలో ఇక్కడ ఉంది:
```python
>>> from transformers import pipeline
# Allocate a pipeline for sentiment-analysis
>>> classifier = pipeline('sentiment-analysis')
>>> classifier('We are very happy to introduce pipeline to the transformers repository.')
[{'label': 'POSITIVE', 'score': 0.9996980428695679}]
```
రెండవ లైన్ కోడ్ డౌన్లోడ్ మరియు పైప్లైన్ ఉపయోగించే ప్రీట్రైన్డ్ మోడల్ను కాష్ చేస్తుంది, మూడవది ఇచ్చిన టెక్స్ట్పై మూల్యాంకనం చేస్తుంది. ఇక్కడ సమాధానం 99.97% విశ్వాసంతో "పాజిటివ్".
చాలా పనులు NLPలో కానీ కంప్యూటర్ విజన్ మరియు స్పీచ్లో కూడా ముందుగా శిక్షణ పొందిన `pipeline` సిద్ధంగా ఉన్నాయి. ఉదాహరణకు, మనం చిత్రంలో గుర్తించిన వస్తువులను సులభంగా సంగ్రహించవచ్చు:
``` python
>>> import requests
>>> from PIL import Image
>>> from transformers import pipeline
# Download an image with cute cats
>>> url = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/coco_sample.png"
>>> image_data = requests.get(url, stream=True).raw
>>> image = Image.open(image_data)
# Allocate a pipeline for object detection
>>> object_detector = pipeline('object-detection')
>>> object_detector(image)
[{'score': 0.9982201457023621,
'label': 'remote',
'box': {'xmin': 40, 'ymin': 70, 'xmax': 175, 'ymax': 117}},
{'score': 0.9960021376609802,
'label': 'remote',
'box': {'xmin': 333, 'ymin': 72, 'xmax': 368, 'ymax': 187}},
{'score': 0.9954745173454285,
'label': 'couch',
'box': {'xmin': 0, 'ymin': 1, 'xmax': 639, 'ymax': 473}},
{'score': 0.9988006353378296,
'label': 'cat',
'box': {'xmin': 13, 'ymin': 52, 'xmax': 314, 'ymax': 470}},
{'score': 0.9986783862113953,
'label': 'cat',
'box': {'xmin': 345, 'ymin': 23, 'xmax': 640, 'ymax': 368}}]
```
ఇక్కడ మనం ఆబ్జెక్ట్ చుట్టూ ఉన్న బాక్స్ మరియు కాన్ఫిడెన్స్ స్కోర్తో చిత్రంలో గుర్తించబడిన వస్తువుల జాబితాను పొందుతాము. ఇక్కడ ఎడమవైపున ఉన్న అసలు చిత్రం, కుడివైపున అంచనాలు ప్రదర్శించబడతాయి:
<h3 align="center">
<a><img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/coco_sample.png" width="400"></a>
<a><img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/coco_sample_post_processed.png" width="400"></a>
</h3>
మీరు [ఈ ట్యుటోరియల్](https://huggingface.co/docs/transformers/task_summary)లో `pipeline` API ద్వారా సపోర్ట్ చేసే టాస్క్ల గురించి మరింత తెలుసుకోవచ్చు.
`pipeline`తో పాటు, మీరు ఇచ్చిన టాస్క్లో ఏదైనా ప్రీట్రైన్డ్ మోడల్లను డౌన్లోడ్ చేయడానికి మరియు ఉపయోగించడానికి, దీనికి మూడు లైన్ల కోడ్ సరిపోతుంది. ఇక్కడ PyTorch వెర్షన్ ఉంది:
```python
>>> from transformers import AutoTokenizer, AutoModel
>>> tokenizer = AutoTokenizer.from_pretrained("bert-base-uncased")
>>> model = AutoModel.from_pretrained("bert-base-uncased")
>>> inputs = tokenizer("Hello world!", return_tensors="pt")
>>> outputs = model(**inputs)
```
మరియు TensorFlow కి సమానమైన కోడ్ ఇక్కడ ఉంది:
```python
>>> from transformers import AutoTokenizer, TFAutoModel
>>> tokenizer = AutoTokenizer.from_pretrained("bert-base-uncased")
>>> model = TFAutoModel.from_pretrained("bert-base-uncased")
>>> inputs = tokenizer("Hello world!", return_tensors="tf")
>>> outputs = model(**inputs)
```
ప్రిట్రైన్డ్ మోడల్ ఆశించే అన్ని ప్రీప్రాసెసింగ్లకు టోకెనైజర్ బాధ్యత వహిస్తుంది మరియు నేరుగా ఒకే స్ట్రింగ్ (పై ఉదాహరణలలో వలె) లేదా జాబితాపై కాల్ చేయవచ్చు. ఇది మీరు డౌన్స్ట్రీమ్ కోడ్లో ఉపయోగించగల నిఘంటువుని అవుట్పుట్ చేస్తుంది లేదా ** ఆర్గ్యుమెంట్ అన్ప్యాకింగ్ ఆపరేటర్ని ఉపయోగించి నేరుగా మీ మోడల్కి పంపుతుంది.
మోడల్ కూడా సాధారణ [Pytorch `nn.Module`](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) లేదా [TensorFlow `tf.keras.Model`]( https://www.tensorflow.org/api_docs/python/tf/keras/Model) (మీ బ్యాకెండ్ని బట్టి) మీరు మామూలుగా ఉపయోగించవచ్చు. [ఈ ట్యుటోరియల్](https://huggingface.co/docs/transformers/training) అటువంటి మోడల్ని క్లాసిక్ PyTorch లేదా TensorFlow ట్రైనింగ్ లూప్లో ఎలా ఇంటిగ్రేట్ చేయాలో లేదా మా `Trainer` API ని ఎలా ఉపయోగించాలో వివరిస్తుంది కొత్త డేటాసెట్.
## నేను ట్రాన్స్ఫార్మర్లను ఎందుకు ఉపయోగించాలి?
1. ఉపయోగించడానికి సులభమైన స్టేట్ ఆఫ్ ది ఆర్ట్ మోడల్లు:
- సహజ భాషా అవగాహన & ఉత్పత్తి, కంప్యూటర్ దృష్టి మరియు ఆడియో పనులపై అధిక పనితీరు.
- విద్యావేత్తలు మరియు అభ్యాసకుల ప్రవేశానికి తక్కువ అవరోధం.
- తెలుసుకోవడానికి కేవలం మూడు తరగతులతో కొన్ని వినియోగదారు-ముఖ సంగ్రహణలు.
- మా అన్ని ప్రీట్రైన్డ్ మోడల్లను ఉపయోగించడం కోసం ఏకీకృత API.
2. తక్కువ గణన ఖర్చులు, చిన్న కార్బన్ పాదముద్ర:
- పరిశోధకులు ఎల్లప్పుడూ మళ్లీ శిక్షణ పొందే బదులు శిక్షణ పొందిన నమూనాలను పంచుకోవచ్చు.
- అభ్యాసకులు గణన సమయాన్ని మరియు ఉత్పత్తి ఖర్చులను తగ్గించగలరు.
- అన్ని పద్ధతుల్లో 60,000 కంటే ఎక్కువ ప్రీట్రైన్డ్ మోడల్లతో డజన్ల కొద్దీ ఆర్కిటెక్చర్లు.
3. మోడల్ జీవితకాలంలో ప్రతి భాగానికి సరైన ఫ్రేమ్వర్క్ను ఎంచుకోండి:
- 3 లైన్ల కోడ్లో స్టేట్ ఆఫ్ ది ఆర్ట్ మోడల్లకు శిక్షణ ఇవ్వండి.
- TF2.0/PyTorch/JAX ఫ్రేమ్వర్క్ల మధ్య ఒకే మోడల్ను ఇష్టానుసారంగా తరలించండి.
- శిక్షణ, మూల్యాంకనం మరియు ఉత్పత్తి కోసం సరైన ఫ్రేమ్వర్క్ను సజావుగా ఎంచుకోండి.
4. మీ అవసరాలకు అనుగుణంగా మోడల్ లేదా ఉదాహరణను సులభంగా అనుకూలీకరించండి:
- ప్రతి ఆర్కిటెక్చర్ దాని అసలు రచయితలు ప్రచురించిన ఫలితాలను పునరుత్పత్తి చేయడానికి మేము ఉదాహరణలను అందిస్తాము.
- మోడల్ ఇంటర్నల్లు వీలైనంత స్థిరంగా బహిర్గతమవుతాయి.
- శీఘ్ర ప్రయోగాల కోసం లైబ్రరీ నుండి స్వతంత్రంగా మోడల్ ఫైల్లను ఉపయోగించవచ్చు.
## నేను ట్రాన్స్ఫార్మర్లను ఎందుకు ఉపయోగించకూడదు?
- ఈ లైబ్రరీ న్యూరల్ నెట్ల కోసం బిల్డింగ్ బ్లాక్ల మాడ్యులర్ టూల్బాక్స్ కాదు. మోడల్ ఫైల్లలోని కోడ్ ఉద్దేశపూర్వకంగా అదనపు సంగ్రహణలతో రీఫ్యాక్టరింగ్ చేయబడదు, తద్వారా పరిశోధకులు అదనపు సంగ్రహణలు/ఫైళ్లలోకి ప్రవేశించకుండా ప్రతి మోడల్పై త్వరగా మళ్లించగలరు.
- శిక్షణ API ఏ మోడల్లో పని చేయడానికి ఉద్దేశించబడలేదు కానీ లైబ్రరీ అందించిన మోడల్లతో పని చేయడానికి ఆప్టిమైజ్ చేయబడింది. సాధారణ మెషిన్ లెర్నింగ్ లూప్ల కోసం, మీరు మరొక లైబ్రరీని ఉపయోగించాలి (బహుశా, [Accelerate](https://huggingface.co/docs/accelerate)).
- మేము వీలైనన్ని ఎక్కువ వినియోగ సందర్భాలను ప్రదర్శించడానికి ప్రయత్నిస్తున్నప్పుడు, మా [ఉదాహరణల ఫోల్డర్](https://github.com/huggingface/transformers/tree/main/examples)లోని స్క్రిప్ట్లు కేవలం: ఉదాహరణలు. మీ నిర్దిష్ట సమస్యపై అవి పని చేయవు మరియు వాటిని మీ అవసరాలకు అనుగుణంగా మార్చుకోవడానికి మీరు కొన్ని కోడ్ లైన్లను మార్చవలసి ఉంటుంది.
## సంస్థాపన
### పిప్ తో
ఈ రిపోజిటరీ పైథాన్ 3.8+, ఫ్లాక్స్ 0.4.1+, PyTorch 1.10+ మరియు TensorFlow 2.6+లో పరీక్షించబడింది.
మీరు [వర్చువల్ వాతావరణం](https://docs.python.org/3/library/venv.html)లో 🤗 ట్రాన్స్ఫార్మర్లను ఇన్స్టాల్ చేయాలి. మీకు పైథాన్ వర్చువల్ పరిసరాల గురించి తెలియకుంటే, [యూజర్ గైడ్](https://packaging.python.org/guides/installing-using-pip-and-virtual-environments/) చూడండి.
ముందుగా, మీరు ఉపయోగించబోతున్న పైథాన్ వెర్షన్తో వర్చువల్ వాతావరణాన్ని సృష్టించండి మరియు దానిని సక్రియం చేయండి.
అప్పుడు, మీరు ఫ్లాక్స్, పైటార్చ్ లేదా టెన్సర్ఫ్లోలో కనీసం ఒకదానిని ఇన్స్టాల్ చేయాలి.
దయచేసి [TensorFlow ఇన్స్టాలేషన్ పేజీ](https://www.tensorflow.org/install/), [PyTorch ఇన్స్టాలేషన్ పేజీ](https://pytorch.org/get-started/locally/#start-locally) మరియు/ని చూడండి లేదా మీ ప్లాట్ఫారమ్ కోసం నిర్దిష్ట ఇన్స్టాలేషన్ కమాండ్కు సంబంధించి [Flax](https://github.com/google/flax#quick-install) మరియు [Jax](https://github.com/google/jax#installation) ఇన్స్టాలేషన్ పేజీలు .
ఆ బ్యాకెండ్లలో ఒకటి ఇన్స్టాల్ చేయబడినప్పుడు, 🤗 ట్రాన్స్ఫార్మర్లను ఈ క్రింది విధంగా పిప్ని ఉపయోగించి ఇన్స్టాల్ చేయవచ్చు:
```bash
pip install transformers
```
మీరు ఉదాహరణలతో ప్లే చేయాలనుకుంటే లేదా కోడ్ యొక్క బ్లీడింగ్ ఎడ్జ్ అవసరం మరియు కొత్త విడుదల కోసం వేచి ఉండలేకపోతే, మీరు తప్పనిసరిగా [మూలం నుండి లైబ్రరీని ఇన్స్టాల్ చేయాలి](https://huggingface.co/docs/transformers/installation#installing-from-source).
### కొండా తో
ట్రాన్స్ఫార్మర్స్ వెర్షన్ v4.0.0 నుండి, మేము ఇప్పుడు కొండా ఛానెల్ని కలిగి ఉన్నాము: `huggingface`.
🤗 కింది విధంగా కొండా ఉపయోగించి ట్రాన్స్ఫార్మర్లను ఇన్స్టాల్ చేయవచ్చు:
```shell script
conda install -c huggingface transformers
```
Flax, PyTorch లేదా TensorFlow యొక్క ఇన్స్టాలేషన్ పేజీలను కొండాతో ఎలా ఇన్స్టాల్ చేయాలో చూడటానికి వాటిని అనుసరించండి.
> **_గమనిక:_** Windowsలో, కాషింగ్ నుండి ప్రయోజనం పొందేందుకు మీరు డెవలపర్ మోడ్ని సక్రియం చేయమని ప్రాంప్ట్ చేయబడవచ్చు. ఇది మీకు ఎంపిక కాకపోతే, దయచేసి [ఈ సంచిక](https://github.com/huggingface/huggingface_hub/issues/1062)లో మాకు తెలియజేయండి.
## మోడల్ ఆర్కిటెక్చర్లు
**[అన్ని మోడల్ చెక్పాయింట్లు](https://huggingface.co/models)** 🤗 అందించిన ట్రాన్స్ఫార్మర్లు huggingface.co [model hub](https://huggingface.co/models) నుండి సజావుగా ఏకీకృతం చేయబడ్డాయి [users](https://huggingface.co/users) మరియు [organizations](https://huggingface.co/organizations) ద్వారా నేరుగా అప్లోడ్ చేయబడతాయి.
ప్రస్తుత తనిఖీ కేంద్రాల సంఖ్య: 
🤗 ట్రాన్స్ఫార్మర్లు ప్రస్తుతం కింది ఆర్కిటెక్చర్లను అందజేస్తున్నాయి (వాటిలో ప్రతి ఒక్కటి ఉన్నత స్థాయి సారాంశం కోసం [ఇక్కడ](https://huggingface.co/docs/transformers/model_summary) చూడండి):
1. **[ALBERT](https://huggingface.co/docs/transformers/model_doc/albert)** (from Google Research and the Toyota Technological Institute at Chicago) released with the paper [ALBERT: A Lite BERT for Self-supervised Learning of Language Representations](https://arxiv.org/abs/1909.11942), by Zhenzhong Lan, Mingda Chen, Sebastian Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut.
1. **[ALIGN](https://huggingface.co/docs/transformers/model_doc/align)** (from Google Research) released with the paper [Scaling Up Visual and Vision-Language Representation Learning With Noisy Text Supervision](https://arxiv.org/abs/2102.05918) by Chao Jia, Yinfei Yang, Ye Xia, Yi-Ting Chen, Zarana Parekh, Hieu Pham, Quoc V. Le, Yunhsuan Sung, Zhen Li, Tom Duerig.
1. **[AltCLIP](https://huggingface.co/docs/transformers/model_doc/altclip)** (from BAAI) released with the paper [AltCLIP: Altering the Language Encoder in CLIP for Extended Language Capabilities](https://arxiv.org/abs/2211.06679) by Chen, Zhongzhi and Liu, Guang and Zhang, Bo-Wen and Ye, Fulong and Yang, Qinghong and Wu, Ledell.
1. **[Audio Spectrogram Transformer](https://huggingface.co/docs/transformers/model_doc/audio-spectrogram-transformer)** (from MIT) released with the paper [AST: Audio Spectrogram Transformer](https://arxiv.org/abs/2104.01778) by Yuan Gong, Yu-An Chung, James Glass.
1. **[Autoformer](https://huggingface.co/docs/transformers/model_doc/autoformer)** (from Tsinghua University) released with the paper [Autoformer: Decomposition Transformers with Auto-Correlation for Long-Term Series Forecasting](https://arxiv.org/abs/2106.13008) by Haixu Wu, Jiehui Xu, Jianmin Wang, Mingsheng Long.
1. **[Bark](https://huggingface.co/docs/transformers/model_doc/bark)** (from Suno) released in the repository [suno-ai/bark](https://github.com/suno-ai/bark) by Suno AI team.
1. **[BART](https://huggingface.co/docs/transformers/model_doc/bart)** (from Facebook) released with the paper [BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension](https://arxiv.org/abs/1910.13461) by Mike Lewis, Yinhan Liu, Naman Goyal, Marjan Ghazvininejad, Abdelrahman Mohamed, Omer Levy, Ves Stoyanov, and Luke Zettlemoyer.
1. **[BARThez](https://huggingface.co/docs/transformers/model_doc/barthez)** (from École polytechnique) released with the paper [BARThez: a Skilled Pretrained French Sequence-to-Sequence Model](https://arxiv.org/abs/2010.12321) by Moussa Kamal Eddine, Antoine J.-P. Tixier, Michalis Vazirgiannis.
1. **[BARTpho](https://huggingface.co/docs/transformers/model_doc/bartpho)** (from VinAI Research) released with the paper [BARTpho: Pre-trained Sequence-to-Sequence Models for Vietnamese](https://arxiv.org/abs/2109.09701) by Nguyen Luong Tran, Duong Minh Le and Dat Quoc Nguyen.
1. **[BEiT](https://huggingface.co/docs/transformers/model_doc/beit)** (from Microsoft) released with the paper [BEiT: BERT Pre-Training of Image Transformers](https://arxiv.org/abs/2106.08254) by Hangbo Bao, Li Dong, Furu Wei.
1. **[BERT](https://huggingface.co/docs/transformers/model_doc/bert)** (from Google) released with the paper [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://arxiv.org/abs/1810.04805) by Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova.
1. **[BERT For Sequence Generation](https://huggingface.co/docs/transformers/model_doc/bert-generation)** (from Google) released with the paper [Leveraging Pre-trained Checkpoints for Sequence Generation Tasks](https://arxiv.org/abs/1907.12461) by Sascha Rothe, Shashi Narayan, Aliaksei Severyn.
1. **[BERTweet](https://huggingface.co/docs/transformers/model_doc/bertweet)** (from VinAI Research) released with the paper [BERTweet: A pre-trained language model for English Tweets](https://aclanthology.org/2020.emnlp-demos.2/) by Dat Quoc Nguyen, Thanh Vu and Anh Tuan Nguyen.
1. **[BigBird-Pegasus](https://huggingface.co/docs/transformers/model_doc/bigbird_pegasus)** (from Google Research) released with the paper [Big Bird: Transformers for Longer Sequences](https://arxiv.org/abs/2007.14062) by Manzil Zaheer, Guru Guruganesh, Avinava Dubey, Joshua Ainslie, Chris Alberti, Santiago Ontanon, Philip Pham, Anirudh Ravula, Qifan Wang, Li Yang, Amr Ahmed.
1. **[BigBird-RoBERTa](https://huggingface.co/docs/transformers/model_doc/big_bird)** (from Google Research) released with the paper [Big Bird: Transformers for Longer Sequences](https://arxiv.org/abs/2007.14062) by Manzil Zaheer, Guru Guruganesh, Avinava Dubey, Joshua Ainslie, Chris Alberti, Santiago Ontanon, Philip Pham, Anirudh Ravula, Qifan Wang, Li Yang, Amr Ahmed.
1. **[BioGpt](https://huggingface.co/docs/transformers/model_doc/biogpt)** (from Microsoft Research AI4Science) released with the paper [BioGPT: generative pre-trained transformer for biomedical text generation and mining](https://academic.oup.com/bib/advance-article/doi/10.1093/bib/bbac409/6713511?guestAccessKey=a66d9b5d-4f83-4017-bb52-405815c907b9) by Renqian Luo, Liai Sun, Yingce Xia, Tao Qin, Sheng Zhang, Hoifung Poon and Tie-Yan Liu.
1. **[BiT](https://huggingface.co/docs/transformers/model_doc/bit)** (from Google AI) released with the paper [Big Transfer (BiT): General Visual Representation Learning](https://arxiv.org/abs/1912.11370) by Alexander Kolesnikov, Lucas Beyer, Xiaohua Zhai, Joan Puigcerver, Jessica Yung, Sylvain Gelly, Neil Houlsby.
1. **[Blenderbot](https://huggingface.co/docs/transformers/model_doc/blenderbot)** (from Facebook) released with the paper [Recipes for building an open-domain chatbot](https://arxiv.org/abs/2004.13637) by Stephen Roller, Emily Dinan, Naman Goyal, Da Ju, Mary Williamson, Yinhan Liu, Jing Xu, Myle Ott, Kurt Shuster, Eric M. Smith, Y-Lan Boureau, Jason Weston.
1. **[BlenderbotSmall](https://huggingface.co/docs/transformers/model_doc/blenderbot-small)** (from Facebook) released with the paper [Recipes for building an open-domain chatbot](https://arxiv.org/abs/2004.13637) by Stephen Roller, Emily Dinan, Naman Goyal, Da Ju, Mary Williamson, Yinhan Liu, Jing Xu, Myle Ott, Kurt Shuster, Eric M. Smith, Y-Lan Boureau, Jason Weston.
1. **[BLIP](https://huggingface.co/docs/transformers/model_doc/blip)** (from Salesforce) released with the paper [BLIP: Bootstrapping Language-Image Pre-training for Unified Vision-Language Understanding and Generation](https://arxiv.org/abs/2201.12086) by Junnan Li, Dongxu Li, Caiming Xiong, Steven Hoi.
1. **[BLIP-2](https://huggingface.co/docs/transformers/model_doc/blip-2)** (from Salesforce) released with the paper [BLIP-2: Bootstrapping Language-Image Pre-training with Frozen Image Encoders and Large Language Models](https://arxiv.org/abs/2301.12597) by Junnan Li, Dongxu Li, Silvio Savarese, Steven Hoi.
1. **[BLOOM](https://huggingface.co/docs/transformers/model_doc/bloom)** (from BigScience workshop) released by the [BigScience Workshop](https://bigscience.huggingface.co/).
1. **[BORT](https://huggingface.co/docs/transformers/model_doc/bort)** (from Alexa) released with the paper [Optimal Subarchitecture Extraction For BERT](https://arxiv.org/abs/2010.10499) by Adrian de Wynter and Daniel J. Perry.
1. **[BridgeTower](https://huggingface.co/docs/transformers/model_doc/bridgetower)** (from Harbin Institute of Technology/Microsoft Research Asia/Intel Labs) released with the paper [BridgeTower: Building Bridges Between Encoders in Vision-Language Representation Learning](https://arxiv.org/abs/2206.08657) by Xiao Xu, Chenfei Wu, Shachar Rosenman, Vasudev Lal, Wanxiang Che, Nan Duan.
1. **[BROS](https://huggingface.co/docs/transformers/model_doc/bros)** (from NAVER CLOVA) released with the paper [BROS: A Pre-trained Language Model Focusing on Text and Layout for Better Key Information Extraction from Documents](https://arxiv.org/abs/2108.04539) by Teakgyu Hong, Donghyun Kim, Mingi Ji, Wonseok Hwang, Daehyun Nam, Sungrae Park.
1. **[ByT5](https://huggingface.co/docs/transformers/model_doc/byt5)** (from Google Research) released with the paper [ByT5: Towards a token-free future with pre-trained byte-to-byte models](https://arxiv.org/abs/2105.13626) by Linting Xue, Aditya Barua, Noah Constant, Rami Al-Rfou, Sharan Narang, Mihir Kale, Adam Roberts, Colin Raffel.
1. **[CamemBERT](https://huggingface.co/docs/transformers/model_doc/camembert)** (from Inria/Facebook/Sorbonne) released with the paper [CamemBERT: a Tasty French Language Model](https://arxiv.org/abs/1911.03894) by Louis Martin*, Benjamin Muller*, Pedro Javier Ortiz Suárez*, Yoann Dupont, Laurent Romary, Éric Villemonte de la Clergerie, Djamé Seddah and Benoît Sagot.
1. **[CANINE](https://huggingface.co/docs/transformers/model_doc/canine)** (from Google Research) released with the paper [CANINE: Pre-training an Efficient Tokenization-Free Encoder for Language Representation](https://arxiv.org/abs/2103.06874) by Jonathan H. Clark, Dan Garrette, Iulia Turc, John Wieting.
1. **[Chinese-CLIP](https://huggingface.co/docs/transformers/model_doc/chinese_clip)** (from OFA-Sys) released with the paper [Chinese CLIP: Contrastive Vision-Language Pretraining in Chinese](https://arxiv.org/abs/2211.01335) by An Yang, Junshu Pan, Junyang Lin, Rui Men, Yichang Zhang, Jingren Zhou, Chang Zhou.
1. **[CLAP](https://huggingface.co/docs/transformers/model_doc/clap)** (from LAION-AI) released with the paper [Large-scale Contrastive Language-Audio Pretraining with Feature Fusion and Keyword-to-Caption Augmentation](https://arxiv.org/abs/2211.06687) by Yusong Wu, Ke Chen, Tianyu Zhang, Yuchen Hui, Taylor Berg-Kirkpatrick, Shlomo Dubnov.
1. **[CLIP](https://huggingface.co/docs/transformers/model_doc/clip)** (from OpenAI) released with the paper [Learning Transferable Visual Models From Natural Language Supervision](https://arxiv.org/abs/2103.00020) by Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, Gretchen Krueger, Ilya Sutskever.
1. **[CLIPSeg](https://huggingface.co/docs/transformers/model_doc/clipseg)** (from University of Göttingen) released with the paper [Image Segmentation Using Text and Image Prompts](https://arxiv.org/abs/2112.10003) by Timo Lüddecke and Alexander Ecker.
1. **[CodeGen](https://huggingface.co/docs/transformers/model_doc/codegen)** (from Salesforce) released with the paper [A Conversational Paradigm for Program Synthesis](https://arxiv.org/abs/2203.13474) by Erik Nijkamp, Bo Pang, Hiroaki Hayashi, Lifu Tu, Huan Wang, Yingbo Zhou, Silvio Savarese, Caiming Xiong.
1. **[CodeLlama](https://huggingface.co/docs/transformers/model_doc/llama_code)** (from MetaAI) released with the paper [Code Llama: Open Foundation Models for Code](https://ai.meta.com/research/publications/code-llama-open-foundation-models-for-code/) by Baptiste Rozière, Jonas Gehring, Fabian Gloeckle, Sten Sootla, Itai Gat, Xiaoqing Ellen Tan, Yossi Adi, Jingyu Liu, Tal Remez, Jérémy Rapin, Artyom Kozhevnikov, Ivan Evtimov, Joanna Bitton, Manish Bhatt, Cristian Canton Ferrer, Aaron Grattafiori, Wenhan Xiong, Alexandre Défossez, Jade Copet, Faisal Azhar, Hugo Touvron, Louis Martin, Nicolas Usunier, Thomas Scialom, Gabriel Synnaeve.
1. **[Conditional DETR](https://huggingface.co/docs/transformers/model_doc/conditional_detr)** (from Microsoft Research Asia) released with the paper [Conditional DETR for Fast Training Convergence](https://arxiv.org/abs/2108.06152) by Depu Meng, Xiaokang Chen, Zejia Fan, Gang Zeng, Houqiang Li, Yuhui Yuan, Lei Sun, Jingdong Wang.
1. **[ConvBERT](https://huggingface.co/docs/transformers/model_doc/convbert)** (from YituTech) released with the paper [ConvBERT: Improving BERT with Span-based Dynamic Convolution](https://arxiv.org/abs/2008.02496) by Zihang Jiang, Weihao Yu, Daquan Zhou, Yunpeng Chen, Jiashi Feng, Shuicheng Yan.
1. **[ConvNeXT](https://huggingface.co/docs/transformers/model_doc/convnext)** (from Facebook AI) released with the paper [A ConvNet for the 2020s](https://arxiv.org/abs/2201.03545) by Zhuang Liu, Hanzi Mao, Chao-Yuan Wu, Christoph Feichtenhofer, Trevor Darrell, Saining Xie.
1. **[ConvNeXTV2](https://huggingface.co/docs/transformers/model_doc/convnextv2)** (from Facebook AI) released with the paper [ConvNeXt V2: Co-designing and Scaling ConvNets with Masked Autoencoders](https://arxiv.org/abs/2301.00808) by Sanghyun Woo, Shoubhik Debnath, Ronghang Hu, Xinlei Chen, Zhuang Liu, In So Kweon, Saining Xie.
1. **[CPM](https://huggingface.co/docs/transformers/model_doc/cpm)** (from Tsinghua University) released with the paper [CPM: A Large-scale Generative Chinese Pre-trained Language Model](https://arxiv.org/abs/2012.00413) by Zhengyan Zhang, Xu Han, Hao Zhou, Pei Ke, Yuxian Gu, Deming Ye, Yujia Qin, Yusheng Su, Haozhe Ji, Jian Guan, Fanchao Qi, Xiaozhi Wang, Yanan Zheng, Guoyang Zeng, Huanqi Cao, Shengqi Chen, Daixuan Li, Zhenbo Sun, Zhiyuan Liu, Minlie Huang, Wentao Han, Jie Tang, Juanzi Li, Xiaoyan Zhu, Maosong Sun.
1. **[CPM-Ant](https://huggingface.co/docs/transformers/model_doc/cpmant)** (from OpenBMB) released by the [OpenBMB](https://www.openbmb.org/).
1. **[CTRL](https://huggingface.co/docs/transformers/model_doc/ctrl)** (from Salesforce) released with the paper [CTRL: A Conditional Transformer Language Model for Controllable Generation](https://arxiv.org/abs/1909.05858) by Nitish Shirish Keskar*, Bryan McCann*, Lav R. Varshney, Caiming Xiong and Richard Socher.
1. **[CvT](https://huggingface.co/docs/transformers/model_doc/cvt)** (from Microsoft) released with the paper [CvT: Introducing Convolutions to Vision Transformers](https://arxiv.org/abs/2103.15808) by Haiping Wu, Bin Xiao, Noel Codella, Mengchen Liu, Xiyang Dai, Lu Yuan, Lei Zhang.
1. **[Data2Vec](https://huggingface.co/docs/transformers/model_doc/data2vec)** (from Facebook) released with the paper [Data2Vec: A General Framework for Self-supervised Learning in Speech, Vision and Language](https://arxiv.org/abs/2202.03555) by Alexei Baevski, Wei-Ning Hsu, Qiantong Xu, Arun Babu, Jiatao Gu, Michael Auli.
1. **[DeBERTa](https://huggingface.co/docs/transformers/model_doc/deberta)** (from Microsoft) released with the paper [DeBERTa: Decoding-enhanced BERT with Disentangled Attention](https://arxiv.org/abs/2006.03654) by Pengcheng He, Xiaodong Liu, Jianfeng Gao, Weizhu Chen.
1. **[DeBERTa-v2](https://huggingface.co/docs/transformers/model_doc/deberta-v2)** (from Microsoft) released with the paper [DeBERTa: Decoding-enhanced BERT with Disentangled Attention](https://arxiv.org/abs/2006.03654) by Pengcheng He, Xiaodong Liu, Jianfeng Gao, Weizhu Chen.
1. **[Decision Transformer](https://huggingface.co/docs/transformers/model_doc/decision_transformer)** (from Berkeley/Facebook/Google) released with the paper [Decision Transformer: Reinforcement Learning via Sequence Modeling](https://arxiv.org/abs/2106.01345) by Lili Chen, Kevin Lu, Aravind Rajeswaran, Kimin Lee, Aditya Grover, Michael Laskin, Pieter Abbeel, Aravind Srinivas, Igor Mordatch.
1. **[Deformable DETR](https://huggingface.co/docs/transformers/model_doc/deformable_detr)** (from SenseTime Research) released with the paper [Deformable DETR: Deformable Transformers for End-to-End Object Detection](https://arxiv.org/abs/2010.04159) by Xizhou Zhu, Weijie Su, Lewei Lu, Bin Li, Xiaogang Wang, Jifeng Dai.
1. **[DeiT](https://huggingface.co/docs/transformers/model_doc/deit)** (from Facebook) released with the paper [Training data-efficient image transformers & distillation through attention](https://arxiv.org/abs/2012.12877) by Hugo Touvron, Matthieu Cord, Matthijs Douze, Francisco Massa, Alexandre Sablayrolles, Hervé Jégou.
1. **[DePlot](https://huggingface.co/docs/transformers/model_doc/deplot)** (from Google AI) released with the paper [DePlot: One-shot visual language reasoning by plot-to-table translation](https://arxiv.org/abs/2212.10505) by Fangyu Liu, Julian Martin Eisenschlos, Francesco Piccinno, Syrine Krichene, Chenxi Pang, Kenton Lee, Mandar Joshi, Wenhu Chen, Nigel Collier, Yasemin Altun.
1. **[DETA](https://huggingface.co/docs/transformers/model_doc/deta)** (from The University of Texas at Austin) released with the paper [NMS Strikes Back](https://arxiv.org/abs/2212.06137) by Jeffrey Ouyang-Zhang, Jang Hyun Cho, Xingyi Zhou, Philipp Krähenbühl.
1. **[DETR](https://huggingface.co/docs/transformers/model_doc/detr)** (from Facebook) released with the paper [End-to-End Object Detection with Transformers](https://arxiv.org/abs/2005.12872) by Nicolas Carion, Francisco Massa, Gabriel Synnaeve, Nicolas Usunier, Alexander Kirillov, Sergey Zagoruyko.
1. **[DialoGPT](https://huggingface.co/docs/transformers/model_doc/dialogpt)** (from Microsoft Research) released with the paper [DialoGPT: Large-Scale Generative Pre-training for Conversational Response Generation](https://arxiv.org/abs/1911.00536) by Yizhe Zhang, Siqi Sun, Michel Galley, Yen-Chun Chen, Chris Brockett, Xiang Gao, Jianfeng Gao, Jingjing Liu, Bill Dolan.
1. **[DiNAT](https://huggingface.co/docs/transformers/model_doc/dinat)** (from SHI Labs) released with the paper [Dilated Neighborhood Attention Transformer](https://arxiv.org/abs/2209.15001) by Ali Hassani and Humphrey Shi.
1. **[DINOv2](https://huggingface.co/docs/transformers/model_doc/dinov2)** (from Meta AI) released with the paper [DINOv2: Learning Robust Visual Features without Supervision](https://arxiv.org/abs/2304.07193) by Maxime Oquab, Timothée Darcet, Théo Moutakanni, Huy Vo, Marc Szafraniec, Vasil Khalidov, Pierre Fernandez, Daniel Haziza, Francisco Massa, Alaaeldin El-Nouby, Mahmoud Assran, Nicolas Ballas, Wojciech Galuba, Russell Howes, Po-Yao Huang, Shang-Wen Li, Ishan Misra, Michael Rabbat, Vasu Sharma, Gabriel Synnaeve, Hu Xu, Hervé Jegou, Julien Mairal, Patrick Labatut, Armand Joulin, Piotr Bojanowski.
1. **[DistilBERT](https://huggingface.co/docs/transformers/model_doc/distilbert)** (from HuggingFace), released together with the paper [DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter](https://arxiv.org/abs/1910.01108) by Victor Sanh, Lysandre Debut and Thomas Wolf. The same method has been applied to compress GPT2 into [DistilGPT2](https://github.com/huggingface/transformers/tree/main/examples/research_projects/distillation), RoBERTa into [DistilRoBERTa](https://github.com/huggingface/transformers/tree/main/examples/research_projects/distillation), Multilingual BERT into [DistilmBERT](https://github.com/huggingface/transformers/tree/main/examples/research_projects/distillation) and a German version of DistilBERT.
1. **[DiT](https://huggingface.co/docs/transformers/model_doc/dit)** (from Microsoft Research) released with the paper [DiT: Self-supervised Pre-training for Document Image Transformer](https://arxiv.org/abs/2203.02378) by Junlong Li, Yiheng Xu, Tengchao Lv, Lei Cui, Cha Zhang, Furu Wei.
1. **[Donut](https://huggingface.co/docs/transformers/model_doc/donut)** (from NAVER), released together with the paper [OCR-free Document Understanding Transformer](https://arxiv.org/abs/2111.15664) by Geewook Kim, Teakgyu Hong, Moonbin Yim, Jeongyeon Nam, Jinyoung Park, Jinyeong Yim, Wonseok Hwang, Sangdoo Yun, Dongyoon Han, Seunghyun Park.
1. **[DPR](https://huggingface.co/docs/transformers/model_doc/dpr)** (from Facebook) released with the paper [Dense Passage Retrieval for Open-Domain Question Answering](https://arxiv.org/abs/2004.04906) by Vladimir Karpukhin, Barlas Oğuz, Sewon Min, Patrick Lewis, Ledell Wu, Sergey Edunov, Danqi Chen, and Wen-tau Yih.
1. **[DPT](https://huggingface.co/docs/transformers/master/model_doc/dpt)** (from Intel Labs) released with the paper [Vision Transformers for Dense Prediction](https://arxiv.org/abs/2103.13413) by René Ranftl, Alexey Bochkovskiy, Vladlen Koltun.
1. **[EfficientFormer](https://huggingface.co/docs/transformers/model_doc/efficientformer)** (from Snap Research) released with the paper [EfficientFormer: Vision Transformers at MobileNetSpeed](https://arxiv.org/abs/2206.01191) by Yanyu Li, Geng Yuan, Yang Wen, Ju Hu, Georgios Evangelidis, Sergey Tulyakov, Yanzhi Wang, Jian Ren.
1. **[EfficientNet](https://huggingface.co/docs/transformers/model_doc/efficientnet)** (from Google Brain) released with the paper [EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks](https://arxiv.org/abs/1905.11946) by Mingxing Tan, Quoc V. Le.
1. **[ELECTRA](https://huggingface.co/docs/transformers/model_doc/electra)** (from Google Research/Stanford University) released with the paper [ELECTRA: Pre-training text encoders as discriminators rather than generators](https://arxiv.org/abs/2003.10555) by Kevin Clark, Minh-Thang Luong, Quoc V. Le, Christopher D. Manning.
1. **[EnCodec](https://huggingface.co/docs/transformers/model_doc/encodec)** (from Meta AI) released with the paper [High Fidelity Neural Audio Compression](https://arxiv.org/abs/2210.13438) by Alexandre Défossez, Jade Copet, Gabriel Synnaeve, Yossi Adi.
1. **[EncoderDecoder](https://huggingface.co/docs/transformers/model_doc/encoder-decoder)** (from Google Research) released with the paper [Leveraging Pre-trained Checkpoints for Sequence Generation Tasks](https://arxiv.org/abs/1907.12461) by Sascha Rothe, Shashi Narayan, Aliaksei Severyn.
1. **[ERNIE](https://huggingface.co/docs/transformers/model_doc/ernie)** (from Baidu) released with the paper [ERNIE: Enhanced Representation through Knowledge Integration](https://arxiv.org/abs/1904.09223) by Yu Sun, Shuohuan Wang, Yukun Li, Shikun Feng, Xuyi Chen, Han Zhang, Xin Tian, Danxiang Zhu, Hao Tian, Hua Wu.
1. **[ErnieM](https://huggingface.co/docs/transformers/model_doc/ernie_m)** (from Baidu) released with the paper [ERNIE-M: Enhanced Multilingual Representation by Aligning Cross-lingual Semantics with Monolingual Corpora](https://arxiv.org/abs/2012.15674) by Xuan Ouyang, Shuohuan Wang, Chao Pang, Yu Sun, Hao Tian, Hua Wu, Haifeng Wang.
1. **[ESM](https://huggingface.co/docs/transformers/model_doc/esm)** (from Meta AI) are transformer protein language models. **ESM-1b** was released with the paper [Biological structure and function emerge from scaling unsupervised learning to 250 million protein sequences](https://www.pnas.org/content/118/15/e2016239118) by Alexander Rives, Joshua Meier, Tom Sercu, Siddharth Goyal, Zeming Lin, Jason Liu, Demi Guo, Myle Ott, C. Lawrence Zitnick, Jerry Ma, and Rob Fergus. **ESM-1v** was released with the paper [Language models enable zero-shot prediction of the effects of mutations on protein function](https://doi.org/10.1101/2021.07.09.450648) by Joshua Meier, Roshan Rao, Robert Verkuil, Jason Liu, Tom Sercu and Alexander Rives. **ESM-2 and ESMFold** were released with the paper [Language models of protein sequences at the scale of evolution enable accurate structure prediction](https://doi.org/10.1101/2022.07.20.500902) by Zeming Lin, Halil Akin, Roshan Rao, Brian Hie, Zhongkai Zhu, Wenting Lu, Allan dos Santos Costa, Maryam Fazel-Zarandi, Tom Sercu, Sal Candido, Alexander Rives.
1. **[Falcon](https://huggingface.co/docs/transformers/model_doc/falcon)** (from Technology Innovation Institute) by Almazrouei, Ebtesam and Alobeidli, Hamza and Alshamsi, Abdulaziz and Cappelli, Alessandro and Cojocaru, Ruxandra and Debbah, Merouane and Goffinet, Etienne and Heslow, Daniel and Launay, Julien and Malartic, Quentin and Noune, Badreddine and Pannier, Baptiste and Penedo, Guilherme.
1. **[FLAN-T5](https://huggingface.co/docs/transformers/model_doc/flan-t5)** (from Google AI) released in the repository [google-research/t5x](https://github.com/google-research/t5x/blob/main/docs/models.md#flan-t5-checkpoints) by Hyung Won Chung, Le Hou, Shayne Longpre, Barret Zoph, Yi Tay, William Fedus, Eric Li, Xuezhi Wang, Mostafa Dehghani, Siddhartha Brahma, Albert Webson, Shixiang Shane Gu, Zhuyun Dai, Mirac Suzgun, Xinyun Chen, Aakanksha Chowdhery, Sharan Narang, Gaurav Mishra, Adams Yu, Vincent Zhao, Yanping Huang, Andrew Dai, Hongkun Yu, Slav Petrov, Ed H. Chi, Jeff Dean, Jacob Devlin, Adam Roberts, Denny Zhou, Quoc V. Le, and Jason Wei
1. **[FLAN-UL2](https://huggingface.co/docs/transformers/model_doc/flan-ul2)** (from Google AI) released in the repository [google-research/t5x](https://github.com/google-research/t5x/blob/main/docs/models.md#flan-ul2-checkpoints) by Hyung Won Chung, Le Hou, Shayne Longpre, Barret Zoph, Yi Tay, William Fedus, Eric Li, Xuezhi Wang, Mostafa Dehghani, Siddhartha Brahma, Albert Webson, Shixiang Shane Gu, Zhuyun Dai, Mirac Suzgun, Xinyun Chen, Aakanksha Chowdhery, Sharan Narang, Gaurav Mishra, Adams Yu, Vincent Zhao, Yanping Huang, Andrew Dai, Hongkun Yu, Slav Petrov, Ed H. Chi, Jeff Dean, Jacob Devlin, Adam Roberts, Denny Zhou, Quoc V. Le, and Jason Wei
1. **[FlauBERT](https://huggingface.co/docs/transformers/model_doc/flaubert)** (from CNRS) released with the paper [FlauBERT: Unsupervised Language Model Pre-training for French](https://arxiv.org/abs/1912.05372) by Hang Le, Loïc Vial, Jibril Frej, Vincent Segonne, Maximin Coavoux, Benjamin Lecouteux, Alexandre Allauzen, Benoît Crabbé, Laurent Besacier, Didier Schwab.
1. **[FLAVA](https://huggingface.co/docs/transformers/model_doc/flava)** (from Facebook AI) released with the paper [FLAVA: A Foundational Language And Vision Alignment Model](https://arxiv.org/abs/2112.04482) by Amanpreet Singh, Ronghang Hu, Vedanuj Goswami, Guillaume Couairon, Wojciech Galuba, Marcus Rohrbach, and Douwe Kiela.
1. **[FNet](https://huggingface.co/docs/transformers/model_doc/fnet)** (from Google Research) released with the paper [FNet: Mixing Tokens with Fourier Transforms](https://arxiv.org/abs/2105.03824) by James Lee-Thorp, Joshua Ainslie, Ilya Eckstein, Santiago Ontanon.
1. **[FocalNet](https://huggingface.co/docs/transformers/model_doc/focalnet)** (from Microsoft Research) released with the paper [Focal Modulation Networks](https://arxiv.org/abs/2203.11926) by Jianwei Yang, Chunyuan Li, Xiyang Dai, Lu Yuan, Jianfeng Gao.
1. **[Funnel Transformer](https://huggingface.co/docs/transformers/model_doc/funnel)** (from CMU/Google Brain) released with the paper [Funnel-Transformer: Filtering out Sequential Redundancy for Efficient Language Processing](https://arxiv.org/abs/2006.03236) by Zihang Dai, Guokun Lai, Yiming Yang, Quoc V. Le.
1. **[Fuyu](https://huggingface.co/docs/transformers/model_doc/fuyu)** (from ADEPT) Rohan Bavishi, Erich Elsen, Curtis Hawthorne, Maxwell Nye, Augustus Odena, Arushi Somani, Sağnak Taşırlar. Released with the paper [blog post](https://www.adept.ai/blog/fuyu-8b)
1. **[GIT](https://huggingface.co/docs/transformers/model_doc/git)** (from Microsoft Research) released with the paper [GIT: A Generative Image-to-text Transformer for Vision and Language](https://arxiv.org/abs/2205.14100) by Jianfeng Wang, Zhengyuan Yang, Xiaowei Hu, Linjie Li, Kevin Lin, Zhe Gan, Zicheng Liu, Ce Liu, Lijuan Wang.
1. **[GLPN](https://huggingface.co/docs/transformers/model_doc/glpn)** (from KAIST) released with the paper [Global-Local Path Networks for Monocular Depth Estimation with Vertical CutDepth](https://arxiv.org/abs/2201.07436) by Doyeon Kim, Woonghyun Ga, Pyungwhan Ahn, Donggyu Joo, Sehwan Chun, Junmo Kim.
1. **[GPT](https://huggingface.co/docs/transformers/model_doc/openai-gpt)** (from OpenAI) released with the paper [Improving Language Understanding by Generative Pre-Training](https://openai.com/research/language-unsupervised/) by Alec Radford, Karthik Narasimhan, Tim Salimans and Ilya Sutskever.
1. **[GPT Neo](https://huggingface.co/docs/transformers/model_doc/gpt_neo)** (from EleutherAI) released in the repository [EleutherAI/gpt-neo](https://github.com/EleutherAI/gpt-neo) by Sid Black, Stella Biderman, Leo Gao, Phil Wang and Connor Leahy.
1. **[GPT NeoX](https://huggingface.co/docs/transformers/model_doc/gpt_neox)** (from EleutherAI) released with the paper [GPT-NeoX-20B: An Open-Source Autoregressive Language Model](https://arxiv.org/abs/2204.06745) by Sid Black, Stella Biderman, Eric Hallahan, Quentin Anthony, Leo Gao, Laurence Golding, Horace He, Connor Leahy, Kyle McDonell, Jason Phang, Michael Pieler, USVSN Sai Prashanth, Shivanshu Purohit, Laria Reynolds, Jonathan Tow, Ben Wang, Samuel Weinbach
1. **[GPT NeoX Japanese](https://huggingface.co/docs/transformers/model_doc/gpt_neox_japanese)** (from ABEJA) released by Shinya Otani, Takayoshi Makabe, Anuj Arora, and Kyo Hattori.
1. **[GPT-2](https://huggingface.co/docs/transformers/model_doc/gpt2)** (from OpenAI) released with the paper [Language Models are Unsupervised Multitask Learners](https://openai.com/research/better-language-models/) by Alec Radford*, Jeffrey Wu*, Rewon Child, David Luan, Dario Amodei** and Ilya Sutskever**.
1. **[GPT-J](https://huggingface.co/docs/transformers/model_doc/gptj)** (from EleutherAI) released in the repository [kingoflolz/mesh-transformer-jax](https://github.com/kingoflolz/mesh-transformer-jax/) by Ben Wang and Aran Komatsuzaki.
1. **[GPT-Sw3](https://huggingface.co/docs/transformers/model_doc/gpt-sw3)** (from AI-Sweden) released with the paper [Lessons Learned from GPT-SW3: Building the First Large-Scale Generative Language Model for Swedish](http://www.lrec-conf.org/proceedings/lrec2022/pdf/2022.lrec-1.376.pdf) by Ariel Ekgren, Amaru Cuba Gyllensten, Evangelia Gogoulou, Alice Heiman, Severine Verlinden, Joey Öhman, Fredrik Carlsson, Magnus Sahlgren.
1. **[GPTBigCode](https://huggingface.co/docs/transformers/model_doc/gpt_bigcode)** (from BigCode) released with the paper [SantaCoder: don't reach for the stars!](https://arxiv.org/abs/2301.03988) by Loubna Ben Allal, Raymond Li, Denis Kocetkov, Chenghao Mou, Christopher Akiki, Carlos Munoz Ferrandis, Niklas Muennighoff, Mayank Mishra, Alex Gu, Manan Dey, Logesh Kumar Umapathi, Carolyn Jane Anderson, Yangtian Zi, Joel Lamy Poirier, Hailey Schoelkopf, Sergey Troshin, Dmitry Abulkhanov, Manuel Romero, Michael Lappert, Francesco De Toni, Bernardo García del Río, Qian Liu, Shamik Bose, Urvashi Bhattacharyya, Terry Yue Zhuo, Ian Yu, Paulo Villegas, Marco Zocca, Sourab Mangrulkar, David Lansky, Huu Nguyen, Danish Contractor, Luis Villa, Jia Li, Dzmitry Bahdanau, Yacine Jernite, Sean Hughes, Daniel Fried, Arjun Guha, Harm de Vries, Leandro von Werra.
1. **[GPTSAN-japanese](https://huggingface.co/docs/transformers/model_doc/gptsan-japanese)** released in the repository [tanreinama/GPTSAN](https://github.com/tanreinama/GPTSAN/blob/main/report/model.md) by Toshiyuki Sakamoto(tanreinama).
1. **[Graphormer](https://huggingface.co/docs/transformers/model_doc/graphormer)** (from Microsoft) released with the paper [Do Transformers Really Perform Bad for Graph Representation?](https://arxiv.org/abs/2106.05234) by Chengxuan Ying, Tianle Cai, Shengjie Luo, Shuxin Zheng, Guolin Ke, Di He, Yanming Shen, Tie-Yan Liu.
1. **[GroupViT](https://huggingface.co/docs/transformers/model_doc/groupvit)** (from UCSD, NVIDIA) released with the paper [GroupViT: Semantic Segmentation Emerges from Text Supervision](https://arxiv.org/abs/2202.11094) by Jiarui Xu, Shalini De Mello, Sifei Liu, Wonmin Byeon, Thomas Breuel, Jan Kautz, Xiaolong Wang.
1. **[HerBERT](https://huggingface.co/docs/transformers/model_doc/herbert)** (from Allegro.pl, AGH University of Science and Technology) released with the paper [KLEJ: Comprehensive Benchmark for Polish Language Understanding](https://www.aclweb.org/anthology/2020.acl-main.111.pdf) by Piotr Rybak, Robert Mroczkowski, Janusz Tracz, Ireneusz Gawlik.
1. **[Hubert](https://huggingface.co/docs/transformers/model_doc/hubert)** (from Facebook) released with the paper [HuBERT: Self-Supervised Speech Representation Learning by Masked Prediction of Hidden Units](https://arxiv.org/abs/2106.07447) by Wei-Ning Hsu, Benjamin Bolte, Yao-Hung Hubert Tsai, Kushal Lakhotia, Ruslan Salakhutdinov, Abdelrahman Mohamed.
1. **[I-BERT](https://huggingface.co/docs/transformers/model_doc/ibert)** (from Berkeley) released with the paper [I-BERT: Integer-only BERT Quantization](https://arxiv.org/abs/2101.01321) by Sehoon Kim, Amir Gholami, Zhewei Yao, Michael W. Mahoney, Kurt Keutzer.
1. **[IDEFICS](https://huggingface.co/docs/transformers/model_doc/idefics)** (from HuggingFace) released with the paper [OBELICS: An Open Web-Scale Filtered Dataset of Interleaved Image-Text Documents](https://huggingface.co/papers/2306.16527) by Hugo Laurençon, Lucile Saulnier, Léo Tronchon, Stas Bekman, Amanpreet Singh, Anton Lozhkov, Thomas Wang, Siddharth Karamcheti, Alexander M. Rush, Douwe Kiela, Matthieu Cord, Victor Sanh.
1. **[ImageGPT](https://huggingface.co/docs/transformers/model_doc/imagegpt)** (from OpenAI) released with the paper [Generative Pretraining from Pixels](https://openai.com/blog/image-gpt/) by Mark Chen, Alec Radford, Rewon Child, Jeffrey Wu, Heewoo Jun, David Luan, Ilya Sutskever.
1. **[Informer](https://huggingface.co/docs/transformers/model_doc/informer)** (from Beihang University, UC Berkeley, Rutgers University, SEDD Company) released with the paper [Informer: Beyond Efficient Transformer for Long Sequence Time-Series Forecasting](https://arxiv.org/abs/2012.07436) by Haoyi Zhou, Shanghang Zhang, Jieqi Peng, Shuai Zhang, Jianxin Li, Hui Xiong, and Wancai Zhang.
1. **[InstructBLIP](https://huggingface.co/docs/transformers/model_doc/instructblip)** (from Salesforce) released with the paper [InstructBLIP: Towards General-purpose Vision-Language Models with Instruction Tuning](https://arxiv.org/abs/2305.06500) by Wenliang Dai, Junnan Li, Dongxu Li, Anthony Meng Huat Tiong, Junqi Zhao, Weisheng Wang, Boyang Li, Pascale Fung, Steven Hoi.
1. **[Jukebox](https://huggingface.co/docs/transformers/model_doc/jukebox)** (from OpenAI) released with the paper [Jukebox: A Generative Model for Music](https://arxiv.org/pdf/2005.00341.pdf) by Prafulla Dhariwal, Heewoo Jun, Christine Payne, Jong Wook Kim, Alec Radford, Ilya Sutskever.
1. **[LayoutLM](https://huggingface.co/docs/transformers/model_doc/layoutlm)** (from Microsoft Research Asia) released with the paper [LayoutLM: Pre-training of Text and Layout for Document Image Understanding](https://arxiv.org/abs/1912.13318) by Yiheng Xu, Minghao Li, Lei Cui, Shaohan Huang, Furu Wei, Ming Zhou.
1. **[LayoutLMv2](https://huggingface.co/docs/transformers/model_doc/layoutlmv2)** (from Microsoft Research Asia) released with the paper [LayoutLMv2: Multi-modal Pre-training for Visually-Rich Document Understanding](https://arxiv.org/abs/2012.14740) by Yang Xu, Yiheng Xu, Tengchao Lv, Lei Cui, Furu Wei, Guoxin Wang, Yijuan Lu, Dinei Florencio, Cha Zhang, Wanxiang Che, Min Zhang, Lidong Zhou.
1. **[LayoutLMv3](https://huggingface.co/docs/transformers/model_doc/layoutlmv3)** (from Microsoft Research Asia) released with the paper [LayoutLMv3: Pre-training for Document AI with Unified Text and Image Masking](https://arxiv.org/abs/2204.08387) by Yupan Huang, Tengchao Lv, Lei Cui, Yutong Lu, Furu Wei.
1. **[LayoutXLM](https://huggingface.co/docs/transformers/model_doc/layoutxlm)** (from Microsoft Research Asia) released with the paper [LayoutXLM: Multimodal Pre-training for Multilingual Visually-rich Document Understanding](https://arxiv.org/abs/2104.08836) by Yiheng Xu, Tengchao Lv, Lei Cui, Guoxin Wang, Yijuan Lu, Dinei Florencio, Cha Zhang, Furu Wei.
1. **[LED](https://huggingface.co/docs/transformers/model_doc/led)** (from AllenAI) released with the paper [Longformer: The Long-Document Transformer](https://arxiv.org/abs/2004.05150) by Iz Beltagy, Matthew E. Peters, Arman Cohan.
1. **[LeViT](https://huggingface.co/docs/transformers/model_doc/levit)** (from Meta AI) released with the paper [LeViT: A Vision Transformer in ConvNet's Clothing for Faster Inference](https://arxiv.org/abs/2104.01136) by Ben Graham, Alaaeldin El-Nouby, Hugo Touvron, Pierre Stock, Armand Joulin, Hervé Jégou, Matthijs Douze.
1. **[LiLT](https://huggingface.co/docs/transformers/model_doc/lilt)** (from South China University of Technology) released with the paper [LiLT: A Simple yet Effective Language-Independent Layout Transformer for Structured Document Understanding](https://arxiv.org/abs/2202.13669) by Jiapeng Wang, Lianwen Jin, Kai Ding.
1. **[LLaMA](https://huggingface.co/docs/transformers/model_doc/llama)** (from The FAIR team of Meta AI) released with the paper [LLaMA: Open and Efficient Foundation Language Models](https://arxiv.org/abs/2302.13971) by Hugo Touvron, Thibaut Lavril, Gautier Izacard, Xavier Martinet, Marie-Anne Lachaux, Timothée Lacroix, Baptiste Rozière, Naman Goyal, Eric Hambro, Faisal Azhar, Aurelien Rodriguez, Armand Joulin, Edouard Grave, Guillaume Lample.
1. **[Llama2](https://huggingface.co/docs/transformers/model_doc/llama2)** (from The FAIR team of Meta AI) released with the paper [Llama2: Open Foundation and Fine-Tuned Chat Models](https://ai.meta.com/research/publications/llama-2-open-foundation-and-fine-tuned-chat-models/) by Hugo Touvron, Louis Martin, Kevin Stone, Peter Albert, Amjad Almahairi, Yasmine Babaei, Nikolay Bashlykov, Soumya Batra, Prajjwal Bhargava, Shruti Bhosale, Dan Bikel, Lukas Blecher, Cristian Canton Ferrer, Moya Chen, Guillem Cucurull, David Esiobu, Jude Fernandes, Jeremy Fu, Wenyin Fu, Brian Fuller, Cynthia Gao, Vedanuj Goswami, Naman Goyal, Anthony Hartshorn, Saghar Hosseini, Rui Hou, Hakan Inan, Marcin Kardas, Viktor Kerkez Madian Khabsa, Isabel Kloumann, Artem Korenev, Punit Singh Koura, Marie-Anne Lachaux, Thibaut Lavril, Jenya Lee, Diana Liskovich, Yinghai Lu, Yuning Mao, Xavier Martinet, Todor Mihaylov, Pushka rMishra, Igor Molybog, Yixin Nie, Andrew Poulton, Jeremy Reizenstein, Rashi Rungta, Kalyan Saladi, Alan Schelten, Ruan Silva, Eric Michael Smith, Ranjan Subramanian, Xiaoqing EllenTan, Binh Tang, Ross Taylor, Adina Williams, Jian Xiang Kuan, Puxin Xu, Zheng Yan, Iliyan Zarov, Yuchen Zhang, Angela Fan, Melanie Kambadur, Sharan Narang, Aurelien Rodriguez, Robert Stojnic, Sergey Edunov, Thomas Scialom.
1. **[Longformer](https://huggingface.co/docs/transformers/model_doc/longformer)** (from AllenAI) released with the paper [Longformer: The Long-Document Transformer](https://arxiv.org/abs/2004.05150) by Iz Beltagy, Matthew E. Peters, Arman Cohan.
1. **[LongT5](https://huggingface.co/docs/transformers/model_doc/longt5)** (from Google AI) released with the paper [LongT5: Efficient Text-To-Text Transformer for Long Sequences](https://arxiv.org/abs/2112.07916) by Mandy Guo, Joshua Ainslie, David Uthus, Santiago Ontanon, Jianmo Ni, Yun-Hsuan Sung, Yinfei Yang.
1. **[LUKE](https://huggingface.co/docs/transformers/model_doc/luke)** (from Studio Ousia) released with the paper [LUKE: Deep Contextualized Entity Representations with Entity-aware Self-attention](https://arxiv.org/abs/2010.01057) by Ikuya Yamada, Akari Asai, Hiroyuki Shindo, Hideaki Takeda, Yuji Matsumoto.
1. **[LXMERT](https://huggingface.co/docs/transformers/model_doc/lxmert)** (from UNC Chapel Hill) released with the paper [LXMERT: Learning Cross-Modality Encoder Representations from Transformers for Open-Domain Question Answering](https://arxiv.org/abs/1908.07490) by Hao Tan and Mohit Bansal.
1. **[M-CTC-T](https://huggingface.co/docs/transformers/model_doc/mctct)** (from Facebook) released with the paper [Pseudo-Labeling For Massively Multilingual Speech Recognition](https://arxiv.org/abs/2111.00161) by Loren Lugosch, Tatiana Likhomanenko, Gabriel Synnaeve, and Ronan Collobert.
1. **[M2M100](https://huggingface.co/docs/transformers/model_doc/m2m_100)** (from Facebook) released with the paper [Beyond English-Centric Multilingual Machine Translation](https://arxiv.org/abs/2010.11125) by Angela Fan, Shruti Bhosale, Holger Schwenk, Zhiyi Ma, Ahmed El-Kishky, Siddharth Goyal, Mandeep Baines, Onur Celebi, Guillaume Wenzek, Vishrav Chaudhary, Naman Goyal, Tom Birch, Vitaliy Liptchinsky, Sergey Edunov, Edouard Grave, Michael Auli, Armand Joulin.
1. **[MADLAD-400](https://huggingface.co/docs/transformers/model_doc/madlad-400)** (from Google) released with the paper [MADLAD-400: A Multilingual And Document-Level Large Audited Dataset](https://arxiv.org/abs/2309.04662) by Sneha Kudugunta, Isaac Caswell, Biao Zhang, Xavier Garcia, Christopher A. Choquette-Choo, Katherine Lee, Derrick Xin, Aditya Kusupati, Romi Stella, Ankur Bapna, Orhan Firat.
1. **[MarianMT](https://huggingface.co/docs/transformers/model_doc/marian)** Machine translation models trained using [OPUS](http://opus.nlpl.eu/) data by Jörg Tiedemann. The [Marian Framework](https://marian-nmt.github.io/) is being developed by the Microsoft Translator Team.
1. **[MarkupLM](https://huggingface.co/docs/transformers/model_doc/markuplm)** (from Microsoft Research Asia) released with the paper [MarkupLM: Pre-training of Text and Markup Language for Visually-rich Document Understanding](https://arxiv.org/abs/2110.08518) by Junlong Li, Yiheng Xu, Lei Cui, Furu Wei.
1. **[Mask2Former](https://huggingface.co/docs/transformers/model_doc/mask2former)** (from FAIR and UIUC) released with the paper [Masked-attention Mask Transformer for Universal Image Segmentation](https://arxiv.org/abs/2112.01527) by Bowen Cheng, Ishan Misra, Alexander G. Schwing, Alexander Kirillov, Rohit Girdhar.
1. **[MaskFormer](https://huggingface.co/docs/transformers/model_doc/maskformer)** (from Meta and UIUC) released with the paper [Per-Pixel Classification is Not All You Need for Semantic Segmentation](https://arxiv.org/abs/2107.06278) by Bowen Cheng, Alexander G. Schwing, Alexander Kirillov.
1. **[MatCha](https://huggingface.co/docs/transformers/model_doc/matcha)** (from Google AI) released with the paper [MatCha: Enhancing Visual Language Pretraining with Math Reasoning and Chart Derendering](https://arxiv.org/abs/2212.09662) by Fangyu Liu, Francesco Piccinno, Syrine Krichene, Chenxi Pang, Kenton Lee, Mandar Joshi, Yasemin Altun, Nigel Collier, Julian Martin Eisenschlos.
1. **[mBART](https://huggingface.co/docs/transformers/model_doc/mbart)** (from Facebook) released with the paper [Multilingual Denoising Pre-training for Neural Machine Translation](https://arxiv.org/abs/2001.08210) by Yinhan Liu, Jiatao Gu, Naman Goyal, Xian Li, Sergey Edunov, Marjan Ghazvininejad, Mike Lewis, Luke Zettlemoyer.
1. **[mBART-50](https://huggingface.co/docs/transformers/model_doc/mbart)** (from Facebook) released with the paper [Multilingual Translation with Extensible Multilingual Pretraining and Finetuning](https://arxiv.org/abs/2008.00401) by Yuqing Tang, Chau Tran, Xian Li, Peng-Jen Chen, Naman Goyal, Vishrav Chaudhary, Jiatao Gu, Angela Fan.
1. **[MEGA](https://huggingface.co/docs/transformers/model_doc/mega)** (from Meta/USC/CMU/SJTU) released with the paper [Mega: Moving Average Equipped Gated Attention](https://arxiv.org/abs/2209.10655) by Xuezhe Ma, Chunting Zhou, Xiang Kong, Junxian He, Liangke Gui, Graham Neubig, Jonathan May, and Luke Zettlemoyer.
1. **[Megatron-BERT](https://huggingface.co/docs/transformers/model_doc/megatron-bert)** (from NVIDIA) released with the paper [Megatron-LM: Training Multi-Billion Parameter Language Models Using Model Parallelism](https://arxiv.org/abs/1909.08053) by Mohammad Shoeybi, Mostofa Patwary, Raul Puri, Patrick LeGresley, Jared Casper and Bryan Catanzaro.
1. **[Megatron-GPT2](https://huggingface.co/docs/transformers/model_doc/megatron_gpt2)** (from NVIDIA) released with the paper [Megatron-LM: Training Multi-Billion Parameter Language Models Using Model Parallelism](https://arxiv.org/abs/1909.08053) by Mohammad Shoeybi, Mostofa Patwary, Raul Puri, Patrick LeGresley, Jared Casper and Bryan Catanzaro.
1. **[MGP-STR](https://huggingface.co/docs/transformers/model_doc/mgp-str)** (from Alibaba Research) released with the paper [Multi-Granularity Prediction for Scene Text Recognition](https://arxiv.org/abs/2209.03592) by Peng Wang, Cheng Da, and Cong Yao.
1. **[Mistral](https://huggingface.co/docs/transformers/model_doc/mistral)** (from Mistral AI) by The [Mistral AI](https://mistral.ai) team: Albert Jiang, Alexandre Sablayrolles, Arthur Mensch, Chris Bamford, Devendra Singh Chaplot, Diego de las Casas, Florian Bressand, Gianna Lengyel, Guillaume Lample, Lélio Renard Lavaud, Lucile Saulnier, Marie-Anne Lachaux, Pierre Stock, Teven Le Scao, Thibaut Lavril, Thomas Wang, Timothée Lacroix, William El Sayed.
1. **[mLUKE](https://huggingface.co/docs/transformers/model_doc/mluke)** (from Studio Ousia) released with the paper [mLUKE: The Power of Entity Representations in Multilingual Pretrained Language Models](https://arxiv.org/abs/2110.08151) by Ryokan Ri, Ikuya Yamada, and Yoshimasa Tsuruoka.
1. **[MMS](https://huggingface.co/docs/transformers/model_doc/mms)** (from Facebook) released with the paper [Scaling Speech Technology to 1,000+ Languages](https://arxiv.org/abs/2305.13516) by Vineel Pratap, Andros Tjandra, Bowen Shi, Paden Tomasello, Arun Babu, Sayani Kundu, Ali Elkahky, Zhaoheng Ni, Apoorv Vyas, Maryam Fazel-Zarandi, Alexei Baevski, Yossi Adi, Xiaohui Zhang, Wei-Ning Hsu, Alexis Conneau, Michael Auli.
1. **[MobileBERT](https://huggingface.co/docs/transformers/model_doc/mobilebert)** (from CMU/Google Brain) released with the paper [MobileBERT: a Compact Task-Agnostic BERT for Resource-Limited Devices](https://arxiv.org/abs/2004.02984) by Zhiqing Sun, Hongkun Yu, Xiaodan Song, Renjie Liu, Yiming Yang, and Denny Zhou.
1. **[MobileNetV1](https://huggingface.co/docs/transformers/model_doc/mobilenet_v1)** (from Google Inc.) released with the paper [MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications](https://arxiv.org/abs/1704.04861) by Andrew G. Howard, Menglong Zhu, Bo Chen, Dmitry Kalenichenko, Weijun Wang, Tobias Weyand, Marco Andreetto, Hartwig Adam.
1. **[MobileNetV2](https://huggingface.co/docs/transformers/model_doc/mobilenet_v2)** (from Google Inc.) released with the paper [MobileNetV2: Inverted Residuals and Linear Bottlenecks](https://arxiv.org/abs/1801.04381) by Mark Sandler, Andrew Howard, Menglong Zhu, Andrey Zhmoginov, Liang-Chieh Chen.
1. **[MobileViT](https://huggingface.co/docs/transformers/model_doc/mobilevit)** (from Apple) released with the paper [MobileViT: Light-weight, General-purpose, and Mobile-friendly Vision Transformer](https://arxiv.org/abs/2110.02178) by Sachin Mehta and Mohammad Rastegari.
1. **[MobileViTV2](https://huggingface.co/docs/transformers/model_doc/mobilevitv2)** (from Apple) released with the paper [Separable Self-attention for Mobile Vision Transformers](https://arxiv.org/abs/2206.02680) by Sachin Mehta and Mohammad Rastegari.
1. **[MPNet](https://huggingface.co/docs/transformers/model_doc/mpnet)** (from Microsoft Research) released with the paper [MPNet: Masked and Permuted Pre-training for Language Understanding](https://arxiv.org/abs/2004.09297) by Kaitao Song, Xu Tan, Tao Qin, Jianfeng Lu, Tie-Yan Liu.
1. **[MPT](https://huggingface.co/docs/transformers/model_doc/mpt)** (from MosaiML) released with the repository [llm-foundry](https://github.com/mosaicml/llm-foundry/) by the MosaicML NLP Team.
1. **[MRA](https://huggingface.co/docs/transformers/model_doc/mra)** (from the University of Wisconsin - Madison) released with the paper [Multi Resolution Analysis (MRA) for Approximate Self-Attention](https://arxiv.org/abs/2207.10284) by Zhanpeng Zeng, Sourav Pal, Jeffery Kline, Glenn M Fung, Vikas Singh.
1. **[MT5](https://huggingface.co/docs/transformers/model_doc/mt5)** (from Google AI) released with the paper [mT5: A massively multilingual pre-trained text-to-text transformer](https://arxiv.org/abs/2010.11934) by Linting Xue, Noah Constant, Adam Roberts, Mihir Kale, Rami Al-Rfou, Aditya Siddhant, Aditya Barua, Colin Raffel.
1. **[MusicGen](https://huggingface.co/docs/transformers/model_doc/musicgen)** (from Meta) released with the paper [Simple and Controllable Music Generation](https://arxiv.org/abs/2306.05284) by Jade Copet, Felix Kreuk, Itai Gat, Tal Remez, David Kant, Gabriel Synnaeve, Yossi Adi and Alexandre Défossez.
1. **[MVP](https://huggingface.co/docs/transformers/model_doc/mvp)** (from RUC AI Box) released with the paper [MVP: Multi-task Supervised Pre-training for Natural Language Generation](https://arxiv.org/abs/2206.12131) by Tianyi Tang, Junyi Li, Wayne Xin Zhao and Ji-Rong Wen.
1. **[NAT](https://huggingface.co/docs/transformers/model_doc/nat)** (from SHI Labs) released with the paper [Neighborhood Attention Transformer](https://arxiv.org/abs/2204.07143) by Ali Hassani, Steven Walton, Jiachen Li, Shen Li, and Humphrey Shi.
1. **[Nezha](https://huggingface.co/docs/transformers/model_doc/nezha)** (from Huawei Noah’s Ark Lab) released with the paper [NEZHA: Neural Contextualized Representation for Chinese Language Understanding](https://arxiv.org/abs/1909.00204) by Junqiu Wei, Xiaozhe Ren, Xiaoguang Li, Wenyong Huang, Yi Liao, Yasheng Wang, Jiashu Lin, Xin Jiang, Xiao Chen and Qun Liu.
1. **[NLLB](https://huggingface.co/docs/transformers/model_doc/nllb)** (from Meta) released with the paper [No Language Left Behind: Scaling Human-Centered Machine Translation](https://arxiv.org/abs/2207.04672) by the NLLB team.
1. **[NLLB-MOE](https://huggingface.co/docs/transformers/model_doc/nllb-moe)** (from Meta) released with the paper [No Language Left Behind: Scaling Human-Centered Machine Translation](https://arxiv.org/abs/2207.04672) by the NLLB team.
1. **[Nougat](https://huggingface.co/docs/transformers/model_doc/nougat)** (from Meta AI) released with the paper [Nougat: Neural Optical Understanding for Academic Documents](https://arxiv.org/abs/2308.13418) by Lukas Blecher, Guillem Cucurull, Thomas Scialom, Robert Stojnic.
1. **[Nyströmformer](https://huggingface.co/docs/transformers/model_doc/nystromformer)** (from the University of Wisconsin - Madison) released with the paper [Nyströmformer: A Nyström-Based Algorithm for Approximating Self-Attention](https://arxiv.org/abs/2102.03902) by Yunyang Xiong, Zhanpeng Zeng, Rudrasis Chakraborty, Mingxing Tan, Glenn Fung, Yin Li, Vikas Singh.
1. **[OneFormer](https://huggingface.co/docs/transformers/model_doc/oneformer)** (from SHI Labs) released with the paper [OneFormer: One Transformer to Rule Universal Image Segmentation](https://arxiv.org/abs/2211.06220) by Jitesh Jain, Jiachen Li, MangTik Chiu, Ali Hassani, Nikita Orlov, Humphrey Shi.
1. **[OpenLlama](https://huggingface.co/docs/transformers/model_doc/open-llama)** (from [s-JoL](https://huggingface.co/s-JoL)) released on GitHub (now removed).
1. **[OPT](https://huggingface.co/docs/transformers/master/model_doc/opt)** (from Meta AI) released with the paper [OPT: Open Pre-trained Transformer Language Models](https://arxiv.org/abs/2205.01068) by Susan Zhang, Stephen Roller, Naman Goyal, Mikel Artetxe, Moya Chen, Shuohui Chen et al.
1. **[OWL-ViT](https://huggingface.co/docs/transformers/model_doc/owlvit)** (from Google AI) released with the paper [Simple Open-Vocabulary Object Detection with Vision Transformers](https://arxiv.org/abs/2205.06230) by Matthias Minderer, Alexey Gritsenko, Austin Stone, Maxim Neumann, Dirk Weissenborn, Alexey Dosovitskiy, Aravindh Mahendran, Anurag Arnab, Mostafa Dehghani, Zhuoran Shen, Xiao Wang, Xiaohua Zhai, Thomas Kipf, and Neil Houlsby.
1. **[OWLv2](https://huggingface.co/docs/transformers/main/model_doc/owlv2)** (from Google AI) released with the paper [Scaling Open-Vocabulary Object Detection](https://arxiv.org/abs/2306.09683) by Matthias Minderer, Alexey Gritsenko, Neil Houlsby.
1. **[Pegasus](https://huggingface.co/docs/transformers/model_doc/pegasus)** (from Google) released with the paper [PEGASUS: Pre-training with Extracted Gap-sentences for Abstractive Summarization](https://arxiv.org/abs/1912.08777) by Jingqing Zhang, Yao Zhao, Mohammad Saleh and Peter J. Liu.
1. **[PEGASUS-X](https://huggingface.co/docs/transformers/model_doc/pegasus_x)** (from Google) released with the paper [Investigating Efficiently Extending Transformers for Long Input Summarization](https://arxiv.org/abs/2208.04347) by Jason Phang, Yao Zhao, and Peter J. Liu.
1. **[Perceiver IO](https://huggingface.co/docs/transformers/model_doc/perceiver)** (from Deepmind) released with the paper [Perceiver IO: A General Architecture for Structured Inputs & Outputs](https://arxiv.org/abs/2107.14795) by Andrew Jaegle, Sebastian Borgeaud, Jean-Baptiste Alayrac, Carl Doersch, Catalin Ionescu, David Ding, Skanda Koppula, Daniel Zoran, Andrew Brock, Evan Shelhamer, Olivier Hénaff, Matthew M. Botvinick, Andrew Zisserman, Oriol Vinyals, João Carreira.
1. **[Persimmon](https://huggingface.co/docs/transformers/model_doc/persimmon)** (from ADEPT) released in a [blog post](https://www.adept.ai/blog/persimmon-8b) by Erich Elsen, Augustus Odena, Maxwell Nye, Sağnak Taşırlar, Tri Dao, Curtis Hawthorne, Deepak Moparthi, Arushi Somani.
1. **[PhoBERT](https://huggingface.co/docs/transformers/model_doc/phobert)** (from VinAI Research) released with the paper [PhoBERT: Pre-trained language models for Vietnamese](https://www.aclweb.org/anthology/2020.findings-emnlp.92/) by Dat Quoc Nguyen and Anh Tuan Nguyen.
1. **[Pix2Struct](https://huggingface.co/docs/transformers/model_doc/pix2struct)** (from Google) released with the paper [Pix2Struct: Screenshot Parsing as Pretraining for Visual Language Understanding](https://arxiv.org/abs/2210.03347) by Kenton Lee, Mandar Joshi, Iulia Turc, Hexiang Hu, Fangyu Liu, Julian Eisenschlos, Urvashi Khandelwal, Peter Shaw, Ming-Wei Chang, Kristina Toutanova.
1. **[PLBart](https://huggingface.co/docs/transformers/model_doc/plbart)** (from UCLA NLP) released with the paper [Unified Pre-training for Program Understanding and Generation](https://arxiv.org/abs/2103.06333) by Wasi Uddin Ahmad, Saikat Chakraborty, Baishakhi Ray, Kai-Wei Chang.
1. **[PoolFormer](https://huggingface.co/docs/transformers/model_doc/poolformer)** (from Sea AI Labs) released with the paper [MetaFormer is Actually What You Need for Vision](https://arxiv.org/abs/2111.11418) by Yu, Weihao and Luo, Mi and Zhou, Pan and Si, Chenyang and Zhou, Yichen and Wang, Xinchao and Feng, Jiashi and Yan, Shuicheng.
1. **[Pop2Piano](https://huggingface.co/docs/transformers/model_doc/pop2piano)** released with the paper [Pop2Piano : Pop Audio-based Piano Cover Generation](https://arxiv.org/abs/2211.00895) by Jongho Choi and Kyogu Lee.
1. **[ProphetNet](https://huggingface.co/docs/transformers/model_doc/prophetnet)** (from Microsoft Research) released with the paper [ProphetNet: Predicting Future N-gram for Sequence-to-Sequence Pre-training](https://arxiv.org/abs/2001.04063) by Yu Yan, Weizhen Qi, Yeyun Gong, Dayiheng Liu, Nan Duan, Jiusheng Chen, Ruofei Zhang and Ming Zhou.
1. **[PVT](https://huggingface.co/docs/transformers/model_doc/pvt)** (from Nanjing University, The University of Hong Kong etc.) released with the paper [Pyramid Vision Transformer: A Versatile Backbone for Dense Prediction without Convolutions](https://arxiv.org/pdf/2102.12122.pdf) by Wenhai Wang, Enze Xie, Xiang Li, Deng-Ping Fan, Kaitao Song, Ding Liang, Tong Lu, Ping Luo, Ling Shao.
1. **[QDQBert](https://huggingface.co/docs/transformers/model_doc/qdqbert)** (from NVIDIA) released with the paper [Integer Quantization for Deep Learning Inference: Principles and Empirical Evaluation](https://arxiv.org/abs/2004.09602) by Hao Wu, Patrick Judd, Xiaojie Zhang, Mikhail Isaev and Paulius Micikevicius.
1. **[RAG](https://huggingface.co/docs/transformers/model_doc/rag)** (from Facebook) released with the paper [Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks](https://arxiv.org/abs/2005.11401) by Patrick Lewis, Ethan Perez, Aleksandara Piktus, Fabio Petroni, Vladimir Karpukhin, Naman Goyal, Heinrich Küttler, Mike Lewis, Wen-tau Yih, Tim Rocktäschel, Sebastian Riedel, Douwe Kiela.
1. **[REALM](https://huggingface.co/docs/transformers/model_doc/realm.html)** (from Google Research) released with the paper [REALM: Retrieval-Augmented Language Model Pre-Training](https://arxiv.org/abs/2002.08909) by Kelvin Guu, Kenton Lee, Zora Tung, Panupong Pasupat and Ming-Wei Chang.
1. **[Reformer](https://huggingface.co/docs/transformers/model_doc/reformer)** (from Google Research) released with the paper [Reformer: The Efficient Transformer](https://arxiv.org/abs/2001.04451) by Nikita Kitaev, Łukasz Kaiser, Anselm Levskaya.
1. **[RegNet](https://huggingface.co/docs/transformers/model_doc/regnet)** (from META Platforms) released with the paper [Designing Network Design Space](https://arxiv.org/abs/2003.13678) by Ilija Radosavovic, Raj Prateek Kosaraju, Ross Girshick, Kaiming He, Piotr Dollár.
1. **[RemBERT](https://huggingface.co/docs/transformers/model_doc/rembert)** (from Google Research) released with the paper [Rethinking embedding coupling in pre-trained language models](https://arxiv.org/abs/2010.12821) by Hyung Won Chung, Thibault Févry, Henry Tsai, M. Johnson, Sebastian Ruder.
1. **[ResNet](https://huggingface.co/docs/transformers/model_doc/resnet)** (from Microsoft Research) released with the paper [Deep Residual Learning for Image Recognition](https://arxiv.org/abs/1512.03385) by Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun.
1. **[RoBERTa](https://huggingface.co/docs/transformers/model_doc/roberta)** (from Facebook), released together with the paper [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://arxiv.org/abs/1907.11692) by Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Mandar Joshi, Danqi Chen, Omer Levy, Mike Lewis, Luke Zettlemoyer, Veselin Stoyanov.
1. **[RoBERTa-PreLayerNorm](https://huggingface.co/docs/transformers/model_doc/roberta-prelayernorm)** (from Facebook) released with the paper [fairseq: A Fast, Extensible Toolkit for Sequence Modeling](https://arxiv.org/abs/1904.01038) by Myle Ott, Sergey Edunov, Alexei Baevski, Angela Fan, Sam Gross, Nathan Ng, David Grangier, Michael Auli.
1. **[RoCBert](https://huggingface.co/docs/transformers/model_doc/roc_bert)** (from WeChatAI) released with the paper [RoCBert: Robust Chinese Bert with Multimodal Contrastive Pretraining](https://aclanthology.org/2022.acl-long.65.pdf) by HuiSu, WeiweiShi, XiaoyuShen, XiaoZhou, TuoJi, JiaruiFang, JieZhou.
1. **[RoFormer](https://huggingface.co/docs/transformers/model_doc/roformer)** (from ZhuiyiTechnology), released together with the paper [RoFormer: Enhanced Transformer with Rotary Position Embedding](https://arxiv.org/abs/2104.09864) by Jianlin Su and Yu Lu and Shengfeng Pan and Bo Wen and Yunfeng Liu.
1. **[RWKV](https://huggingface.co/docs/transformers/model_doc/rwkv)** (from Bo Peng), released on [this repo](https://github.com/BlinkDL/RWKV-LM) by Bo Peng.
1. **[SeamlessM4T](https://huggingface.co/docs/transformers/main/model_doc/seamless_m4t)** (from Meta AI) released with the paper [SeamlessM4T — Massively Multilingual & Multimodal Machine Translation](https://dl.fbaipublicfiles.com/seamless/seamless_m4t_paper.pdf) by the Seamless Communication team.
1. **[SegFormer](https://huggingface.co/docs/transformers/model_doc/segformer)** (from NVIDIA) released with the paper [SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers](https://arxiv.org/abs/2105.15203) by Enze Xie, Wenhai Wang, Zhiding Yu, Anima Anandkumar, Jose M. Alvarez, Ping Luo.
1. **[Segment Anything](https://huggingface.co/docs/transformers/model_doc/sam)** (from Meta AI) released with the paper [Segment Anything](https://arxiv.org/pdf/2304.02643v1.pdf) by Alexander Kirillov, Eric Mintun, Nikhila Ravi, Hanzi Mao, Chloe Rolland, Laura Gustafson, Tete Xiao, Spencer Whitehead, Alex Berg, Wan-Yen Lo, Piotr Dollar, Ross Girshick.
1. **[SEW](https://huggingface.co/docs/transformers/model_doc/sew)** (from ASAPP) released with the paper [Performance-Efficiency Trade-offs in Unsupervised Pre-training for Speech Recognition](https://arxiv.org/abs/2109.06870) by Felix Wu, Kwangyoun Kim, Jing Pan, Kyu Han, Kilian Q. Weinberger, Yoav Artzi.
1. **[SEW-D](https://huggingface.co/docs/transformers/model_doc/sew_d)** (from ASAPP) released with the paper [Performance-Efficiency Trade-offs in Unsupervised Pre-training for Speech Recognition](https://arxiv.org/abs/2109.06870) by Felix Wu, Kwangyoun Kim, Jing Pan, Kyu Han, Kilian Q. Weinberger, Yoav Artzi.
1. **[SpeechT5](https://huggingface.co/docs/transformers/model_doc/speecht5)** (from Microsoft Research) released with the paper [SpeechT5: Unified-Modal Encoder-Decoder Pre-Training for Spoken Language Processing](https://arxiv.org/abs/2110.07205) by Junyi Ao, Rui Wang, Long Zhou, Chengyi Wang, Shuo Ren, Yu Wu, Shujie Liu, Tom Ko, Qing Li, Yu Zhang, Zhihua Wei, Yao Qian, Jinyu Li, Furu Wei.
1. **[SpeechToTextTransformer](https://huggingface.co/docs/transformers/model_doc/speech_to_text)** (from Facebook), released together with the paper [fairseq S2T: Fast Speech-to-Text Modeling with fairseq](https://arxiv.org/abs/2010.05171) by Changhan Wang, Yun Tang, Xutai Ma, Anne Wu, Dmytro Okhonko, Juan Pino.
1. **[SpeechToTextTransformer2](https://huggingface.co/docs/transformers/model_doc/speech_to_text_2)** (from Facebook), released together with the paper [Large-Scale Self- and Semi-Supervised Learning for Speech Translation](https://arxiv.org/abs/2104.06678) by Changhan Wang, Anne Wu, Juan Pino, Alexei Baevski, Michael Auli, Alexis Conneau.
1. **[Splinter](https://huggingface.co/docs/transformers/model_doc/splinter)** (from Tel Aviv University), released together with the paper [Few-Shot Question Answering by Pretraining Span Selection](https://arxiv.org/abs/2101.00438) by Ori Ram, Yuval Kirstain, Jonathan Berant, Amir Globerson, Omer Levy.
1. **[SqueezeBERT](https://huggingface.co/docs/transformers/model_doc/squeezebert)** (from Berkeley) released with the paper [SqueezeBERT: What can computer vision teach NLP about efficient neural networks?](https://arxiv.org/abs/2006.11316) by Forrest N. Iandola, Albert E. Shaw, Ravi Krishna, and Kurt W. Keutzer.
1. **[SwiftFormer](https://huggingface.co/docs/transformers/model_doc/swiftformer)** (from MBZUAI) released with the paper [SwiftFormer: Efficient Additive Attention for Transformer-based Real-time Mobile Vision Applications](https://arxiv.org/abs/2303.15446) by Abdelrahman Shaker, Muhammad Maaz, Hanoona Rasheed, Salman Khan, Ming-Hsuan Yang, Fahad Shahbaz Khan.
1. **[Swin Transformer](https://huggingface.co/docs/transformers/model_doc/swin)** (from Microsoft) released with the paper [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://arxiv.org/abs/2103.14030) by Ze Liu, Yutong Lin, Yue Cao, Han Hu, Yixuan Wei, Zheng Zhang, Stephen Lin, Baining Guo.
1. **[Swin Transformer V2](https://huggingface.co/docs/transformers/model_doc/swinv2)** (from Microsoft) released with the paper [Swin Transformer V2: Scaling Up Capacity and Resolution](https://arxiv.org/abs/2111.09883) by Ze Liu, Han Hu, Yutong Lin, Zhuliang Yao, Zhenda Xie, Yixuan Wei, Jia Ning, Yue Cao, Zheng Zhang, Li Dong, Furu Wei, Baining Guo.
1. **[Swin2SR](https://huggingface.co/docs/transformers/model_doc/swin2sr)** (from University of Würzburg) released with the paper [Swin2SR: SwinV2 Transformer for Compressed Image Super-Resolution and Restoration](https://arxiv.org/abs/2209.11345) by Marcos V. Conde, Ui-Jin Choi, Maxime Burchi, Radu Timofte.
1. **[SwitchTransformers](https://huggingface.co/docs/transformers/model_doc/switch_transformers)** (from Google) released with the paper [Switch Transformers: Scaling to Trillion Parameter Models with Simple and Efficient Sparsity](https://arxiv.org/abs/2101.03961) by William Fedus, Barret Zoph, Noam Shazeer.
1. **[T5](https://huggingface.co/docs/transformers/model_doc/t5)** (from Google AI) released with the paper [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://arxiv.org/abs/1910.10683) by Colin Raffel and Noam Shazeer and Adam Roberts and Katherine Lee and Sharan Narang and Michael Matena and Yanqi Zhou and Wei Li and Peter J. Liu.
1. **[T5v1.1](https://huggingface.co/docs/transformers/model_doc/t5v1.1)** (from Google AI) released in the repository [google-research/text-to-text-transfer-transformer](https://github.com/google-research/text-to-text-transfer-transformer/blob/main/released_checkpoints.md#t511) by Colin Raffel and Noam Shazeer and Adam Roberts and Katherine Lee and Sharan Narang and Michael Matena and Yanqi Zhou and Wei Li and Peter J. Liu.
1. **[Table Transformer](https://huggingface.co/docs/transformers/model_doc/table-transformer)** (from Microsoft Research) released with the paper [PubTables-1M: Towards Comprehensive Table Extraction From Unstructured Documents](https://arxiv.org/abs/2110.00061) by Brandon Smock, Rohith Pesala, Robin Abraham.
1. **[TAPAS](https://huggingface.co/docs/transformers/model_doc/tapas)** (from Google AI) released with the paper [TAPAS: Weakly Supervised Table Parsing via Pre-training](https://arxiv.org/abs/2004.02349) by Jonathan Herzig, Paweł Krzysztof Nowak, Thomas Müller, Francesco Piccinno and Julian Martin Eisenschlos.
1. **[TAPEX](https://huggingface.co/docs/transformers/model_doc/tapex)** (from Microsoft Research) released with the paper [TAPEX: Table Pre-training via Learning a Neural SQL Executor](https://arxiv.org/abs/2107.07653) by Qian Liu, Bei Chen, Jiaqi Guo, Morteza Ziyadi, Zeqi Lin, Weizhu Chen, Jian-Guang Lou.
1. **[Time Series Transformer](https://huggingface.co/docs/transformers/model_doc/time_series_transformer)** (from HuggingFace).
1. **[TimeSformer](https://huggingface.co/docs/transformers/model_doc/timesformer)** (from Facebook) released with the paper [Is Space-Time Attention All You Need for Video Understanding?](https://arxiv.org/abs/2102.05095) by Gedas Bertasius, Heng Wang, Lorenzo Torresani.
1. **[Trajectory Transformer](https://huggingface.co/docs/transformers/model_doc/trajectory_transformers)** (from the University of California at Berkeley) released with the paper [Offline Reinforcement Learning as One Big Sequence Modeling Problem](https://arxiv.org/abs/2106.02039) by Michael Janner, Qiyang Li, Sergey Levine
1. **[Transformer-XL](https://huggingface.co/docs/transformers/model_doc/transfo-xl)** (from Google/CMU) released with the paper [Transformer-XL: Attentive Language Models Beyond a Fixed-Length Context](https://arxiv.org/abs/1901.02860) by Zihang Dai*, Zhilin Yang*, Yiming Yang, Jaime Carbonell, Quoc V. Le, Ruslan Salakhutdinov.
1. **[TrOCR](https://huggingface.co/docs/transformers/model_doc/trocr)** (from Microsoft), released together with the paper [TrOCR: Transformer-based Optical Character Recognition with Pre-trained Models](https://arxiv.org/abs/2109.10282) by Minghao Li, Tengchao Lv, Lei Cui, Yijuan Lu, Dinei Florencio, Cha Zhang, Zhoujun Li, Furu Wei.
1. **[TVLT](https://huggingface.co/docs/transformers/model_doc/tvlt)** (from UNC Chapel Hill) released with the paper [TVLT: Textless Vision-Language Transformer](https://arxiv.org/abs/2209.14156) by Zineng Tang, Jaemin Cho, Yixin Nie, Mohit Bansal.
1. **[UL2](https://huggingface.co/docs/transformers/model_doc/ul2)** (from Google Research) released with the paper [Unifying Language Learning Paradigms](https://arxiv.org/abs/2205.05131v1) by Yi Tay, Mostafa Dehghani, Vinh Q. Tran, Xavier Garcia, Dara Bahri, Tal Schuster, Huaixiu Steven Zheng, Neil Houlsby, Donald Metzler
1. **[UMT5](https://huggingface.co/docs/transformers/model_doc/umt5)** (from Google Research) released with the paper [UniMax: Fairer and More Effective Language Sampling for Large-Scale Multilingual Pretraining](https://openreview.net/forum?id=kXwdL1cWOAi) by Hyung Won Chung, Xavier Garcia, Adam Roberts, Yi Tay, Orhan Firat, Sharan Narang, Noah Constant.
1. **[UniSpeech](https://huggingface.co/docs/transformers/model_doc/unispeech)** (from Microsoft Research) released with the paper [UniSpeech: Unified Speech Representation Learning with Labeled and Unlabeled Data](https://arxiv.org/abs/2101.07597) by Chengyi Wang, Yu Wu, Yao Qian, Kenichi Kumatani, Shujie Liu, Furu Wei, Michael Zeng, Xuedong Huang.
1. **[UniSpeechSat](https://huggingface.co/docs/transformers/model_doc/unispeech-sat)** (from Microsoft Research) released with the paper [UNISPEECH-SAT: UNIVERSAL SPEECH REPRESENTATION LEARNING WITH SPEAKER AWARE PRE-TRAINING](https://arxiv.org/abs/2110.05752) by Sanyuan Chen, Yu Wu, Chengyi Wang, Zhengyang Chen, Zhuo Chen, Shujie Liu, Jian Wu, Yao Qian, Furu Wei, Jinyu Li, Xiangzhan Yu.
1. **[UPerNet](https://huggingface.co/docs/transformers/model_doc/upernet)** (from Peking University) released with the paper [Unified Perceptual Parsing for Scene Understanding](https://arxiv.org/abs/1807.10221) by Tete Xiao, Yingcheng Liu, Bolei Zhou, Yuning Jiang, Jian Sun.
1. **[VAN](https://huggingface.co/docs/transformers/model_doc/van)** (from Tsinghua University and Nankai University) released with the paper [Visual Attention Network](https://arxiv.org/abs/2202.09741) by Meng-Hao Guo, Cheng-Ze Lu, Zheng-Ning Liu, Ming-Ming Cheng, Shi-Min Hu.
1. **[VideoMAE](https://huggingface.co/docs/transformers/model_doc/videomae)** (from Multimedia Computing Group, Nanjing University) released with the paper [VideoMAE: Masked Autoencoders are Data-Efficient Learners for Self-Supervised Video Pre-Training](https://arxiv.org/abs/2203.12602) by Zhan Tong, Yibing Song, Jue Wang, Limin Wang.
1. **[ViLT](https://huggingface.co/docs/transformers/model_doc/vilt)** (from NAVER AI Lab/Kakao Enterprise/Kakao Brain) released with the paper [ViLT: Vision-and-Language Transformer Without Convolution or Region Supervision](https://arxiv.org/abs/2102.03334) by Wonjae Kim, Bokyung Son, Ildoo Kim.
1. **[Vision Transformer (ViT)](https://huggingface.co/docs/transformers/model_doc/vit)** (from Google AI) released with the paper [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://arxiv.org/abs/2010.11929) by Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, Jakob Uszkoreit, Neil Houlsby.
1. **[VisualBERT](https://huggingface.co/docs/transformers/model_doc/visual_bert)** (from UCLA NLP) released with the paper [VisualBERT: A Simple and Performant Baseline for Vision and Language](https://arxiv.org/pdf/1908.03557) by Liunian Harold Li, Mark Yatskar, Da Yin, Cho-Jui Hsieh, Kai-Wei Chang.
1. **[ViT Hybrid](https://huggingface.co/docs/transformers/model_doc/vit_hybrid)** (from Google AI) released with the paper [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://arxiv.org/abs/2010.11929) by Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, Jakob Uszkoreit, Neil Houlsby.
1. **[VitDet](https://huggingface.co/docs/transformers/model_doc/vitdet)** (from Meta AI) released with the paper [Exploring Plain Vision Transformer Backbones for Object Detection](https://arxiv.org/abs/2203.16527) by Yanghao Li, Hanzi Mao, Ross Girshick, Kaiming He.
1. **[ViTMAE](https://huggingface.co/docs/transformers/model_doc/vit_mae)** (from Meta AI) released with the paper [Masked Autoencoders Are Scalable Vision Learners](https://arxiv.org/abs/2111.06377) by Kaiming He, Xinlei Chen, Saining Xie, Yanghao Li, Piotr Dollár, Ross Girshick.
1. **[ViTMatte](https://huggingface.co/docs/transformers/model_doc/vitmatte)** (from HUST-VL) released with the paper [ViTMatte: Boosting Image Matting with Pretrained Plain Vision Transformers](https://arxiv.org/abs/2305.15272) by Jingfeng Yao, Xinggang Wang, Shusheng Yang, Baoyuan Wang.
1. **[ViTMSN](https://huggingface.co/docs/transformers/model_doc/vit_msn)** (from Meta AI) released with the paper [Masked Siamese Networks for Label-Efficient Learning](https://arxiv.org/abs/2204.07141) by Mahmoud Assran, Mathilde Caron, Ishan Misra, Piotr Bojanowski, Florian Bordes, Pascal Vincent, Armand Joulin, Michael Rabbat, Nicolas Ballas.
1. **[VITS](https://huggingface.co/docs/transformers/model_doc/vits)** (from Kakao Enterprise) released with the paper [Conditional Variational Autoencoder with Adversarial Learning for End-to-End Text-to-Speech](https://arxiv.org/abs/2106.06103) by Jaehyeon Kim, Jungil Kong, Juhee Son.
1. **[ViViT](https://huggingface.co/docs/transformers/model_doc/vivit)** (from Google Research) released with the paper [ViViT: A Video Vision Transformer](https://arxiv.org/abs/2103.15691) by Anurag Arnab, Mostafa Dehghani, Georg Heigold, Chen Sun, Mario Lučić, Cordelia Schmid.
1. **[Wav2Vec2](https://huggingface.co/docs/transformers/model_doc/wav2vec2)** (from Facebook AI) released with the paper [wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations](https://arxiv.org/abs/2006.11477) by Alexei Baevski, Henry Zhou, Abdelrahman Mohamed, Michael Auli.
1. **[Wav2Vec2-Conformer](https://huggingface.co/docs/transformers/model_doc/wav2vec2-conformer)** (from Facebook AI) released with the paper [FAIRSEQ S2T: Fast Speech-to-Text Modeling with FAIRSEQ](https://arxiv.org/abs/2010.05171) by Changhan Wang, Yun Tang, Xutai Ma, Anne Wu, Sravya Popuri, Dmytro Okhonko, Juan Pino.
1. **[Wav2Vec2Phoneme](https://huggingface.co/docs/transformers/model_doc/wav2vec2_phoneme)** (from Facebook AI) released with the paper [Simple and Effective Zero-shot Cross-lingual Phoneme Recognition](https://arxiv.org/abs/2109.11680) by Qiantong Xu, Alexei Baevski, Michael Auli.
1. **[WavLM](https://huggingface.co/docs/transformers/model_doc/wavlm)** (from Microsoft Research) released with the paper [WavLM: Large-Scale Self-Supervised Pre-Training for Full Stack Speech Processing](https://arxiv.org/abs/2110.13900) by Sanyuan Chen, Chengyi Wang, Zhengyang Chen, Yu Wu, Shujie Liu, Zhuo Chen, Jinyu Li, Naoyuki Kanda, Takuya Yoshioka, Xiong Xiao, Jian Wu, Long Zhou, Shuo Ren, Yanmin Qian, Yao Qian, Jian Wu, Michael Zeng, Furu Wei.
1. **[Whisper](https://huggingface.co/docs/transformers/model_doc/whisper)** (from OpenAI) released with the paper [Robust Speech Recognition via Large-Scale Weak Supervision](https://cdn.openai.com/papers/whisper.pdf) by Alec Radford, Jong Wook Kim, Tao Xu, Greg Brockman, Christine McLeavey, Ilya Sutskever.
1. **[X-CLIP](https://huggingface.co/docs/transformers/model_doc/xclip)** (from Microsoft Research) released with the paper [Expanding Language-Image Pretrained Models for General Video Recognition](https://arxiv.org/abs/2208.02816) by Bolin Ni, Houwen Peng, Minghao Chen, Songyang Zhang, Gaofeng Meng, Jianlong Fu, Shiming Xiang, Haibin Ling.
1. **[X-MOD](https://huggingface.co/docs/transformers/model_doc/xmod)** (from Meta AI) released with the paper [Lifting the Curse of Multilinguality by Pre-training Modular Transformers](http://dx.doi.org/10.18653/v1/2022.naacl-main.255) by Jonas Pfeiffer, Naman Goyal, Xi Lin, Xian Li, James Cross, Sebastian Riedel, Mikel Artetxe.
1. **[XGLM](https://huggingface.co/docs/transformers/model_doc/xglm)** (From Facebook AI) released with the paper [Few-shot Learning with Multilingual Language Models](https://arxiv.org/abs/2112.10668) by Xi Victoria Lin, Todor Mihaylov, Mikel Artetxe, Tianlu Wang, Shuohui Chen, Daniel Simig, Myle Ott, Naman Goyal, Shruti Bhosale, Jingfei Du, Ramakanth Pasunuru, Sam Shleifer, Punit Singh Koura, Vishrav Chaudhary, Brian O'Horo, Jeff Wang, Luke Zettlemoyer, Zornitsa Kozareva, Mona Diab, Veselin Stoyanov, Xian Li.
1. **[XLM](https://huggingface.co/docs/transformers/model_doc/xlm)** (from Facebook) released together with the paper [Cross-lingual Language Model Pretraining](https://arxiv.org/abs/1901.07291) by Guillaume Lample and Alexis Conneau.
1. **[XLM-ProphetNet](https://huggingface.co/docs/transformers/model_doc/xlm-prophetnet)** (from Microsoft Research) released with the paper [ProphetNet: Predicting Future N-gram for Sequence-to-Sequence Pre-training](https://arxiv.org/abs/2001.04063) by Yu Yan, Weizhen Qi, Yeyun Gong, Dayiheng Liu, Nan Duan, Jiusheng Chen, Ruofei Zhang and Ming Zhou.
1. **[XLM-RoBERTa](https://huggingface.co/docs/transformers/model_doc/xlm-roberta)** (from Facebook AI), released together with the paper [Unsupervised Cross-lingual Representation Learning at Scale](https://arxiv.org/abs/1911.02116) by Alexis Conneau*, Kartikay Khandelwal*, Naman Goyal, Vishrav Chaudhary, Guillaume Wenzek, Francisco Guzmán, Edouard Grave, Myle Ott, Luke Zettlemoyer and Veselin Stoyanov.
1. **[XLM-RoBERTa-XL](https://huggingface.co/docs/transformers/model_doc/xlm-roberta-xl)** (from Facebook AI), released together with the paper [Larger-Scale Transformers for Multilingual Masked Language Modeling](https://arxiv.org/abs/2105.00572) by Naman Goyal, Jingfei Du, Myle Ott, Giri Anantharaman, Alexis Conneau.
1. **[XLM-V](https://huggingface.co/docs/transformers/model_doc/xlm-v)** (from Meta AI) released with the paper [XLM-V: Overcoming the Vocabulary Bottleneck in Multilingual Masked Language Models](https://arxiv.org/abs/2301.10472) by Davis Liang, Hila Gonen, Yuning Mao, Rui Hou, Naman Goyal, Marjan Ghazvininejad, Luke Zettlemoyer, Madian Khabsa.
1. **[XLNet](https://huggingface.co/docs/transformers/model_doc/xlnet)** (from Google/CMU) released with the paper [XLNet: Generalized Autoregressive Pretraining for Language Understanding](https://arxiv.org/abs/1906.08237) by Zhilin Yang*, Zihang Dai*, Yiming Yang, Jaime Carbonell, Ruslan Salakhutdinov, Quoc V. Le.
1. **[XLS-R](https://huggingface.co/docs/transformers/model_doc/xls_r)** (from Facebook AI) released with the paper [XLS-R: Self-supervised Cross-lingual Speech Representation Learning at Scale](https://arxiv.org/abs/2111.09296) by Arun Babu, Changhan Wang, Andros Tjandra, Kushal Lakhotia, Qiantong Xu, Naman Goyal, Kritika Singh, Patrick von Platen, Yatharth Saraf, Juan Pino, Alexei Baevski, Alexis Conneau, Michael Auli.
1. **[XLSR-Wav2Vec2](https://huggingface.co/docs/transformers/model_doc/xlsr_wav2vec2)** (from Facebook AI) released with the paper [Unsupervised Cross-Lingual Representation Learning For Speech Recognition](https://arxiv.org/abs/2006.13979) by Alexis Conneau, Alexei Baevski, Ronan Collobert, Abdelrahman Mohamed, Michael Auli.
1. **[YOLOS](https://huggingface.co/docs/transformers/model_doc/yolos)** (from Huazhong University of Science & Technology) released with the paper [You Only Look at One Sequence: Rethinking Transformer in Vision through Object Detection](https://arxiv.org/abs/2106.00666) by Yuxin Fang, Bencheng Liao, Xinggang Wang, Jiemin Fang, Jiyang Qi, Rui Wu, Jianwei Niu, Wenyu Liu.
1. **[YOSO](https://huggingface.co/docs/transformers/model_doc/yoso)** (from the University of Wisconsin - Madison) released with the paper [You Only Sample (Almost) Once: Linear Cost Self-Attention Via Bernoulli Sampling](https://arxiv.org/abs/2111.09714) by Zhanpeng Zeng, Yunyang Xiong, Sathya N. Ravi, Shailesh Acharya, Glenn Fung, Vikas Singh.
1. కొత్త మోడల్ను అందించాలనుకుంటున్నారా? కొత్త మోడల్ను జోడించే ప్రక్రియలో మీకు మార్గనిర్దేశం చేసేందుకు మేము **వివరణాత్మక గైడ్ మరియు టెంప్లేట్లను** జోడించాము. మీరు వాటిని రిపోజిటరీ యొక్క [`టెంప్లేట్లు`](./టెంప్లేట్లు) ఫోల్డర్లో కనుగొనవచ్చు. మీ PRని ప్రారంభించడానికి ముందు [సహకార మార్గదర్శకాలు](./CONTRIBUTING.md)ని తనిఖీ చేసి, నిర్వహణదారులను సంప్రదించండి లేదా అభిప్రాయాన్ని సేకరించడానికి సమస్యను తెరవండి.
ప్రతి మోడల్ ఫ్లాక్స్, పైటార్చ్ లేదా టెన్సర్ఫ్లోలో అమలు చేయబడిందా లేదా 🤗 Tokenizers లైబ్రరీ ద్వారా అనుబంధించబడిన టోకెనైజర్ని కలిగి ఉందో లేదో తనిఖీ చేయడానికి, [ఈ పట్టిక](https://huggingface.co/docs/transformers/index#supported-frameworks).
ఈ అమలులు అనేక డేటాసెట్లలో పరీక్షించబడ్డాయి (ఉదాహరణ స్క్రిప్ట్లను చూడండి) మరియు అసలైన అమలుల పనితీరుతో సరిపోలాలి. మీరు [డాక్యుమెంటేషన్](https://github.com/huggingface/transformers/tree/main/examples) యొక్క ఉదాహరణల విభాగంలో పనితీరుపై మరిన్ని వివరాలను కనుగొనవచ్చు.
## ఇంకా నేర్చుకో
| విభాగం | వివరణ |
|-|-|
| [డాక్యుమెంటేషన్](https://huggingface.co/docs/transformers/) | పూర్తి API డాక్యుమెంటేషన్ మరియు ట్యుటోరియల్స్ |
| [టాస్క్ సారాంశం](https://huggingface.co/docs/transformers/task_summary) | 🤗 ట్రాన్స్ఫార్మర్ల ద్వారా సపోర్ట్ చేయబడిన విధులు |
| [ప్రీప్రాసెసింగ్ ట్యుటోరియల్](https://huggingface.co/docs/transformers/preprocessing) | మోడల్ల కోసం డేటాను సిద్ధం చేయడానికి `Tokenizer` క్లాస్ని ఉపయోగించడం |
| [ట్రైనింగ్ మరియు ఫైన్-ట్యూనింగ్](https://huggingface.co/docs/transformers/training) | PyTorch/TensorFlow ట్రైనింగ్ లూప్ మరియు `Trainer` APIలో 🤗 ట్రాన్స్ఫార్మర్లు అందించిన మోడల్లను ఉపయోగించడం |
| [త్వరిత పర్యటన: ఫైన్-ట్యూనింగ్/యూసేజ్ స్క్రిప్ట్లు](https://github.com/huggingface/transformers/tree/main/examples) | విస్తృత శ్రేణి టాస్క్లపై ఫైన్-ట్యూనింగ్ మోడల్స్ కోసం ఉదాహరణ స్క్రిప్ట్లు |
| [మోడల్ భాగస్వామ్యం మరియు అప్లోడ్ చేయడం](https://huggingface.co/docs/transformers/model_sharing) | కమ్యూనిటీతో మీ ఫైన్-ట్యూన్డ్ మోడల్లను అప్లోడ్ చేయండి మరియు భాగస్వామ్యం చేయండి |
## అనులేఖనం
🤗 ట్రాన్స్ఫార్మర్స్ లైబ్రరీ కోసం మీరు ఉదహరించగల [పేపర్](https://www.aclweb.org/anthology/2020.emnlp-demos.6/) ఇప్పుడు మా వద్ద ఉంది:
```bibtex
@inproceedings{wolf-etal-2020-transformers,
title = "Transformers: State-of-the-Art Natural Language Processing",
author = "Thomas Wolf and Lysandre Debut and Victor Sanh and Julien Chaumond and Clement Delangue and Anthony Moi and Pierric Cistac and Tim Rault and Rémi Louf and Morgan Funtowicz and Joe Davison and Sam Shleifer and Patrick von Platen and Clara Ma and Yacine Jernite and Julien Plu and Canwen Xu and Teven Le Scao and Sylvain Gugger and Mariama Drame and Quentin Lhoest and Alexander M. Rush",
booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations",
month = oct,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/2020.emnlp-demos.6",
pages = "38--45"
}
```
| huggingface/transformers/blob/main/README_te.md |
!--Copyright 2021 The HuggingFace Team. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
the License. You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
specific language governing permissions and limitations under the License.
⚠️ Note that this file is in Markdown but contain specific syntax for our doc-builder (similar to MDX) that may not be
rendered properly in your Markdown viewer.
-->
# ByT5
## Overview
The ByT5 model was presented in [ByT5: Towards a token-free future with pre-trained byte-to-byte models](https://arxiv.org/abs/2105.13626) by Linting Xue, Aditya Barua, Noah Constant, Rami Al-Rfou, Sharan Narang, Mihir
Kale, Adam Roberts, Colin Raffel.
The abstract from the paper is the following:
*Most widely-used pre-trained language models operate on sequences of tokens corresponding to word or subword units.
Encoding text as a sequence of tokens requires a tokenizer, which is typically created as an independent artifact from
the model. Token-free models that instead operate directly on raw text (bytes or characters) have many benefits: they
can process text in any language out of the box, they are more robust to noise, and they minimize technical debt by
removing complex and error-prone text preprocessing pipelines. Since byte or character sequences are longer than token
sequences, past work on token-free models has often introduced new model architectures designed to amortize the cost of
operating directly on raw text. In this paper, we show that a standard Transformer architecture can be used with
minimal modifications to process byte sequences. We carefully characterize the trade-offs in terms of parameter count,
training FLOPs, and inference speed, and show that byte-level models are competitive with their token-level
counterparts. We also demonstrate that byte-level models are significantly more robust to noise and perform better on
tasks that are sensitive to spelling and pronunciation. As part of our contribution, we release a new set of
pre-trained byte-level Transformer models based on the T5 architecture, as well as all code and data used in our
experiments.*
This model was contributed by [patrickvonplaten](https://huggingface.co/patrickvonplaten). The original code can be
found [here](https://github.com/google-research/byt5).
<Tip>
ByT5's architecture is based on the T5v1.1 model, refer to [T5v1.1's documentation page](t5v1.1) for the API reference. They
only differ in how inputs should be prepared for the model, see the code examples below.
</Tip>
Since ByT5 was pre-trained unsupervisedly, there's no real advantage to using a task prefix during single-task
fine-tuning. If you are doing multi-task fine-tuning, you should use a prefix.
## Usage example
ByT5 works on raw UTF-8 bytes, so it can be used without a tokenizer:
```python
>>> from transformers import T5ForConditionalGeneration
>>> import torch
>>> model = T5ForConditionalGeneration.from_pretrained("google/byt5-small")
>>> num_special_tokens = 3
>>> # Model has 3 special tokens which take up the input ids 0,1,2 of ByT5.
>>> # => Need to shift utf-8 character encodings by 3 before passing ids to model.
>>> input_ids = torch.tensor([list("Life is like a box of chocolates.".encode("utf-8"))]) + num_special_tokens
>>> labels = torch.tensor([list("La vie est comme une boîte de chocolat.".encode("utf-8"))]) + num_special_tokens
>>> loss = model(input_ids, labels=labels).loss
>>> loss.item()
2.66
```
For batched inference and training it is however recommended to make use of the tokenizer:
```python
>>> from transformers import T5ForConditionalGeneration, AutoTokenizer
>>> model = T5ForConditionalGeneration.from_pretrained("google/byt5-small")
>>> tokenizer = AutoTokenizer.from_pretrained("google/byt5-small")
>>> model_inputs = tokenizer(
... ["Life is like a box of chocolates.", "Today is Monday."], padding="longest", return_tensors="pt"
... )
>>> labels_dict = tokenizer(
... ["La vie est comme une boîte de chocolat.", "Aujourd'hui c'est lundi."], padding="longest", return_tensors="pt"
... )
>>> labels = labels_dict.input_ids
>>> loss = model(**model_inputs, labels=labels).loss
>>> loss.item()
17.9
```
Similar to [T5](t5), ByT5 was trained on the span-mask denoising task. However,
since the model works directly on characters, the pretraining task is a bit
different. Let's corrupt some characters of the
input sentence `"The dog chases a ball in the park."` and ask ByT5 to predict them
for us.
```python
>>> from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
>>> import torch
>>> tokenizer = AutoTokenizer.from_pretrained("google/byt5-base")
>>> model = AutoModelForSeq2SeqLM.from_pretrained("google/byt5-base")
>>> input_ids_prompt = "The dog chases a ball in the park."
>>> input_ids = tokenizer(input_ids_prompt).input_ids
>>> # Note that we cannot add "{extra_id_...}" to the string directly
>>> # as the Byte tokenizer would incorrectly merge the tokens
>>> # For ByT5, we need to work directly on the character level
>>> # Contrary to T5, ByT5 does not use sentinel tokens for masking, but instead
>>> # uses final utf character ids.
>>> # UTF-8 is represented by 8 bits and ByT5 has 3 special tokens.
>>> # => There are 2**8+2 = 259 input ids and mask tokens count down from index 258.
>>> # => mask to "The dog [258]a ball [257]park."
>>> input_ids = torch.tensor([input_ids[:8] + [258] + input_ids[14:21] + [257] + input_ids[28:]])
>>> input_ids
tensor([[ 87, 107, 104, 35, 103, 114, 106, 35, 258, 35, 100, 35, 101, 100, 111, 111, 257, 35, 115, 100, 117, 110, 49, 1]])
>>> # ByT5 produces only one char at a time so we need to produce many more output characters here -> set `max_length=100`.
>>> output_ids = model.generate(input_ids, max_length=100)[0].tolist()
>>> output_ids
[0, 258, 108, 118, 35, 119, 107, 104, 35, 114, 113, 104, 35, 122, 107, 114, 35, 103, 114, 104, 118, 257, 35, 108, 113, 35, 119, 107, 104, 35, 103, 108, 118, 102, 114, 256, 108, 113, 35, 119, 107, 104, 35, 115, 100, 117, 110, 49, 35, 87, 107, 104, 35, 103, 114, 106, 35, 108, 118, 35, 119, 107, 104, 35, 114, 113, 104, 35, 122, 107, 114, 35, 103, 114, 104, 118, 35, 100, 35, 101, 100, 111, 111, 35, 108, 113, 255, 35, 108, 113, 35, 119, 107, 104, 35, 115, 100, 117, 110, 49]
>>> # ^- Note how 258 descends to 257, 256, 255
>>> # Now we need to split on the sentinel tokens, let's write a short loop for this
>>> output_ids_list = []
>>> start_token = 0
>>> sentinel_token = 258
>>> while sentinel_token in output_ids:
... split_idx = output_ids.index(sentinel_token)
... output_ids_list.append(output_ids[start_token:split_idx])
... start_token = split_idx
... sentinel_token -= 1
>>> output_ids_list.append(output_ids[start_token:])
>>> output_string = tokenizer.batch_decode(output_ids_list)
>>> output_string
['<pad>', 'is the one who does', ' in the disco', 'in the park. The dog is the one who does a ball in', ' in the park.']
```
## ByT5Tokenizer
[[autodoc]] ByT5Tokenizer
See [`ByT5Tokenizer`] for all details.
| huggingface/transformers/blob/main/docs/source/en/model_doc/byt5.md |
!--Copyright 2021 The HuggingFace Team. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
the License. You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
specific language governing permissions and limitations under the License.
⚠️ Note that this file is in Markdown but contain specific syntax for our doc-builder (similar to MDX) that may not be
rendered properly in your Markdown viewer.
-->
# BARTpho
## Overview
The BARTpho model was proposed in [BARTpho: Pre-trained Sequence-to-Sequence Models for Vietnamese](https://arxiv.org/abs/2109.09701) by Nguyen Luong Tran, Duong Minh Le and Dat Quoc Nguyen.
The abstract from the paper is the following:
*We present BARTpho with two versions -- BARTpho_word and BARTpho_syllable -- the first public large-scale monolingual
sequence-to-sequence models pre-trained for Vietnamese. Our BARTpho uses the "large" architecture and pre-training
scheme of the sequence-to-sequence denoising model BART, thus especially suitable for generative NLP tasks. Experiments
on a downstream task of Vietnamese text summarization show that in both automatic and human evaluations, our BARTpho
outperforms the strong baseline mBART and improves the state-of-the-art. We release BARTpho to facilitate future
research and applications of generative Vietnamese NLP tasks.*
This model was contributed by [dqnguyen](https://huggingface.co/dqnguyen). The original code can be found [here](https://github.com/VinAIResearch/BARTpho).
## Usage example
```python
>>> import torch
>>> from transformers import AutoModel, AutoTokenizer
>>> bartpho = AutoModel.from_pretrained("vinai/bartpho-syllable")
>>> tokenizer = AutoTokenizer.from_pretrained("vinai/bartpho-syllable")
>>> line = "Chúng tôi là những nghiên cứu viên."
>>> input_ids = tokenizer(line, return_tensors="pt")
>>> with torch.no_grad():
... features = bartpho(**input_ids) # Models outputs are now tuples
>>> # With TensorFlow 2.0+:
>>> from transformers import TFAutoModel
>>> bartpho = TFAutoModel.from_pretrained("vinai/bartpho-syllable")
>>> input_ids = tokenizer(line, return_tensors="tf")
>>> features = bartpho(**input_ids)
```
## Usage tips
- Following mBART, BARTpho uses the "large" architecture of BART with an additional layer-normalization layer on top of
both the encoder and decoder. Thus, usage examples in the [documentation of BART](bart), when adapting to use
with BARTpho, should be adjusted by replacing the BART-specialized classes with the mBART-specialized counterparts.
For example:
```python
>>> from transformers import MBartForConditionalGeneration
>>> bartpho = MBartForConditionalGeneration.from_pretrained("vinai/bartpho-syllable")
>>> TXT = "Chúng tôi là <mask> nghiên cứu viên."
>>> input_ids = tokenizer([TXT], return_tensors="pt")["input_ids"]
>>> logits = bartpho(input_ids).logits
>>> masked_index = (input_ids[0] == tokenizer.mask_token_id).nonzero().item()
>>> probs = logits[0, masked_index].softmax(dim=0)
>>> values, predictions = probs.topk(5)
>>> print(tokenizer.decode(predictions).split())
```
- This implementation is only for tokenization: "monolingual_vocab_file" consists of Vietnamese-specialized types
extracted from the pre-trained SentencePiece model "vocab_file" that is available from the multilingual XLM-RoBERTa.
Other languages, if employing this pre-trained multilingual SentencePiece model "vocab_file" for subword
segmentation, can reuse BartphoTokenizer with their own language-specialized "monolingual_vocab_file".
## BartphoTokenizer
[[autodoc]] BartphoTokenizer
| huggingface/transformers/blob/main/docs/source/en/model_doc/bartpho.md |
!---
Copyright 2020 The HuggingFace Team. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
-->
<!---
A useful guide for English-Traditional Chinese translation of Hugging Face documentation
- Add space around English words and numbers when they appear between Chinese characters. E.g., 共 100 多種語言; 使用 transformers 函式庫。
- Use square quotes, e.g.,「引用」
- Some of terms in the file can be found at National Academy for Educational Research (https://terms.naer.edu.tw/), an official website providing bilingual translations between English and Traditional Chinese.
Dictionary
API: API (不翻譯）
add: 加入
checkpoint: 檢查點
code: 程式碼
community: 社群
confidence: 信賴度
dataset: 資料集
documentation: 文件
example: 基本翻譯為「範例」，或依語意翻為「例子」
finetune: 微調
Hugging Face: Hugging Face（不翻譯）
implementation: 實作
inference: 推論
library: 函式庫
module: 模組
NLP/Natural Language Processing: 以 NLP 出現時不翻譯，以 Natural Language Processing 出現時翻譯為自然語言處理
online demos: 線上Demo
pipeline: pipeline（不翻譯）
pretrained/pretrain: 預訓練
Python data structures (e.g., list, set, dict): 翻譯為串列，集合，字典，並用括號標註原英文
repository: repository（不翻譯）
summary: 概覽
token-: token-（不翻譯）
Trainer: Trainer（不翻譯）
transformer: transformer（不翻譯）
tutorial: 教學
user: 使用者
-->
<p align="center">
<br>
<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers_logo_name.png" width="400"/>
<br>
</p>
<p align="center">
<a href="https://circleci.com/gh/huggingface/transformers">
<img alt="Build" src="https://img.shields.io/circleci/build/github/huggingface/transformers/main">
</a>
<a href="https://github.com/huggingface/transformers/blob/main/LICENSE">
<img alt="GitHub" src="https://img.shields.io/github/license/huggingface/transformers.svg?color=blue">
</a>
<a href="https://huggingface.co/docs/transformers/index">
<img alt="Documentation" src="https://img.shields.io/website/http/huggingface.co/docs/transformers/index.svg?down_color=red&down_message=offline&up_message=online">
</a>
<a href="https://github.com/huggingface/transformers/releases">
<img alt="GitHub release" src="https://img.shields.io/github/release/huggingface/transformers.svg">
</a>
<a href="https://github.com/huggingface/transformers/blob/main/CODE_OF_CONDUCT.md">
<img alt="Contributor Covenant" src="https://img.shields.io/badge/Contributor%20Covenant-v2.0%20adopted-ff69b4.svg">
</a>
<a href="https://zenodo.org/badge/latestdoi/155220641"><img src="https://zenodo.org/badge/155220641.svg" alt="DOI"></a>
</p>
<h4 align="center">
<p>
<a href="https://github.com/huggingface/transformers/">English</a> |
<a href="https://github.com/huggingface/transformers/blob/main/README_zh-hans.md">简体中文</a> |
<b>繁體中文</b> |
<a href="https://github.com/huggingface/transformers/blob/main/README_ko.md">한국어</a> |
<a href="https://github.com/huggingface/transformers/blob/main/README_es.md">Español</a> |
<a href="https://github.com/huggingface/transformers/blob/main/README_ja.md">日本語</a> |
<a href="https://github.com/huggingface/transformers/blob/main/README_hd.md">हिन्दी</a>
<a href="https://github.com/huggingface/transformers//blob/main/README_te.md">తెలుగు</a> |
</p>
</h4>
<h3 align="center">
<p>為 Jax、PyTorch 以及 TensorFlow 打造的先進自然語言處理函式庫</p>
</h3>
<h3 align="center">
<a href="https://hf.co/course"><img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/course_banner.png"></a>
</h3>
🤗 Transformers 提供了數以千計的預訓練模型，支援 100 多種語言的文本分類、資訊擷取、問答、摘要、翻譯、文本生成。它的宗旨是讓最先進的 NLP 技術人人易用。
🤗 Transformers 提供了便於快速下載和使用的API，讓你可以將預訓練模型用在給定文本、在你的資料集上微調然後經由 [model hub](https://huggingface.co/models) 與社群共享。同時，每個定義的 Python 模組架構均完全獨立，方便修改和快速研究實驗。
🤗 Transformers 支援三個最熱門的深度學習函式庫： [Jax](https://jax.readthedocs.io/en/latest/), [PyTorch](https://pytorch.org/) 以及 [TensorFlow](https://www.tensorflow.org/) — 並與之完美整合。你可以直接使用其中一個框架訓練你的模型，然後用另一個載入和推論。
## 線上Demo
你可以直接在 [model hub](https://huggingface.co/models) 上測試大多數的模型。我們也提供了 [私有模型託管、模型版本管理以及推論API](https://huggingface.co/pricing)。
這裡是一些範例：
- [用 BERT 做遮蓋填詞](https://huggingface.co/bert-base-uncased?text=Paris+is+the+%5BMASK%5D+of+France)
- [用 Electra 做專有名詞辨識](https://huggingface.co/dbmdz/electra-large-discriminator-finetuned-conll03-english?text=My+name+is+Sarah+and+I+live+in+London+city)
- [用 GPT-2 做文本生成](https://huggingface.co/gpt2?text=A+long+time+ago%2C+)
- [用 RoBERTa 做自然語言推論](https://huggingface.co/roberta-large-mnli?text=The+dog+was+lost.+Nobody+lost+any+animal)
- [用 BART 做文本摘要](https://huggingface.co/facebook/bart-large-cnn?text=The+tower+is+324+metres+%281%2C063+ft%29+tall%2C+about+the+same+height+as+an+81-storey+building%2C+and+the+tallest+structure+in+Paris.+Its+base+is+square%2C+measuring+125+metres+%28410+ft%29+on+each+side.+During+its+construction%2C+the+Eiffel+Tower+surpassed+the+Washington+Monument+to+become+the+tallest+man-made+structure+in+the+world%2C+a+title+it+held+for+41+years+until+the+Chrysler+Building+in+New+York+City+was+finished+in+1930.+It+was+the+first+structure+to+reach+a+height+of+300+metres.+Due+to+the+addition+of+a+broadcasting+aerial+at+the+top+of+the+tower+in+1957%2C+it+is+now+taller+than+the+Chrysler+Building+by+5.2+metres+%2817+ft%29.+Excluding+transmitters%2C+the+Eiffel+Tower+is+the+second+tallest+free-standing+structure+in+France+after+the+Millau+Viaduct)
- [用 DistilBERT 做問答](https://huggingface.co/distilbert-base-uncased-distilled-squad?text=Which+name+is+also+used+to+describe+the+Amazon+rainforest+in+English%3F&context=The+Amazon+rainforest+%28Portuguese%3A+Floresta+Amaz%C3%B4nica+or+Amaz%C3%B4nia%3B+Spanish%3A+Selva+Amaz%C3%B3nica%2C+Amazon%C3%ADa+or+usually+Amazonia%3B+French%3A+For%C3%AAt+amazonienne%3B+Dutch%3A+Amazoneregenwoud%29%2C+also+known+in+English+as+Amazonia+or+the+Amazon+Jungle%2C+is+a+moist+broadleaf+forest+that+covers+most+of+the+Amazon+basin+of+South+America.+This+basin+encompasses+7%2C000%2C000+square+kilometres+%282%2C700%2C000+sq+mi%29%2C+of+which+5%2C500%2C000+square+kilometres+%282%2C100%2C000+sq+mi%29+are+covered+by+the+rainforest.+This+region+includes+territory+belonging+to+nine+nations.+The+majority+of+the+forest+is+contained+within+Brazil%2C+with+60%25+of+the+rainforest%2C+followed+by+Peru+with+13%25%2C+Colombia+with+10%25%2C+and+with+minor+amounts+in+Venezuela%2C+Ecuador%2C+Bolivia%2C+Guyana%2C+Suriname+and+French+Guiana.+States+or+departments+in+four+nations+contain+%22Amazonas%22+in+their+names.+The+Amazon+represents+over+half+of+the+planet%27s+remaining+rainforests%2C+and+comprises+the+largest+and+most+biodiverse+tract+of+tropical+rainforest+in+the+world%2C+with+an+estimated+390+billion+individual+trees+divided+into+16%2C000+species)
- [用 T5 做翻譯](https://huggingface.co/t5-base?text=My+name+is+Wolfgang+and+I+live+in+Berlin)
**[Write With Transformer](https://transformer.huggingface.co)**，由 Hugging Face 團隊所打造，是一個文本生成的官方 demo。
## 如果你在尋找由 Hugging Face 團隊所提供的客製化支援服務
<a target="_blank" href="https://huggingface.co/support">
<img alt="HuggingFace Expert Acceleration Program" src="https://huggingface.co/front/thumbnails/support.png" style="max-width: 600px; border: 1px solid #eee; border-radius: 4px; box-shadow: 0 1px 2px 0 rgba(0, 0, 0, 0.05);">
</a><br>
## 快速上手
我們為快速使用模型提供了 `pipeline` API。 Pipeline 包含了預訓練模型和對應的文本預處理。下面是一個快速使用 pipeline 去判斷正負面情緒的例子：
```python
>>> from transformers import pipeline
# 使用情緒分析 pipeline
>>> classifier = pipeline('sentiment-analysis')
>>> classifier('We are very happy to introduce pipeline to the transformers repository.')
[{'label': 'POSITIVE', 'score': 0.9996980428695679}]
```
第二行程式碼下載並快取 pipeline 使用的預訓練模型，而第三行程式碼則在給定的文本上進行了評估。這裡的答案“正面” (positive) 具有 99.97% 的信賴度。
許多的 NLP 任務都有隨選即用的預訓練 `pipeline`。例如，我們可以輕鬆地從給定文本中擷取問題答案：
``` python
>>> from transformers import pipeline
# 使用問答 pipeline
>>> question_answerer = pipeline('question-answering')
>>> question_answerer({
... 'question': 'What is the name of the repository ?',
... 'context': 'Pipeline has been included in the huggingface/transformers repository'
... })
{'score': 0.30970096588134766, 'start': 34, 'end': 58, 'answer': 'huggingface/transformers'}
```
除了提供問題解答，預訓練模型還提供了對應的信賴度分數以及解答在 tokenized 後的文本中開始和結束的位置。你可以從[這個教學](https://huggingface.co/docs/transformers/task_summary)了解更多 `pipeline` API支援的任務。
要在你的任務中下載和使用任何預訓練模型很簡單，只需三行程式碼。這裡是 PyTorch 版的範例：
```python
>>> from transformers import AutoTokenizer, AutoModel
>>> tokenizer = AutoTokenizer.from_pretrained("bert-base-uncased")
>>> model = AutoModel.from_pretrained("bert-base-uncased")
>>> inputs = tokenizer("Hello world!", return_tensors="pt")
>>> outputs = model(**inputs)
```
這裡是對應的 TensorFlow 程式碼：
```python
>>> from transformers import AutoTokenizer, TFAutoModel
>>> tokenizer = AutoTokenizer.from_pretrained("bert-base-uncased")
>>> model = TFAutoModel.from_pretrained("bert-base-uncased")
>>> inputs = tokenizer("Hello world!", return_tensors="tf")
>>> outputs = model(**inputs)
```
Tokenizer 為所有的預訓練模型提供了預處理，並可以直接轉換單一字串（比如上面的例子）或串列 (list)。它會輸出一個的字典 (dict) 讓你可以在下游程式碼裡使用或直接藉由 `**` 運算式傳給模型。
模型本身是一個常規的 [Pytorch `nn.Module`](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) 或 [TensorFlow `tf.keras.Model`](https://www.tensorflow.org/api_docs/python/tf/keras/Model)（取決於你的後端），可依常規方式使用。 [這個教學](https://huggingface.co/transformers/training.html)解釋了如何將這樣的模型整合到一般的 PyTorch 或 TensorFlow 訓練迴圈中，或是如何使用我們的 `Trainer` API 在一個新的資料集上快速進行微調。
## 為什麼要用 transformers？
1. 便於使用的先進模型：
- NLU 和 NLG 上性能卓越
- 對教學和實作友好且低門檻
- 高度抽象，使用者只須學習 3 個類別
- 對所有模型使用的制式化API
1. 更低的運算成本，更少的碳排放：
- 研究人員可以分享已訓練的模型而非每次從頭開始訓練
- 工程師可以減少計算時間以及生產成本
- 數十種模型架構、兩千多個預訓練模型、100多種語言支援
1. 對於模型生命週期的每一個部分都面面俱到：
- 訓練先進的模型，只需 3 行程式碼
- 模型可以在不同深度學習框架之間任意轉換
- 為訓練、評估和生產選擇最適合的框架，並完美銜接
1. 為你的需求輕鬆客製化專屬模型和範例：
- 我們為每種模型架構提供了多個範例來重現原論文結果
- 一致的模型內部架構
- 模型檔案可單獨使用，便於修改和快速實驗
## 什麼情況下我不該用 transformers？
- 本函式庫並不是模組化的神經網絡工具箱。模型文件中的程式碼並未做額外的抽象封裝，以便研究人員快速地翻閱及修改程式碼，而不會深陷複雜的類別包裝之中。
- `Trainer` API 並非相容任何模型，它只為本函式庫中的模型最佳化。對於一般的機器學習用途，請使用其他函式庫。
- 儘管我們已盡力而為，[examples 目錄](https://github.com/huggingface/transformers/tree/main/examples)中的腳本也僅為範例而已。對於特定問題，它們並不一定隨選即用，可能需要修改幾行程式碼以符合需求。
## 安裝
### 使用 pip
這個 Repository 已在 Python 3.8+、Flax 0.4.1+、PyTorch 1.10+ 和 TensorFlow 2.6+ 下經過測試。
你可以在[虛擬環境](https://docs.python.org/3/library/venv.html)中安裝 🤗 Transformers。如果你還不熟悉 Python 的虛擬環境，請閱此[使用者指引](https://packaging.python.org/guides/installing-using-pip-and-virtual-environments/)。
首先，用你打算使用的版本的 Python 創建一個虛擬環境並進入。
然後，你需要安裝 Flax、PyTorch 或 TensorFlow 其中之一。對於該如何在你使用的平台上安裝這些框架，請參閱 [TensorFlow 安裝頁面](https://www.tensorflow.org/install/), [PyTorch 安裝頁面](https://pytorch.org/get-started/locally/#start-locally) 或 [Flax 安裝頁面](https://github.com/google/flax#quick-install)。
當其中一個後端安裝成功後，🤗 Transformers 可依此安裝：
```bash
pip install transformers
```
如果你想要試試範例或者想在正式發布前使用最新開發中的程式碼，你必須[從原始碼安裝](https://huggingface.co/docs/transformers/installation#installing-from-source)。
### 使用 conda
自 Transformers 4.0.0 版始，我們有了一個 conda channel： `huggingface`。
🤗 Transformers 可以藉由 conda 依此安裝：
```shell script
conda install -c huggingface transformers
```
要藉由 conda 安裝 Flax、PyTorch 或 TensorFlow 其中之一，請參閱它們各自安裝頁面的說明。
## 模型架構
**🤗 Transformers 支援的[所有的模型檢查點](https://huggingface.co/models)**，由[使用者](https://huggingface.co/users)和[組織](https://huggingface.co/organizations)上傳，均與 huggingface.co [model hub](https://huggingface.co) 完美結合。
目前的檢查點數量： 
🤗 Transformers 目前支援以下的架構（模型概覽請參閱[這裡](https://huggingface.co/docs/transformers/model_summary)）：
1. **[ALBERT](https://huggingface.co/docs/transformers/model_doc/albert)** (from Google Research and the Toyota Technological Institute at Chicago) released with the paper [ALBERT: A Lite BERT for Self-supervised Learning of Language Representations](https://arxiv.org/abs/1909.11942), by Zhenzhong Lan, Mingda Chen, Sebastian Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut.
1. **[ALIGN](https://huggingface.co/docs/transformers/model_doc/align)** (from Google Research) released with the paper [Scaling Up Visual and Vision-Language Representation Learning With Noisy Text Supervision](https://arxiv.org/abs/2102.05918) by Chao Jia, Yinfei Yang, Ye Xia, Yi-Ting Chen, Zarana Parekh, Hieu Pham, Quoc V. Le, Yunhsuan Sung, Zhen Li, Tom Duerig.
1. **[AltCLIP](https://huggingface.co/docs/transformers/model_doc/altclip)** (from BAAI) released with the paper [AltCLIP: Altering the Language Encoder in CLIP for Extended Language Capabilities](https://arxiv.org/abs/2211.06679) by Chen, Zhongzhi and Liu, Guang and Zhang, Bo-Wen and Ye, Fulong and Yang, Qinghong and Wu, Ledell.
1. **[Audio Spectrogram Transformer](https://huggingface.co/docs/transformers/model_doc/audio-spectrogram-transformer)** (from MIT) released with the paper [AST: Audio Spectrogram Transformer](https://arxiv.org/abs/2104.01778) by Yuan Gong, Yu-An Chung, James Glass.
1. **[Autoformer](https://huggingface.co/docs/transformers/model_doc/autoformer)** (from Tsinghua University) released with the paper [Autoformer: Decomposition Transformers with Auto-Correlation for Long-Term Series Forecasting](https://arxiv.org/abs/2106.13008) by Haixu Wu, Jiehui Xu, Jianmin Wang, Mingsheng Long.
1. **[Bark](https://huggingface.co/docs/transformers/model_doc/bark)** (from Suno) released in the repository [suno-ai/bark](https://github.com/suno-ai/bark) by Suno AI team.
1. **[BART](https://huggingface.co/docs/transformers/model_doc/bart)** (from Facebook) released with the paper [BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension](https://arxiv.org/pdf/1910.13461.pdf) by Mike Lewis, Yinhan Liu, Naman Goyal, Marjan Ghazvininejad, Abdelrahman Mohamed, Omer Levy, Ves Stoyanov and Luke Zettlemoyer.
1. **[BARThez](https://huggingface.co/docs/transformers/model_doc/barthez)** (from École polytechnique) released with the paper [BARThez: a Skilled Pretrained French Sequence-to-Sequence Model](https://arxiv.org/abs/2010.12321) by Moussa Kamal Eddine, Antoine J.-P. Tixier, Michalis Vazirgiannis.
1. **[BARTpho](https://huggingface.co/docs/transformers/model_doc/bartpho)** (from VinAI Research) released with the paper [BARTpho: Pre-trained Sequence-to-Sequence Models for Vietnamese](https://arxiv.org/abs/2109.09701) by Nguyen Luong Tran, Duong Minh Le and Dat Quoc Nguyen.
1. **[BEiT](https://huggingface.co/docs/transformers/model_doc/beit)** (from Microsoft) released with the paper [BEiT: BERT Pre-Training of Image Transformers](https://arxiv.org/abs/2106.08254) by Hangbo Bao, Li Dong, Furu Wei.
1. **[BERT](https://huggingface.co/docs/transformers/model_doc/bert)** (from Google) released with the paper [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://arxiv.org/abs/1810.04805) by Jacob Devlin, Ming-Wei Chang, Kenton Lee and Kristina Toutanova.
1. **[BERT For Sequence Generation](https://huggingface.co/docs/transformers/model_doc/bert-generation)** (from Google) released with the paper [Leveraging Pre-trained Checkpoints for Sequence Generation Tasks](https://arxiv.org/abs/1907.12461) by Sascha Rothe, Shashi Narayan, Aliaksei Severyn.
1. **[BERTweet](https://huggingface.co/docs/transformers/model_doc/bertweet)** (from VinAI Research) released with the paper [BERTweet: A pre-trained language model for English Tweets](https://aclanthology.org/2020.emnlp-demos.2/) by Dat Quoc Nguyen, Thanh Vu and Anh Tuan Nguyen.
1. **[BigBird-Pegasus](https://huggingface.co/docs/transformers/model_doc/bigbird_pegasus)** (from Google Research) released with the paper [Big Bird: Transformers for Longer Sequences](https://arxiv.org/abs/2007.14062) by Manzil Zaheer, Guru Guruganesh, Avinava Dubey, Joshua Ainslie, Chris Alberti, Santiago Ontanon, Philip Pham, Anirudh Ravula, Qifan Wang, Li Yang, Amr Ahmed.
1. **[BigBird-RoBERTa](https://huggingface.co/docs/transformers/model_doc/big_bird)** (from Google Research) released with the paper [Big Bird: Transformers for Longer Sequences](https://arxiv.org/abs/2007.14062) by Manzil Zaheer, Guru Guruganesh, Avinava Dubey, Joshua Ainslie, Chris Alberti, Santiago Ontanon, Philip Pham, Anirudh Ravula, Qifan Wang, Li Yang, Amr Ahmed.
1. **[BioGpt](https://huggingface.co/docs/transformers/model_doc/biogpt)** (from Microsoft Research AI4Science) released with the paper [BioGPT: generative pre-trained transformer for biomedical text generation and mining](https://academic.oup.com/bib/advance-article/doi/10.1093/bib/bbac409/6713511?guestAccessKey=a66d9b5d-4f83-4017-bb52-405815c907b9) by Renqian Luo, Liai Sun, Yingce Xia, Tao Qin, Sheng Zhang, Hoifung Poon and Tie-Yan Liu.
1. **[BiT](https://huggingface.co/docs/transformers/model_doc/bit)** (from Google AI) released with the paper [Big Transfer (BiT) by Alexander Kolesnikov, Lucas Beyer, Xiaohua Zhai, Joan Puigcerver, Jessica Yung, Sylvain Gelly, Neil Houlsby.
1. **[Blenderbot](https://huggingface.co/docs/transformers/model_doc/blenderbot)** (from Facebook) released with the paper [Recipes for building an open-domain chatbot](https://arxiv.org/abs/2004.13637) by Stephen Roller, Emily Dinan, Naman Goyal, Da Ju, Mary Williamson, Yinhan Liu, Jing Xu, Myle Ott, Kurt Shuster, Eric M. Smith, Y-Lan Boureau, Jason Weston.
1. **[BlenderbotSmall](https://huggingface.co/docs/transformers/model_doc/blenderbot-small)** (from Facebook) released with the paper [Recipes for building an open-domain chatbot](https://arxiv.org/abs/2004.13637) by Stephen Roller, Emily Dinan, Naman Goyal, Da Ju, Mary Williamson, Yinhan Liu, Jing Xu, Myle Ott, Kurt Shuster, Eric M. Smith, Y-Lan Boureau, Jason Weston.
1. **[BLIP](https://huggingface.co/docs/transformers/model_doc/blip)** (from Salesforce) released with the paper [BLIP: Bootstrapping Language-Image Pre-training for Unified Vision-Language Understanding and Generation](https://arxiv.org/abs/2201.12086) by Junnan Li, Dongxu Li, Caiming Xiong, Steven Hoi.
1. **[BLIP-2](https://huggingface.co/docs/transformers/model_doc/blip-2)** (from Salesforce) released with the paper [BLIP-2: Bootstrapping Language-Image Pre-training with Frozen Image Encoders and Large Language Models](https://arxiv.org/abs/2301.12597) by Junnan Li, Dongxu Li, Silvio Savarese, Steven Hoi.
1. **[BLOOM](https://huggingface.co/docs/transformers/model_doc/bloom)** (from BigScience workshop) released by the [BigScience Workshop](https://bigscience.huggingface.co/).
1. **[BORT](https://huggingface.co/docs/transformers/model_doc/bort)** (from Alexa) released with the paper [Optimal Subarchitecture Extraction For BERT](https://arxiv.org/abs/2010.10499) by Adrian de Wynter and Daniel J. Perry.
1. **[BridgeTower](https://huggingface.co/docs/transformers/model_doc/bridgetower)** (from Harbin Institute of Technology/Microsoft Research Asia/Intel Labs) released with the paper [BridgeTower: Building Bridges Between Encoders in Vision-Language Representation Learning](https://arxiv.org/abs/2206.08657) by Xiao Xu, Chenfei Wu, Shachar Rosenman, Vasudev Lal, Wanxiang Che, Nan Duan.
1. **[BROS](https://huggingface.co/docs/transformers/model_doc/bros)** (from NAVER CLOVA) released with the paper [BROS: A Pre-trained Language Model Focusing on Text and Layout for Better Key Information Extraction from Documents](https://arxiv.org/abs/2108.04539) by Teakgyu Hong, Donghyun Kim, Mingi Ji, Wonseok Hwang, Daehyun Nam, Sungrae Park.
1. **[ByT5](https://huggingface.co/docs/transformers/model_doc/byt5)** (from Google Research) released with the paper [ByT5: Towards a token-free future with pre-trained byte-to-byte models](https://arxiv.org/abs/2105.13626) by Linting Xue, Aditya Barua, Noah Constant, Rami Al-Rfou, Sharan Narang, Mihir Kale, Adam Roberts, Colin Raffel.
1. **[CamemBERT](https://huggingface.co/docs/transformers/model_doc/camembert)** (from Inria/Facebook/Sorbonne) released with the paper [CamemBERT: a Tasty French Language Model](https://arxiv.org/abs/1911.03894) by Louis Martin*, Benjamin Muller*, Pedro Javier Ortiz Suárez*, Yoann Dupont, Laurent Romary, Éric Villemonte de la Clergerie, Djamé Seddah and Benoît Sagot.
1. **[CANINE](https://huggingface.co/docs/transformers/model_doc/canine)** (from Google Research) released with the paper [CANINE: Pre-training an Efficient Tokenization-Free Encoder for Language Representation](https://arxiv.org/abs/2103.06874) by Jonathan H. Clark, Dan Garrette, Iulia Turc, John Wieting.
1. **[Chinese-CLIP](https://huggingface.co/docs/transformers/model_doc/chinese_clip)** (from OFA-Sys) released with the paper [Chinese CLIP: Contrastive Vision-Language Pretraining in Chinese](https://arxiv.org/abs/2211.01335) by An Yang, Junshu Pan, Junyang Lin, Rui Men, Yichang Zhang, Jingren Zhou, Chang Zhou.
1. **[CLAP](https://huggingface.co/docs/transformers/model_doc/clap)** (from LAION-AI) released with the paper [Large-scale Contrastive Language-Audio Pretraining with Feature Fusion and Keyword-to-Caption Augmentation](https://arxiv.org/abs/2211.06687) by Yusong Wu, Ke Chen, Tianyu Zhang, Yuchen Hui, Taylor Berg-Kirkpatrick, Shlomo Dubnov.
1. **[CLIP](https://huggingface.co/docs/transformers/model_doc/clip)** (from OpenAI) released with the paper [Learning Transferable Visual Models From Natural Language Supervision](https://arxiv.org/abs/2103.00020) by Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, Gretchen Krueger, Ilya Sutskever.
1. **[CLIPSeg](https://huggingface.co/docs/transformers/model_doc/clipseg)** (from University of Göttingen) released with the paper [Image Segmentation Using Text and Image Prompts](https://arxiv.org/abs/2112.10003) by Timo Lüddecke and Alexander Ecker.
1. **[CLVP](https://huggingface.co/docs/transformers/model_doc/clvp)** released with the paper [Better speech synthesis through scaling](https://arxiv.org/abs/2305.07243) by James Betker.
1. **[CodeGen](https://huggingface.co/docs/transformers/model_doc/codegen)** (from Salesforce) released with the paper [A Conversational Paradigm for Program Synthesis](https://arxiv.org/abs/2203.13474) by Erik Nijkamp, Bo Pang, Hiroaki Hayashi, Lifu Tu, Huan Wang, Yingbo Zhou, Silvio Savarese, Caiming Xiong.
1. **[CodeLlama](https://huggingface.co/docs/transformers/model_doc/llama_code)** (from MetaAI) released with the paper [Code Llama: Open Foundation Models for Code](https://ai.meta.com/research/publications/code-llama-open-foundation-models-for-code/) by Baptiste Rozière, Jonas Gehring, Fabian Gloeckle, Sten Sootla, Itai Gat, Xiaoqing Ellen Tan, Yossi Adi, Jingyu Liu, Tal Remez, Jérémy Rapin, Artyom Kozhevnikov, Ivan Evtimov, Joanna Bitton, Manish Bhatt, Cristian Canton Ferrer, Aaron Grattafiori, Wenhan Xiong, Alexandre Défossez, Jade Copet, Faisal Azhar, Hugo Touvron, Louis Martin, Nicolas Usunier, Thomas Scialom, Gabriel Synnaeve.
1. **[Conditional DETR](https://huggingface.co/docs/transformers/model_doc/conditional_detr)** (from Microsoft Research Asia) released with the paper [Conditional DETR for Fast Training Convergence](https://arxiv.org/abs/2108.06152) by Depu Meng, Xiaokang Chen, Zejia Fan, Gang Zeng, Houqiang Li, Yuhui Yuan, Lei Sun, Jingdong Wang.
1. **[ConvBERT](https://huggingface.co/docs/transformers/model_doc/convbert)** (from YituTech) released with the paper [ConvBERT: Improving BERT with Span-based Dynamic Convolution](https://arxiv.org/abs/2008.02496) by Zihang Jiang, Weihao Yu, Daquan Zhou, Yunpeng Chen, Jiashi Feng, Shuicheng Yan.
1. **[ConvNeXT](https://huggingface.co/docs/transformers/model_doc/convnext)** (from Facebook AI) released with the paper [A ConvNet for the 2020s](https://arxiv.org/abs/2201.03545) by Zhuang Liu, Hanzi Mao, Chao-Yuan Wu, Christoph Feichtenhofer, Trevor Darrell, Saining Xie.
1. **[ConvNeXTV2](https://huggingface.co/docs/transformers/model_doc/convnextv2)** (from Facebook AI) released with the paper [ConvNeXt V2: Co-designing and Scaling ConvNets with Masked Autoencoders](https://arxiv.org/abs/2301.00808) by Sanghyun Woo, Shoubhik Debnath, Ronghang Hu, Xinlei Chen, Zhuang Liu, In So Kweon, Saining Xie.
1. **[CPM](https://huggingface.co/docs/transformers/model_doc/cpm)** (from Tsinghua University) released with the paper [CPM: A Large-scale Generative Chinese Pre-trained Language Model](https://arxiv.org/abs/2012.00413) by Zhengyan Zhang, Xu Han, Hao Zhou, Pei Ke, Yuxian Gu, Deming Ye, Yujia Qin, Yusheng Su, Haozhe Ji, Jian Guan, Fanchao Qi, Xiaozhi Wang, Yanan Zheng, Guoyang Zeng, Huanqi Cao, Shengqi Chen, Daixuan Li, Zhenbo Sun, Zhiyuan Liu, Minlie Huang, Wentao Han, Jie Tang, Juanzi Li, Xiaoyan Zhu, Maosong Sun.
1. **[CPM-Ant](https://huggingface.co/docs/transformers/model_doc/cpmant)** (from OpenBMB) released by the [OpenBMB](https://www.openbmb.org/).
1. **[CTRL](https://huggingface.co/docs/transformers/model_doc/ctrl)** (from Salesforce) released with the paper [CTRL: A Conditional Transformer Language Model for Controllable Generation](https://arxiv.org/abs/1909.05858) by Nitish Shirish Keskar*, Bryan McCann*, Lav R. Varshney, Caiming Xiong and Richard Socher.
1. **[CvT](https://huggingface.co/docs/transformers/model_doc/cvt)** (from Microsoft) released with the paper [CvT: Introducing Convolutions to Vision Transformers](https://arxiv.org/abs/2103.15808) by Haiping Wu, Bin Xiao, Noel Codella, Mengchen Liu, Xiyang Dai, Lu Yuan, Lei Zhang.
1. **[Data2Vec](https://huggingface.co/docs/transformers/model_doc/data2vec)** (from Facebook) released with the paper [Data2Vec: A General Framework for Self-supervised Learning in Speech, Vision and Language](https://arxiv.org/abs/2202.03555) by Alexei Baevski, Wei-Ning Hsu, Qiantong Xu, Arun Babu, Jiatao Gu, Michael Auli.
1. **[DeBERTa](https://huggingface.co/docs/transformers/model_doc/deberta)** (from Microsoft) released with the paper [DeBERTa: Decoding-enhanced BERT with Disentangled Attention](https://arxiv.org/abs/2006.03654) by Pengcheng He, Xiaodong Liu, Jianfeng Gao, Weizhu Chen.
1. **[DeBERTa-v2](https://huggingface.co/docs/transformers/model_doc/deberta-v2)** (from Microsoft) released with the paper [DeBERTa: Decoding-enhanced BERT with Disentangled Attention](https://arxiv.org/abs/2006.03654) by Pengcheng He, Xiaodong Liu, Jianfeng Gao, Weizhu Chen.
1. **[Decision Transformer](https://huggingface.co/docs/transformers/model_doc/decision_transformer)** (from Berkeley/Facebook/Google) released with the paper [Decision Transformer: Reinforcement Learning via Sequence Modeling](https://arxiv.org/abs/2106.01345) by Lili Chen, Kevin Lu, Aravind Rajeswaran, Kimin Lee, Aditya Grover, Michael Laskin, Pieter Abbeel, Aravind Srinivas, Igor Mordatch.
1. **[Deformable DETR](https://huggingface.co/docs/transformers/model_doc/deformable_detr)** (from SenseTime Research) released with the paper [Deformable DETR: Deformable Transformers for End-to-End Object Detection](https://arxiv.org/abs/2010.04159) by Xizhou Zhu, Weijie Su, Lewei Lu, Bin Li, Xiaogang Wang, Jifeng Dai.
1. **[DeiT](https://huggingface.co/docs/transformers/model_doc/deit)** (from Facebook) released with the paper [Training data-efficient image transformers & distillation through attention](https://arxiv.org/abs/2012.12877) by Hugo Touvron, Matthieu Cord, Matthijs Douze, Francisco Massa, Alexandre Sablayrolles, Hervé Jégou.
1. **[DePlot](https://huggingface.co/docs/transformers/model_doc/deplot)** (from Google AI) released with the paper [DePlot: One-shot visual language reasoning by plot-to-table translation](https://arxiv.org/abs/2212.10505) by Fangyu Liu, Julian Martin Eisenschlos, Francesco Piccinno, Syrine Krichene, Chenxi Pang, Kenton Lee, Mandar Joshi, Wenhu Chen, Nigel Collier, Yasemin Altun.
1. **[DETA](https://huggingface.co/docs/transformers/model_doc/deta)** (from The University of Texas at Austin) released with the paper [NMS Strikes Back](https://arxiv.org/abs/2212.06137) by Jeffrey Ouyang-Zhang, Jang Hyun Cho, Xingyi Zhou, Philipp Krähenbühl.
1. **[DETR](https://huggingface.co/docs/transformers/model_doc/detr)** (from Facebook) released with the paper [End-to-End Object Detection with Transformers](https://arxiv.org/abs/2005.12872) by Nicolas Carion, Francisco Massa, Gabriel Synnaeve, Nicolas Usunier, Alexander Kirillov, Sergey Zagoruyko.
1. **[DialoGPT](https://huggingface.co/docs/transformers/model_doc/dialogpt)** (from Microsoft Research) released with the paper [DialoGPT: Large-Scale Generative Pre-training for Conversational Response Generation](https://arxiv.org/abs/1911.00536) by Yizhe Zhang, Siqi Sun, Michel Galley, Yen-Chun Chen, Chris Brockett, Xiang Gao, Jianfeng Gao, Jingjing Liu, Bill Dolan.
1. **[DiNAT](https://huggingface.co/docs/transformers/model_doc/dinat)** (from SHI Labs) released with the paper [Dilated Neighborhood Attention Transformer](https://arxiv.org/abs/2209.15001) by Ali Hassani and Humphrey Shi.
1. **[DINOv2](https://huggingface.co/docs/transformers/model_doc/dinov2)** (from Meta AI) released with the paper [DINOv2: Learning Robust Visual Features without Supervision](https://arxiv.org/abs/2304.07193) by Maxime Oquab, Timothée Darcet, Théo Moutakanni, Huy Vo, Marc Szafraniec, Vasil Khalidov, Pierre Fernandez, Daniel Haziza, Francisco Massa, Alaaeldin El-Nouby, Mahmoud Assran, Nicolas Ballas, Wojciech Galuba, Russell Howes, Po-Yao Huang, Shang-Wen Li, Ishan Misra, Michael Rabbat, Vasu Sharma, Gabriel Synnaeve, Hu Xu, Hervé Jegou, Julien Mairal, Patrick Labatut, Armand Joulin, Piotr Bojanowski.
1. **[DistilBERT](https://huggingface.co/docs/transformers/model_doc/distilbert)** (from HuggingFace), released together with the paper [DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter](https://arxiv.org/abs/1910.01108) by Victor Sanh, Lysandre Debut and Thomas Wolf. The same method has been applied to compress GPT2 into [DistilGPT2](https://github.com/huggingface/transformers/tree/main/examples/distillation), RoBERTa into [DistilRoBERTa](https://github.com/huggingface/transformers/tree/main/examples/distillation), Multilingual BERT into [DistilmBERT](https://github.com/huggingface/transformers/tree/main/examples/distillation) and a German version of DistilBERT.
1. **[DiT](https://huggingface.co/docs/transformers/model_doc/dit)** (from Microsoft Research) released with the paper [DiT: Self-supervised Pre-training for Document Image Transformer](https://arxiv.org/abs/2203.02378) by Junlong Li, Yiheng Xu, Tengchao Lv, Lei Cui, Cha Zhang, Furu Wei.
1. **[Donut](https://huggingface.co/docs/transformers/model_doc/donut)** (from NAVER) released with the paper [OCR-free Document Understanding Transformer](https://arxiv.org/abs/2111.15664) by Geewook Kim, Teakgyu Hong, Moonbin Yim, Jeongyeon Nam, Jinyoung Park, Jinyeong Yim, Wonseok Hwang, Sangdoo Yun, Dongyoon Han, Seunghyun Park.
1. **[DPR](https://huggingface.co/docs/transformers/model_doc/dpr)** (from Facebook) released with the paper [Dense Passage Retrieval for Open-Domain Question Answering](https://arxiv.org/abs/2004.04906) by Vladimir Karpukhin, Barlas Oğuz, Sewon Min, Patrick Lewis, Ledell Wu, Sergey Edunov, Danqi Chen, and Wen-tau Yih.
1. **[DPT](https://huggingface.co/docs/transformers/master/model_doc/dpt)** (from Intel Labs) released with the paper [Vision Transformers for Dense Prediction](https://arxiv.org/abs/2103.13413) by René Ranftl, Alexey Bochkovskiy, Vladlen Koltun.
1. **[EfficientFormer](https://huggingface.co/docs/transformers/model_doc/efficientformer)** (from Snap Research) released with the paper [EfficientFormer: Vision Transformers at MobileNetSpeed](https://arxiv.org/abs/2206.01191) by Yanyu Li, Geng Yuan, Yang Wen, Ju Hu, Georgios Evangelidis, Sergey Tulyakov, Yanzhi Wang, Jian Ren.
1. **[EfficientNet](https://huggingface.co/docs/transformers/model_doc/efficientnet)** (from Google Brain) released with the paper [EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks](https://arxiv.org/abs/1905.11946) by Mingxing Tan, Quoc V. Le.
1. **[ELECTRA](https://huggingface.co/docs/transformers/model_doc/electra)** (from Google Research/Stanford University) released with the paper [ELECTRA: Pre-training text encoders as discriminators rather than generators](https://arxiv.org/abs/2003.10555) by Kevin Clark, Minh-Thang Luong, Quoc V. Le, Christopher D. Manning.
1. **[EnCodec](https://huggingface.co/docs/transformers/model_doc/encodec)** (from Meta AI) released with the paper [High Fidelity Neural Audio Compression](https://arxiv.org/abs/2210.13438) by Alexandre Défossez, Jade Copet, Gabriel Synnaeve, Yossi Adi.
1. **[EncoderDecoder](https://huggingface.co/docs/transformers/model_doc/encoder-decoder)** (from Google Research) released with the paper [Leveraging Pre-trained Checkpoints for Sequence Generation Tasks](https://arxiv.org/abs/1907.12461) by Sascha Rothe, Shashi Narayan, Aliaksei Severyn.
1. **[ERNIE](https://huggingface.co/docs/transformers/model_doc/ernie)** (from Baidu) released with the paper [ERNIE: Enhanced Representation through Knowledge Integration](https://arxiv.org/abs/1904.09223) by Yu Sun, Shuohuan Wang, Yukun Li, Shikun Feng, Xuyi Chen, Han Zhang, Xin Tian, Danxiang Zhu, Hao Tian, Hua Wu.
1. **[ErnieM](https://huggingface.co/docs/transformers/model_doc/ernie_m)** (from Baidu) released with the paper [ERNIE-M: Enhanced Multilingual Representation by Aligning Cross-lingual Semantics with Monolingual Corpora](https://arxiv.org/abs/2012.15674) by Xuan Ouyang, Shuohuan Wang, Chao Pang, Yu Sun, Hao Tian, Hua Wu, Haifeng Wang.
1. **[ESM](https://huggingface.co/docs/transformers/model_doc/esm)** (from Meta AI) are transformer protein language models. **ESM-1b** was released with the paper [Biological structure and function emerge from scaling unsupervised learning to 250 million protein sequences](https://www.pnas.org/content/118/15/e2016239118) by Alexander Rives, Joshua Meier, Tom Sercu, Siddharth Goyal, Zeming Lin, Jason Liu, Demi Guo, Myle Ott, C. Lawrence Zitnick, Jerry Ma, and Rob Fergus. **ESM-1v** was released with the paper [Language models enable zero-shot prediction of the effects of mutations on protein function](https://doi.org/10.1101/2021.07.09.450648) by Joshua Meier, Roshan Rao, Robert Verkuil, Jason Liu, Tom Sercu and Alexander Rives. **ESM-2** was released with the paper [Language models of protein sequences at the scale of evolution enable accurate structure prediction](https://doi.org/10.1101/2022.07.20.500902) by Zeming Lin, Halil Akin, Roshan Rao, Brian Hie, Zhongkai Zhu, Wenting Lu, Allan dos Santos Costa, Maryam Fazel-Zarandi, Tom Sercu, Sal Candido, Alexander Rives.
1. **[Falcon](https://huggingface.co/docs/transformers/model_doc/falcon)** (from Technology Innovation Institute) by Almazrouei, Ebtesam and Alobeidli, Hamza and Alshamsi, Abdulaziz and Cappelli, Alessandro and Cojocaru, Ruxandra and Debbah, Merouane and Goffinet, Etienne and Heslow, Daniel and Launay, Julien and Malartic, Quentin and Noune, Badreddine and Pannier, Baptiste and Penedo, Guilherme.
1. **[FLAN-T5](https://huggingface.co/docs/transformers/model_doc/flan-t5)** (from Google AI) released in the repository [google-research/t5x](https://github.com/google-research/t5x/blob/main/docs/models.md#flan-t5-checkpoints) by Hyung Won Chung, Le Hou, Shayne Longpre, Barret Zoph, Yi Tay, William Fedus, Eric Li, Xuezhi Wang, Mostafa Dehghani, Siddhartha Brahma, Albert Webson, Shixiang Shane Gu, Zhuyun Dai, Mirac Suzgun, Xinyun Chen, Aakanksha Chowdhery, Sharan Narang, Gaurav Mishra, Adams Yu, Vincent Zhao, Yanping Huang, Andrew Dai, Hongkun Yu, Slav Petrov, Ed H. Chi, Jeff Dean, Jacob Devlin, Adam Roberts, Denny Zhou, Quoc V. Le, and Jason Wei
1. **[FLAN-UL2](https://huggingface.co/docs/transformers/model_doc/flan-ul2)** (from Google AI) released in the repository [google-research/t5x](https://github.com/google-research/t5x/blob/main/docs/models.md#flan-ul2-checkpoints) by Hyung Won Chung, Le Hou, Shayne Longpre, Barret Zoph, Yi Tay, William Fedus, Eric Li, Xuezhi Wang, Mostafa Dehghani, Siddhartha Brahma, Albert Webson, Shixiang Shane Gu, Zhuyun Dai, Mirac Suzgun, Xinyun Chen, Aakanksha Chowdhery, Sharan Narang, Gaurav Mishra, Adams Yu, Vincent Zhao, Yanping Huang, Andrew Dai, Hongkun Yu, Slav Petrov, Ed H. Chi, Jeff Dean, Jacob Devlin, Adam Roberts, Denny Zhou, Quoc V. Le, and Jason Wei
1. **[FlauBERT](https://huggingface.co/docs/transformers/model_doc/flaubert)** (from CNRS) released with the paper [FlauBERT: Unsupervised Language Model Pre-training for French](https://arxiv.org/abs/1912.05372) by Hang Le, Loïc Vial, Jibril Frej, Vincent Segonne, Maximin Coavoux, Benjamin Lecouteux, Alexandre Allauzen, Benoît Crabbé, Laurent Besacier, Didier Schwab.
1. **[FLAVA](https://huggingface.co/docs/transformers/model_doc/flava)** (from Facebook AI) released with the paper [FLAVA: A Foundational Language And Vision Alignment Model](https://arxiv.org/abs/2112.04482) by Amanpreet Singh, Ronghang Hu, Vedanuj Goswami, Guillaume Couairon, Wojciech Galuba, Marcus Rohrbach, and Douwe Kiela.
1. **[FNet](https://huggingface.co/docs/transformers/model_doc/fnet)** (from Google Research) released with the paper [FNet: Mixing Tokens with Fourier Transforms](https://arxiv.org/abs/2105.03824) by James Lee-Thorp, Joshua Ainslie, Ilya Eckstein, Santiago Ontanon.
1. **[FocalNet](https://huggingface.co/docs/transformers/model_doc/focalnet)** (from Microsoft Research) released with the paper [Focal Modulation Networks](https://arxiv.org/abs/2203.11926) by Jianwei Yang, Chunyuan Li, Xiyang Dai, Lu Yuan, Jianfeng Gao.
1. **[Funnel Transformer](https://huggingface.co/docs/transformers/model_doc/funnel)** (from CMU/Google Brain) released with the paper [Funnel-Transformer: Filtering out Sequential Redundancy for Efficient Language Processing](https://arxiv.org/abs/2006.03236) by Zihang Dai, Guokun Lai, Yiming Yang, Quoc V. Le.
1. **[Fuyu](https://huggingface.co/docs/transformers/model_doc/fuyu)** (from ADEPT) Rohan Bavishi, Erich Elsen, Curtis Hawthorne, Maxwell Nye, Augustus Odena, Arushi Somani, Sağnak Taşırlar. Released with the paper [blog post](https://www.adept.ai/blog/fuyu-8b)
1. **[GIT](https://huggingface.co/docs/transformers/model_doc/git)** (from Microsoft Research) released with the paper [GIT: A Generative Image-to-text Transformer for Vision and Language](https://arxiv.org/abs/2205.14100) by Jianfeng Wang, Zhengyuan Yang, Xiaowei Hu, Linjie Li, Kevin Lin, Zhe Gan, Zicheng Liu, Ce Liu, Lijuan Wang.
1. **[GLPN](https://huggingface.co/docs/transformers/model_doc/glpn)** (from KAIST) released with the paper [Global-Local Path Networks for Monocular Depth Estimation with Vertical CutDepth](https://arxiv.org/abs/2201.07436) by Doyeon Kim, Woonghyun Ga, Pyungwhan Ahn, Donggyu Joo, Sehwan Chun, Junmo Kim.
1. **[GPT](https://huggingface.co/docs/transformers/model_doc/openai-gpt)** (from OpenAI) released with the paper [Improving Language Understanding by Generative Pre-Training](https://blog.openai.com/language-unsupervised/) by Alec Radford, Karthik Narasimhan, Tim Salimans and Ilya Sutskever.
1. **[GPT Neo](https://huggingface.co/docs/transformers/model_doc/gpt_neo)** (from EleutherAI) released in the repository [EleutherAI/gpt-neo](https://github.com/EleutherAI/gpt-neo) by Sid Black, Stella Biderman, Leo Gao, Phil Wang and Connor Leahy.
1. **[GPT NeoX](https://huggingface.co/docs/transformers/model_doc/gpt_neox)** (from EleutherAI) released with the paper [GPT-NeoX-20B: An Open-Source Autoregressive Language Model](https://arxiv.org/abs/2204.06745) by Sid Black, Stella Biderman, Eric Hallahan, Quentin Anthony, Leo Gao, Laurence Golding, Horace He, Connor Leahy, Kyle McDonell, Jason Phang, Michael Pieler, USVSN Sai Prashanth, Shivanshu Purohit, Laria Reynolds, Jonathan Tow, Ben Wang, Samuel Weinbach
1. **[GPT NeoX Japanese](https://huggingface.co/docs/transformers/model_doc/gpt_neox_japanese)** (from ABEJA) released by Shinya Otani, Takayoshi Makabe, Anuj Arora, and Kyo Hattori.
1. **[GPT-2](https://huggingface.co/docs/transformers/model_doc/gpt2)** (from OpenAI) released with the paper [Language Models are Unsupervised Multitask Learners](https://blog.openai.com/better-language-models/) by Alec Radford*, Jeffrey Wu*, Rewon Child, David Luan, Dario Amodei** and Ilya Sutskever**.
1. **[GPT-J](https://huggingface.co/docs/transformers/model_doc/gptj)** (from EleutherAI) released with the paper [kingoflolz/mesh-transformer-jax](https://github.com/kingoflolz/mesh-transformer-jax/) by Ben Wang and Aran Komatsuzaki.
1. **[GPT-Sw3](https://huggingface.co/docs/transformers/model_doc/gpt-sw3)** (from AI-Sweden) released with the paper [Lessons Learned from GPT-SW3: Building the First Large-Scale Generative Language Model for Swedish](http://www.lrec-conf.org/proceedings/lrec2022/pdf/2022.lrec-1.376.pdf) by Ariel Ekgren, Amaru Cuba Gyllensten, Evangelia Gogoulou, Alice Heiman, Severine Verlinden, Joey Öhman, Fredrik Carlsson, Magnus Sahlgren.
1. **[GPTBigCode](https://huggingface.co/docs/transformers/model_doc/gpt_bigcode)** (from BigCode) released with the paper [SantaCoder: don't reach for the stars!](https://arxiv.org/abs/2301.03988) by Loubna Ben Allal, Raymond Li, Denis Kocetkov, Chenghao Mou, Christopher Akiki, Carlos Munoz Ferrandis, Niklas Muennighoff, Mayank Mishra, Alex Gu, Manan Dey, Logesh Kumar Umapathi, Carolyn Jane Anderson, Yangtian Zi, Joel Lamy Poirier, Hailey Schoelkopf, Sergey Troshin, Dmitry Abulkhanov, Manuel Romero, Michael Lappert, Francesco De Toni, Bernardo García del Río, Qian Liu, Shamik Bose, Urvashi Bhattacharyya, Terry Yue Zhuo, Ian Yu, Paulo Villegas, Marco Zocca, Sourab Mangrulkar, David Lansky, Huu Nguyen, Danish Contractor, Luis Villa, Jia Li, Dzmitry Bahdanau, Yacine Jernite, Sean Hughes, Daniel Fried, Arjun Guha, Harm de Vries, Leandro von Werra.
1. **[GPTSAN-japanese](https://huggingface.co/docs/transformers/model_doc/gptsan-japanese)** released in the repository [tanreinama/GPTSAN](https://github.com/tanreinama/GPTSAN/blob/main/report/model.md) by 坂本俊之(tanreinama).
1. **[Graphormer](https://huggingface.co/docs/transformers/model_doc/graphormer)** (from Microsoft) released with the paper [Do Transformers Really Perform Bad for Graph Representation?](https://arxiv.org/abs/2106.05234) by Chengxuan Ying, Tianle Cai, Shengjie Luo, Shuxin Zheng, Guolin Ke, Di He, Yanming Shen, Tie-Yan Liu.
1. **[GroupViT](https://huggingface.co/docs/transformers/model_doc/groupvit)** (from UCSD, NVIDIA) released with the paper [GroupViT: Semantic Segmentation Emerges from Text Supervision](https://arxiv.org/abs/2202.11094) by Jiarui Xu, Shalini De Mello, Sifei Liu, Wonmin Byeon, Thomas Breuel, Jan Kautz, Xiaolong Wang.
1. **[HerBERT](https://huggingface.co/docs/transformers/model_doc/herbert)** (from Allegro.pl, AGH University of Science and Technology) released with the paper [KLEJ: Comprehensive Benchmark for Polish Language Understanding](https://www.aclweb.org/anthology/2020.acl-main.111.pdf) by Piotr Rybak, Robert Mroczkowski, Janusz Tracz, Ireneusz Gawlik.
1. **[Hubert](https://huggingface.co/docs/transformers/model_doc/hubert)** (from Facebook) released with the paper [HuBERT: Self-Supervised Speech Representation Learning by Masked Prediction of Hidden Units](https://arxiv.org/abs/2106.07447) by Wei-Ning Hsu, Benjamin Bolte, Yao-Hung Hubert Tsai, Kushal Lakhotia, Ruslan Salakhutdinov, Abdelrahman Mohamed.
1. **[I-BERT](https://huggingface.co/docs/transformers/model_doc/ibert)** (from Berkeley) released with the paper [I-BERT: Integer-only BERT Quantization](https://arxiv.org/abs/2101.01321) by Sehoon Kim, Amir Gholami, Zhewei Yao, Michael W. Mahoney, Kurt Keutzer.
1. **[IDEFICS](https://huggingface.co/docs/transformers/model_doc/idefics)** (from HuggingFace) released with the paper [OBELICS: An Open Web-Scale Filtered Dataset of Interleaved Image-Text Documents](https://huggingface.co/papers/2306.16527) by Hugo Laurençon, Lucile Saulnier, Léo Tronchon, Stas Bekman, Amanpreet Singh, Anton Lozhkov, Thomas Wang, Siddharth Karamcheti, Alexander M. Rush, Douwe Kiela, Matthieu Cord, Victor Sanh.
1. **[ImageGPT](https://huggingface.co/docs/transformers/model_doc/imagegpt)** (from OpenAI) released with the paper [Generative Pretraining from Pixels](https://openai.com/blog/image-gpt/) by Mark Chen, Alec Radford, Rewon Child, Jeffrey Wu, Heewoo Jun, David Luan, Ilya Sutskever.
1. **[Informer](https://huggingface.co/docs/transformers/model_doc/informer)** (from Beihang University, UC Berkeley, Rutgers University, SEDD Company) released with the paper [Informer: Beyond Efficient Transformer for Long Sequence Time-Series Forecasting](https://arxiv.org/abs/2012.07436) by Haoyi Zhou, Shanghang Zhang, Jieqi Peng, Shuai Zhang, Jianxin Li, Hui Xiong, and Wancai Zhang.
1. **[InstructBLIP](https://huggingface.co/docs/transformers/model_doc/instructblip)** (from Salesforce) released with the paper [InstructBLIP: Towards General-purpose Vision-Language Models with Instruction Tuning](https://arxiv.org/abs/2305.06500) by Wenliang Dai, Junnan Li, Dongxu Li, Anthony Meng Huat Tiong, Junqi Zhao, Weisheng Wang, Boyang Li, Pascale Fung, Steven Hoi.
1. **[Jukebox](https://huggingface.co/docs/transformers/model_doc/jukebox)** (from OpenAI) released with the paper [Jukebox: A Generative Model for Music](https://arxiv.org/pdf/2005.00341.pdf) by Prafulla Dhariwal, Heewoo Jun, Christine Payne, Jong Wook Kim, Alec Radford, Ilya Sutskever.
1. **[KOSMOS-2](https://huggingface.co/docs/transformers/model_doc/kosmos-2)** (from Microsoft Research Asia) released with the paper [Kosmos-2: Grounding Multimodal Large Language Models to the World](https://arxiv.org/abs/2306.14824) by Zhiliang Peng, Wenhui Wang, Li Dong, Yaru Hao, Shaohan Huang, Shuming Ma, Furu Wei.
1. **[LayoutLM](https://huggingface.co/docs/transformers/model_doc/layoutlm)** (from Microsoft Research Asia) released with the paper [LayoutLM: Pre-training of Text and Layout for Document Image Understanding](https://arxiv.org/abs/1912.13318) by Yiheng Xu, Minghao Li, Lei Cui, Shaohan Huang, Furu Wei, Ming Zhou.
1. **[LayoutLMv2](https://huggingface.co/docs/transformers/model_doc/layoutlmv2)** (from Microsoft Research Asia) released with the paper [LayoutLMv2: Multi-modal Pre-training for Visually-Rich Document Understanding](https://arxiv.org/abs/2012.14740) by Yang Xu, Yiheng Xu, Tengchao Lv, Lei Cui, Furu Wei, Guoxin Wang, Yijuan Lu, Dinei Florencio, Cha Zhang, Wanxiang Che, Min Zhang, Lidong Zhou.
1. **[LayoutLMv3](https://huggingface.co/docs/transformers/model_doc/layoutlmv3)** (from Microsoft Research Asia) released with the paper [LayoutLMv3: Pre-training for Document AI with Unified Text and Image Masking](https://arxiv.org/abs/2204.08387) by Yupan Huang, Tengchao Lv, Lei Cui, Yutong Lu, Furu Wei.
1. **[LayoutXLM](https://huggingface.co/docs/transformers/model_doc/layoutxlm)** (from Microsoft Research Asia) released with the paper [LayoutXLM: Multimodal Pre-training for Multilingual Visually-rich Document Understanding](https://arxiv.org/abs/2104.08836) by Yiheng Xu, Tengchao Lv, Lei Cui, Guoxin Wang, Yijuan Lu, Dinei Florencio, Cha Zhang, Furu Wei.
1. **[LED](https://huggingface.co/docs/transformers/model_doc/led)** (from AllenAI) released with the paper [Longformer: The Long-Document Transformer](https://arxiv.org/abs/2004.05150) by Iz Beltagy, Matthew E. Peters, Arman Cohan.
1. **[LeViT](https://huggingface.co/docs/transformers/model_doc/levit)** (from Meta AI) released with the paper [LeViT: A Vision Transformer in ConvNet's Clothing for Faster Inference](https://arxiv.org/abs/2104.01136) by Ben Graham, Alaaeldin El-Nouby, Hugo Touvron, Pierre Stock, Armand Joulin, Hervé Jégou, Matthijs Douze.
1. **[LiLT](https://huggingface.co/docs/transformers/model_doc/lilt)** (from South China University of Technology) released with the paper [LiLT: A Simple yet Effective Language-Independent Layout Transformer for Structured Document Understanding](https://arxiv.org/abs/2202.13669) by Jiapeng Wang, Lianwen Jin, Kai Ding.
1. **[LLaMA](https://huggingface.co/docs/transformers/model_doc/llama)** (from The FAIR team of Meta AI) released with the paper [LLaMA: Open and Efficient Foundation Language Models](https://arxiv.org/abs/2302.13971) by Hugo Touvron, Thibaut Lavril, Gautier Izacard, Xavier Martinet, Marie-Anne Lachaux, Timothée Lacroix, Baptiste Rozière, Naman Goyal, Eric Hambro, Faisal Azhar, Aurelien Rodriguez, Armand Joulin, Edouard Grave, Guillaume Lample.
1. **[Llama2](https://huggingface.co/docs/transformers/model_doc/llama2)** (from The FAIR team of Meta AI) released with the paper [Llama2: Open Foundation and Fine-Tuned Chat Models](https://ai.meta.com/research/publications/llama-2-open-foundation-and-fine-tuned-chat-models/XXX) by Hugo Touvron, Louis Martin, Kevin Stone, Peter Albert, Amjad Almahairi, Yasmine Babaei, Nikolay Bashlykov, Soumya Batra, Prajjwal Bhargava, Shruti Bhosale, Dan Bikel, Lukas Blecher, Cristian Canton Ferrer, Moya Chen, Guillem Cucurull, David Esiobu, Jude Fernandes, Jeremy Fu, Wenyin Fu, Brian Fuller, Cynthia Gao, Vedanuj Goswami, Naman Goyal, Anthony Hartshorn, Saghar Hosseini, Rui Hou, Hakan Inan, Marcin Kardas, Viktor Kerkez Madian Khabsa, Isabel Kloumann, Artem Korenev, Punit Singh Koura, Marie-Anne Lachaux, Thibaut Lavril, Jenya Lee, Diana Liskovich, Yinghai Lu, Yuning Mao, Xavier Martinet, Todor Mihaylov, Pushka rMishra, Igor Molybog, Yixin Nie, Andrew Poulton, Jeremy Reizenstein, Rashi Rungta, Kalyan Saladi, Alan Schelten, Ruan Silva, Eric Michael Smith, Ranjan Subramanian, Xiaoqing EllenTan, Binh Tang, Ross Taylor, Adina Williams, Jian Xiang Kuan, Puxin Xu, Zheng Yan, Iliyan Zarov, Yuchen Zhang, Angela Fan, Melanie Kambadur, Sharan Narang, Aurelien Rodriguez, Robert Stojnic, Sergey Edunov, Thomas Scialom..
1. **[LLaVa](https://huggingface.co/docs/transformers/model_doc/llava)** (from Microsoft Research & University of Wisconsin-Madison) released with the paper [Visual Instruction Tuning](https://arxiv.org/abs/2304.08485) by Haotian Liu, Chunyuan Li, Yuheng Li and Yong Jae Lee.
1. **[Longformer](https://huggingface.co/docs/transformers/model_doc/longformer)** (from AllenAI) released with the paper [Longformer: The Long-Document Transformer](https://arxiv.org/abs/2004.05150) by Iz Beltagy, Matthew E. Peters, Arman Cohan.
1. **[LongT5](https://huggingface.co/docs/transformers/model_doc/longt5)** (from Google AI) released with the paper [LongT5: Efficient Text-To-Text Transformer for Long Sequences](https://arxiv.org/abs/2112.07916) by Mandy Guo, Joshua Ainslie, David Uthus, Santiago Ontanon, Jianmo Ni, Yun-Hsuan Sung, Yinfei Yang.
1. **[LUKE](https://huggingface.co/docs/transformers/model_doc/luke)** (from Studio Ousia) released with the paper [LUKE: Deep Contextualized Entity Representations with Entity-aware Self-attention](https://arxiv.org/abs/2010.01057) by Ikuya Yamada, Akari Asai, Hiroyuki Shindo, Hideaki Takeda, Yuji Matsumoto.
1. **[LXMERT](https://huggingface.co/docs/transformers/model_doc/lxmert)** (from UNC Chapel Hill) released with the paper [LXMERT: Learning Cross-Modality Encoder Representations from Transformers for Open-Domain Question Answering](https://arxiv.org/abs/1908.07490) by Hao Tan and Mohit Bansal.
1. **[M-CTC-T](https://huggingface.co/docs/transformers/model_doc/mctct)** (from Facebook) released with the paper [Pseudo-Labeling For Massively Multilingual Speech Recognition](https://arxiv.org/abs/2111.00161) by Loren Lugosch, Tatiana Likhomanenko, Gabriel Synnaeve, and Ronan Collobert.
1. **[M2M100](https://huggingface.co/docs/transformers/model_doc/m2m_100)** (from Facebook) released with the paper [Beyond English-Centric Multilingual Machine Translation](https://arxiv.org/abs/2010.11125) by Angela Fan, Shruti Bhosale, Holger Schwenk, Zhiyi Ma, Ahmed El-Kishky, Siddharth Goyal, Mandeep Baines, Onur Celebi, Guillaume Wenzek, Vishrav Chaudhary, Naman Goyal, Tom Birch, Vitaliy Liptchinsky, Sergey Edunov, Edouard Grave, Michael Auli, Armand Joulin.
1. **[MADLAD-400](https://huggingface.co/docs/transformers/model_doc/madlad-400)** (from Google) released with the paper [MADLAD-400: A Multilingual And Document-Level Large Audited Dataset](https://arxiv.org/abs/2309.04662) by Sneha Kudugunta, Isaac Caswell, Biao Zhang, Xavier Garcia, Christopher A. Choquette-Choo, Katherine Lee, Derrick Xin, Aditya Kusupati, Romi Stella, Ankur Bapna, Orhan Firat.
1. **[MarianMT](https://huggingface.co/docs/transformers/model_doc/marian)** Machine translation models trained using [OPUS](http://opus.nlpl.eu/) data by Jörg Tiedemann. The [Marian Framework](https://marian-nmt.github.io/) is being developed by the Microsoft Translator Team.
1. **[MarkupLM](https://huggingface.co/docs/transformers/model_doc/markuplm)** (from Microsoft Research Asia) released with the paper [MarkupLM: Pre-training of Text and Markup Language for Visually-rich Document Understanding](https://arxiv.org/abs/2110.08518) by Junlong Li, Yiheng Xu, Lei Cui, Furu Wei.
1. **[Mask2Former](https://huggingface.co/docs/transformers/model_doc/mask2former)** (from FAIR and UIUC) released with the paper [Masked-attention Mask Transformer for Universal Image Segmentation](https://arxiv.org/abs/2112.01527) by Bowen Cheng, Ishan Misra, Alexander G. Schwing, Alexander Kirillov, Rohit Girdhar.
1. **[MaskFormer](https://huggingface.co/docs/transformers/model_doc/maskformer)** (from Meta and UIUC) released with the paper [Per-Pixel Classification is Not All You Need for Semantic Segmentation](https://arxiv.org/abs/2107.06278) by Bowen Cheng, Alexander G. Schwing, Alexander Kirillov
1. **[MatCha](https://huggingface.co/docs/transformers/model_doc/matcha)** (from Google AI) released with the paper [MatCha: Enhancing Visual Language Pretraining with Math Reasoning and Chart Derendering](https://arxiv.org/abs/2212.09662) by Fangyu Liu, Francesco Piccinno, Syrine Krichene, Chenxi Pang, Kenton Lee, Mandar Joshi, Yasemin Altun, Nigel Collier, Julian Martin Eisenschlos.
1. **[mBART](https://huggingface.co/docs/transformers/model_doc/mbart)** (from Facebook) released with the paper [Multilingual Denoising Pre-training for Neural Machine Translation](https://arxiv.org/abs/2001.08210) by Yinhan Liu, Jiatao Gu, Naman Goyal, Xian Li, Sergey Edunov, Marjan Ghazvininejad, Mike Lewis, Luke Zettlemoyer.
1. **[mBART-50](https://huggingface.co/docs/transformers/model_doc/mbart)** (from Facebook) released with the paper [Multilingual Translation with Extensible Multilingual Pretraining and Finetuning](https://arxiv.org/abs/2008.00401) by Yuqing Tang, Chau Tran, Xian Li, Peng-Jen Chen, Naman Goyal, Vishrav Chaudhary, Jiatao Gu, Angela Fan.
1. **[MEGA](https://huggingface.co/docs/transformers/model_doc/mega)** (from Facebook) released with the paper [Mega: Moving Average Equipped Gated Attention](https://arxiv.org/abs/2209.10655) by Xuezhe Ma, Chunting Zhou, Xiang Kong, Junxian He, Liangke Gui, Graham Neubig, Jonathan May, and Luke Zettlemoyer.
1. **[Megatron-BERT](https://huggingface.co/docs/transformers/model_doc/megatron-bert)** (from NVIDIA) released with the paper [Megatron-LM: Training Multi-Billion Parameter Language Models Using Model Parallelism](https://arxiv.org/abs/1909.08053) by Mohammad Shoeybi, Mostofa Patwary, Raul Puri, Patrick LeGresley, Jared Casper and Bryan Catanzaro.
1. **[Megatron-GPT2](https://huggingface.co/docs/transformers/model_doc/megatron_gpt2)** (from NVIDIA) released with the paper [Megatron-LM: Training Multi-Billion Parameter Language Models Using Model Parallelism](https://arxiv.org/abs/1909.08053) by Mohammad Shoeybi, Mostofa Patwary, Raul Puri, Patrick LeGresley, Jared Casper and Bryan Catanzaro.
1. **[MGP-STR](https://huggingface.co/docs/transformers/model_doc/mgp-str)** (from Alibaba Research) released with the paper [Multi-Granularity Prediction for Scene Text Recognition](https://arxiv.org/abs/2209.03592) by Peng Wang, Cheng Da, and Cong Yao.
1. **[Mistral](https://huggingface.co/docs/transformers/model_doc/mistral)** (from Mistral AI) by The Mistral AI team: Albert Jiang, Alexandre Sablayrolles, Arthur Mensch, Chris Bamford, Devendra Singh Chaplot, Diego de las Casas, Florian Bressand, Gianna Lengyel, Guillaume Lample, Lélio Renard Lavaud, Lucile Saulnier, Marie-Anne Lachaux, Pierre Stock, Teven Le Scao, Thibaut Lavril, Thomas Wang, Timothée Lacroix, William El Sayed..
1. **[Mixtral](https://huggingface.co/docs/transformers/model_doc/mixtral)** (from Mistral AI) by The [Mistral AI](https://mistral.ai) team: Albert Jiang, Alexandre Sablayrolles, Arthur Mensch, Chris Bamford, Devendra Singh Chaplot, Diego de las Casas, Florian Bressand, Gianna Lengyel, Guillaume Lample, Lélio Renard Lavaud, Lucile Saulnier, Marie-Anne Lachaux, Pierre Stock, Teven Le Scao, Thibaut Lavril, Thomas Wang, Timothée Lacroix, William El Sayed.
1. **[mLUKE](https://huggingface.co/docs/transformers/model_doc/mluke)** (from Studio Ousia) released with the paper [mLUKE: The Power of Entity Representations in Multilingual Pretrained Language Models](https://arxiv.org/abs/2110.08151) by Ryokan Ri, Ikuya Yamada, and Yoshimasa Tsuruoka.
1. **[MMS](https://huggingface.co/docs/transformers/model_doc/mms)** (from Facebook) released with the paper [Scaling Speech Technology to 1,000+ Languages](https://arxiv.org/abs/2305.13516) by Vineel Pratap, Andros Tjandra, Bowen Shi, Paden Tomasello, Arun Babu, Sayani Kundu, Ali Elkahky, Zhaoheng Ni, Apoorv Vyas, Maryam Fazel-Zarandi, Alexei Baevski, Yossi Adi, Xiaohui Zhang, Wei-Ning Hsu, Alexis Conneau, Michael Auli.
1. **[MobileBERT](https://huggingface.co/docs/transformers/model_doc/mobilebert)** (from CMU/Google Brain) released with the paper [MobileBERT: a Compact Task-Agnostic BERT for Resource-Limited Devices](https://arxiv.org/abs/2004.02984) by Zhiqing Sun, Hongkun Yu, Xiaodan Song, Renjie Liu, Yiming Yang, and Denny Zhou.
1. **[MobileNetV1](https://huggingface.co/docs/transformers/model_doc/mobilenet_v1)** (from Google Inc.) released with the paper [MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications](https://arxiv.org/abs/1704.04861) by Andrew G. Howard, Menglong Zhu, Bo Chen, Dmitry Kalenichenko, Weijun Wang, Tobias Weyand, Marco Andreetto, Hartwig Adam.
1. **[MobileNetV2](https://huggingface.co/docs/transformers/model_doc/mobilenet_v2)** (from Google Inc.) released with the paper [MobileNetV2: Inverted Residuals and Linear Bottlenecks](https://arxiv.org/abs/1801.04381) by Mark Sandler, Andrew Howard, Menglong Zhu, Andrey Zhmoginov, Liang-Chieh Chen.
1. **[MobileViT](https://huggingface.co/docs/transformers/model_doc/mobilevit)** (from Apple) released with the paper [MobileViT: Light-weight, General-purpose, and Mobile-friendly Vision Transformer](https://arxiv.org/abs/2110.02178) by Sachin Mehta and Mohammad Rastegari.
1. **[MobileViTV2](https://huggingface.co/docs/transformers/model_doc/mobilevitv2)** (from Apple) released with the paper [Separable Self-attention for Mobile Vision Transformers](https://arxiv.org/abs/2206.02680) by Sachin Mehta and Mohammad Rastegari.
1. **[MPNet](https://huggingface.co/docs/transformers/model_doc/mpnet)** (from Microsoft Research) released with the paper [MPNet: Masked and Permuted Pre-training for Language Understanding](https://arxiv.org/abs/2004.09297) by Kaitao Song, Xu Tan, Tao Qin, Jianfeng Lu, Tie-Yan Liu.
1. **[MPT](https://huggingface.co/docs/transformers/model_doc/mpt)** (from MosaiML) released with the paper [llm-foundry](https://github.com/mosaicml/llm-foundry/) by the MosaicML NLP Team.
1. **[MRA](https://huggingface.co/docs/transformers/model_doc/mra)** (from the University of Wisconsin - Madison) released with the paper [Multi Resolution Analysis (MRA)](https://arxiv.org/abs/2207.10284) by Zhanpeng Zeng, Sourav Pal, Jeffery Kline, Glenn M Fung, Vikas Singh.
1. **[MT5](https://huggingface.co/docs/transformers/model_doc/mt5)** (from Google AI) released with the paper [mT5: A massively multilingual pre-trained text-to-text transformer](https://arxiv.org/abs/2010.11934) by Linting Xue, Noah Constant, Adam Roberts, Mihir Kale, Rami Al-Rfou, Aditya Siddhant, Aditya Barua, Colin Raffel.
1. **[MusicGen](https://huggingface.co/docs/transformers/model_doc/musicgen)** (from Meta) released with the paper [Simple and Controllable Music Generation](https://arxiv.org/abs/2306.05284) by Jade Copet, Felix Kreuk, Itai Gat, Tal Remez, David Kant, Gabriel Synnaeve, Yossi Adi and Alexandre Défossez.
1. **[MVP](https://huggingface.co/docs/transformers/model_doc/mvp)** (from RUC AI Box) released with the paper [MVP: Multi-task Supervised Pre-training for Natural Language Generation](https://arxiv.org/abs/2206.12131) by Tianyi Tang, Junyi Li, Wayne Xin Zhao and Ji-Rong Wen.
1. **[NAT](https://huggingface.co/docs/transformers/model_doc/nat)** (from SHI Labs) released with the paper [Neighborhood Attention Transformer](https://arxiv.org/abs/2204.07143) by Ali Hassani, Steven Walton, Jiachen Li, Shen Li, and Humphrey Shi.
1. **[Nezha](https://huggingface.co/docs/transformers/model_doc/nezha)** (from Huawei Noah’s Ark Lab) released with the paper [NEZHA: Neural Contextualized Representation for Chinese Language Understanding](https://arxiv.org/abs/1909.00204) by Junqiu Wei, Xiaozhe Ren, Xiaoguang Li, Wenyong Huang, Yi Liao, Yasheng Wang, Jiashu Lin, Xin Jiang, Xiao Chen and Qun Liu.
1. **[NLLB](https://huggingface.co/docs/transformers/model_doc/nllb)** (from Meta) released with the paper [No Language Left Behind: Scaling Human-Centered Machine Translation](https://arxiv.org/abs/2207.04672) by the NLLB team.
1. **[NLLB-MOE](https://huggingface.co/docs/transformers/model_doc/nllb-moe)** (from Meta) released with the paper [No Language Left Behind: Scaling Human-Centered Machine Translation](https://arxiv.org/abs/2207.04672) by the NLLB team.
1. **[Nougat](https://huggingface.co/docs/transformers/model_doc/nougat)** (from Meta AI) released with the paper [Nougat: Neural Optical Understanding for Academic Documents](https://arxiv.org/abs/2308.13418) by Lukas Blecher, Guillem Cucurull, Thomas Scialom, Robert Stojnic.
1. **[Nyströmformer](https://huggingface.co/docs/transformers/model_doc/nystromformer)** (from the University of Wisconsin - Madison) released with the paper [Nyströmformer: A Nyström-Based Algorithm for Approximating Self-Attention](https://arxiv.org/abs/2102.03902) by Yunyang Xiong, Zhanpeng Zeng, Rudrasis Chakraborty, Mingxing Tan, Glenn Fung, Yin Li, Vikas Singh.
1. **[OneFormer](https://huggingface.co/docs/transformers/model_doc/oneformer)** (from SHI Labs) released with the paper [OneFormer: One Transformer to Rule Universal Image Segmentation](https://arxiv.org/abs/2211.06220) by Jitesh Jain, Jiachen Li, MangTik Chiu, Ali Hassani, Nikita Orlov, Humphrey Shi.
1. **[OpenLlama](https://huggingface.co/docs/transformers/model_doc/open-llama)** (from [s-JoL](https://huggingface.co/s-JoL)) released on GitHub (now removed).
1. **[OPT](https://huggingface.co/docs/transformers/master/model_doc/opt)** (from Meta AI) released with the paper [OPT: Open Pre-trained Transformer Language Models](https://arxiv.org/abs/2205.01068) by Susan Zhang, Stephen Roller, Naman Goyal, Mikel Artetxe, Moya Chen, Shuohui Chen et al.
1. **[OWL-ViT](https://huggingface.co/docs/transformers/model_doc/owlvit)** (from Google AI) released with the paper [Simple Open-Vocabulary Object Detection with Vision Transformers](https://arxiv.org/abs/2205.06230) by Matthias Minderer, Alexey Gritsenko, Austin Stone, Maxim Neumann, Dirk Weissenborn, Alexey Dosovitskiy, Aravindh Mahendran, Anurag Arnab, Mostafa Dehghani, Zhuoran Shen, Xiao Wang, Xiaohua Zhai, Thomas Kipf, and Neil Houlsby.
1. **[OWLv2](https://huggingface.co/docs/transformers/model_doc/owlv2)** (from Google AI) released with the paper [Scaling Open-Vocabulary Object Detection](https://arxiv.org/abs/2306.09683) by Matthias Minderer, Alexey Gritsenko, Neil Houlsby.
1. **[PatchTSMixer](https://huggingface.co/docs/transformers/model_doc/patchtsmixer)** (from IBM Research) released with the paper [TSMixer: Lightweight MLP-Mixer Model for Multivariate Time Series Forecasting](https://arxiv.org/pdf/2306.09364.pdf) by Vijay Ekambaram, Arindam Jati, Nam Nguyen, Phanwadee Sinthong, Jayant Kalagnanam.
1. **[PatchTST](https://huggingface.co/docs/transformers/model_doc/patchtst)** (from IBM) released with the paper [A Time Series is Worth 64 Words: Long-term Forecasting with Transformers](https://arxiv.org/pdf/2211.14730.pdf) by Yuqi Nie, Nam H. Nguyen, Phanwadee Sinthong, Jayant Kalagnanam.
1. **[Pegasus](https://huggingface.co/docs/transformers/model_doc/pegasus)** (from Google) released with the paper [PEGASUS: Pre-training with Extracted Gap-sentences for Abstractive Summarization](https://arxiv.org/abs/1912.08777) by Jingqing Zhang, Yao Zhao, Mohammad Saleh and Peter J. Liu.
1. **[PEGASUS-X](https://huggingface.co/docs/transformers/model_doc/pegasus_x)** (from Google) released with the paper [Investigating Efficiently Extending Transformers for Long Input Summarization](https://arxiv.org/abs/2208.04347) by Jason Phang, Yao Zhao, Peter J. Liu.
1. **[Perceiver IO](https://huggingface.co/docs/transformers/model_doc/perceiver)** (from Deepmind) released with the paper [Perceiver IO: A General Architecture for Structured Inputs & Outputs](https://arxiv.org/abs/2107.14795) by Andrew Jaegle, Sebastian Borgeaud, Jean-Baptiste Alayrac, Carl Doersch, Catalin Ionescu, David Ding, Skanda Koppula, Daniel Zoran, Andrew Brock, Evan Shelhamer, Olivier Hénaff, Matthew M. Botvinick, Andrew Zisserman, Oriol Vinyals, João Carreira.
1. **[Persimmon](https://huggingface.co/docs/transformers/model_doc/persimmon)** (from ADEPT) released with the paper [blog post](https://www.adept.ai/blog/persimmon-8b) by Erich Elsen, Augustus Odena, Maxwell Nye, Sağnak Taşırlar, Tri Dao, Curtis Hawthorne, Deepak Moparthi, Arushi Somani.
1. **[Phi](https://huggingface.co/docs/transformers/model_doc/phi)** (from Microsoft) released with the papers - [Textbooks Are All You Need](https://arxiv.org/abs/2306.11644) by Suriya Gunasekar, Yi Zhang, Jyoti Aneja, Caio César Teodoro Mendes, Allie Del Giorno, Sivakanth Gopi, Mojan Javaheripi, Piero Kauffmann, Gustavo de Rosa, Olli Saarikivi, Adil Salim, Shital Shah, Harkirat Singh Behl, Xin Wang, Sébastien Bubeck, Ronen Eldan, Adam Tauman Kalai, Yin Tat Lee and Yuanzhi Li, [Textbooks Are All You Need II: phi-1.5 technical report](https://arxiv.org/abs/2309.05463) by Yuanzhi Li, Sébastien Bubeck, Ronen Eldan, Allie Del Giorno, Suriya Gunasekar and Yin Tat Lee.
1. **[PhoBERT](https://huggingface.co/docs/transformers/model_doc/phobert)** (from VinAI Research) released with the paper [PhoBERT: Pre-trained language models for Vietnamese](https://www.aclweb.org/anthology/2020.findings-emnlp.92/) by Dat Quoc Nguyen and Anh Tuan Nguyen.
1. **[Pix2Struct](https://huggingface.co/docs/transformers/model_doc/pix2struct)** (from Google) released with the paper [Pix2Struct: Screenshot Parsing as Pretraining for Visual Language Understanding](https://arxiv.org/abs/2210.03347) by Kenton Lee, Mandar Joshi, Iulia Turc, Hexiang Hu, Fangyu Liu, Julian Eisenschlos, Urvashi Khandelwal, Peter Shaw, Ming-Wei Chang, Kristina Toutanova.
1. **[PLBart](https://huggingface.co/docs/transformers/model_doc/plbart)** (from UCLA NLP) released with the paper [Unified Pre-training for Program Understanding and Generation](https://arxiv.org/abs/2103.06333) by Wasi Uddin Ahmad, Saikat Chakraborty, Baishakhi Ray, Kai-Wei Chang.
1. **[PoolFormer](https://huggingface.co/docs/transformers/model_doc/poolformer)** (from Sea AI Labs) released with the paper [MetaFormer is Actually What You Need for Vision](https://arxiv.org/abs/2111.11418) by Yu, Weihao and Luo, Mi and Zhou, Pan and Si, Chenyang and Zhou, Yichen and Wang, Xinchao and Feng, Jiashi and Yan, Shuicheng.
1. **[Pop2Piano](https://huggingface.co/docs/transformers/model_doc/pop2piano)** released with the paper [Pop2Piano : Pop Audio-based Piano Cover Generation](https://arxiv.org/abs/2211.00895) by Jongho Choi, Kyogu Lee.
1. **[ProphetNet](https://huggingface.co/docs/transformers/model_doc/prophetnet)** (from Microsoft Research) released with the paper [ProphetNet: Predicting Future N-gram for Sequence-to-Sequence Pre-training](https://arxiv.org/abs/2001.04063) by Yu Yan, Weizhen Qi, Yeyun Gong, Dayiheng Liu, Nan Duan, Jiusheng Chen, Ruofei Zhang and Ming Zhou.
1. **[PVT](https://huggingface.co/docs/transformers/model_doc/pvt)** (from Nanjing University, The University of Hong Kong etc.) released with the paper [Pyramid Vision Transformer: A Versatile Backbone for Dense Prediction without Convolutions](https://arxiv.org/pdf/2102.12122.pdf) by Wenhai Wang, Enze Xie, Xiang Li, Deng-Ping Fan, Kaitao Song, Ding Liang, Tong Lu, Ping Luo, Ling Shao.
1. **[QDQBert](https://huggingface.co/docs/transformers/model_doc/qdqbert)** (from NVIDIA) released with the paper [Integer Quantization for Deep Learning Inference: Principles and Empirical Evaluation](https://arxiv.org/abs/2004.09602) by Hao Wu, Patrick Judd, Xiaojie Zhang, Mikhail Isaev and Paulius Micikevicius.
1. **[RAG](https://huggingface.co/docs/transformers/model_doc/rag)** (from Facebook) released with the paper [Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks](https://arxiv.org/abs/2005.11401) by Patrick Lewis, Ethan Perez, Aleksandara Piktus, Fabio Petroni, Vladimir Karpukhin, Naman Goyal, Heinrich Küttler, Mike Lewis, Wen-tau Yih, Tim Rocktäschel, Sebastian Riedel, Douwe Kiela.
1. **[REALM](https://huggingface.co/docs/transformers/model_doc/realm.html)** (from Google Research) released with the paper [REALM: Retrieval-Augmented Language Model Pre-Training](https://arxiv.org/abs/2002.08909) by Kelvin Guu, Kenton Lee, Zora Tung, Panupong Pasupat and Ming-Wei Chang.
1. **[Reformer](https://huggingface.co/docs/transformers/model_doc/reformer)** (from Google Research) released with the paper [Reformer: The Efficient Transformer](https://arxiv.org/abs/2001.04451) by Nikita Kitaev, Łukasz Kaiser, Anselm Levskaya.
1. **[RegNet](https://huggingface.co/docs/transformers/model_doc/regnet)** (from META Research) released with the paper [Designing Network Design Space](https://arxiv.org/abs/2003.13678) by Ilija Radosavovic, Raj Prateek Kosaraju, Ross Girshick, Kaiming He, Piotr Dollár.
1. **[RemBERT](https://huggingface.co/docs/transformers/model_doc/rembert)** (from Google Research) released with the paper [Rethinking embedding coupling in pre-trained language models](https://arxiv.org/pdf/2010.12821.pdf) by Hyung Won Chung, Thibault Févry, Henry Tsai, M. Johnson, Sebastian Ruder.
1. **[ResNet](https://huggingface.co/docs/transformers/model_doc/resnet)** (from Microsoft Research) released with the paper [Deep Residual Learning for Image Recognition](https://arxiv.org/abs/1512.03385) by Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun.
1. **[RoBERTa](https://huggingface.co/docs/transformers/model_doc/roberta)** (from Facebook), released together with the paper a [Robustly Optimized BERT Pretraining Approach](https://arxiv.org/abs/1907.11692) by Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Mandar Joshi, Danqi Chen, Omer Levy, Mike Lewis, Luke Zettlemoyer, Veselin Stoyanov.
1. **[RoBERTa-PreLayerNorm](https://huggingface.co/docs/transformers/model_doc/roberta-prelayernorm)** (from Facebook) released with the paper [fairseq: A Fast, Extensible Toolkit for Sequence Modeling](https://arxiv.org/abs/1904.01038) by Myle Ott, Sergey Edunov, Alexei Baevski, Angela Fan, Sam Gross, Nathan Ng, David Grangier, Michael Auli.
1. **[RoCBert](https://huggingface.co/docs/transformers/model_doc/roc_bert)** (from WeChatAI) released with the paper [RoCBert: Robust Chinese Bert with Multimodal Contrastive Pretraining](https://aclanthology.org/2022.acl-long.65.pdf) by HuiSu, WeiweiShi, XiaoyuShen, XiaoZhou, TuoJi, JiaruiFang, JieZhou.
1. **[RoFormer](https://huggingface.co/docs/transformers/model_doc/roformer)** (from ZhuiyiTechnology), released together with the paper a [RoFormer: Enhanced Transformer with Rotary Position Embedding](https://arxiv.org/pdf/2104.09864v1.pdf) by Jianlin Su and Yu Lu and Shengfeng Pan and Bo Wen and Yunfeng Liu.
1. **[RWKV](https://huggingface.co/docs/transformers/model_doc/rwkv)** (from Bo Peng) released with the paper [this repo](https://github.com/BlinkDL/RWKV-LM) by Bo Peng.
1. **[SeamlessM4T](https://huggingface.co/docs/transformers/model_doc/seamless_m4t)** (from Meta AI) released with the paper [SeamlessM4T — Massively Multilingual & Multimodal Machine Translation](https://dl.fbaipublicfiles.com/seamless/seamless_m4t_paper.pdf) by the Seamless Communication team.
1. **[SeamlessM4Tv2](https://huggingface.co/docs/transformers/model_doc/seamless_m4t_v2)** (from Meta AI) released with the paper [Seamless: Multilingual Expressive and Streaming Speech Translation](https://ai.meta.com/research/publications/seamless-multilingual-expressive-and-streaming-speech-translation/) by the Seamless Communication team.
1. **[SegFormer](https://huggingface.co/docs/transformers/model_doc/segformer)** (from NVIDIA) released with the paper [SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers](https://arxiv.org/abs/2105.15203) by Enze Xie, Wenhai Wang, Zhiding Yu, Anima Anandkumar, Jose M. Alvarez, Ping Luo.
1. **[Segment Anything](https://huggingface.co/docs/transformers/model_doc/sam)** (from Meta AI) released with the paper [Segment Anything](https://arxiv.org/pdf/2304.02643v1.pdf) by Alexander Kirillov, Eric Mintun, Nikhila Ravi, Hanzi Mao, Chloe Rolland, Laura Gustafson, Tete Xiao, Spencer Whitehead, Alex Berg, Wan-Yen Lo, Piotr Dollar, Ross Girshick.
1. **[SEW](https://huggingface.co/docs/transformers/model_doc/sew)** (from ASAPP) released with the paper [Performance-Efficiency Trade-offs in Unsupervised Pre-training for Speech Recognition](https://arxiv.org/abs/2109.06870) by Felix Wu, Kwangyoun Kim, Jing Pan, Kyu Han, Kilian Q. Weinberger, Yoav Artzi.
1. **[SEW-D](https://huggingface.co/docs/transformers/model_doc/sew_d)** (from ASAPP) released with the paper [Performance-Efficiency Trade-offs in Unsupervised Pre-training for Speech Recognition](https://arxiv.org/abs/2109.06870) by Felix Wu, Kwangyoun Kim, Jing Pan, Kyu Han, Kilian Q. Weinberger, Yoav Artzi.
1. **[SpeechT5](https://huggingface.co/docs/transformers/model_doc/speecht5)** (from Microsoft Research) released with the paper [SpeechT5: Unified-Modal Encoder-Decoder Pre-Training for Spoken Language Processing](https://arxiv.org/abs/2110.07205) by Junyi Ao, Rui Wang, Long Zhou, Chengyi Wang, Shuo Ren, Yu Wu, Shujie Liu, Tom Ko, Qing Li, Yu Zhang, Zhihua Wei, Yao Qian, Jinyu Li, Furu Wei.
1. **[SpeechToTextTransformer](https://huggingface.co/docs/transformers/model_doc/speech_to_text)** (from Facebook), released together with the paper [fairseq S2T: Fast Speech-to-Text Modeling with fairseq](https://arxiv.org/abs/2010.05171) by Changhan Wang, Yun Tang, Xutai Ma, Anne Wu, Dmytro Okhonko, Juan Pino.
1. **[SpeechToTextTransformer2](https://huggingface.co/docs/transformers/model_doc/speech_to_text_2)** (from Facebook) released with the paper [Large-Scale Self- and Semi-Supervised Learning for Speech Translation](https://arxiv.org/abs/2104.06678) by Changhan Wang, Anne Wu, Juan Pino, Alexei Baevski, Michael Auli, Alexis Conneau.
1. **[Splinter](https://huggingface.co/docs/transformers/model_doc/splinter)** (from Tel Aviv University) released with the paper [Few-Shot Question Answering by Pretraining Span Selection](https://arxiv.org/abs/2101.00438) by Ori Ram, Yuval Kirstain, Jonathan Berant, Amir Globerson, Omer Levy.
1. **[SqueezeBERT](https://huggingface.co/docs/transformers/model_doc/squeezebert)** (from Berkeley) released with the paper [SqueezeBERT: What can computer vision teach NLP about efficient neural networks?](https://arxiv.org/abs/2006.11316) by Forrest N. Iandola, Albert E. Shaw, Ravi Krishna, and Kurt W. Keutzer.
1. **[SwiftFormer](https://huggingface.co/docs/transformers/model_doc/swiftformer)** (from MBZUAI) released with the paper [SwiftFormer: Efficient Additive Attention for Transformer-based Real-time Mobile Vision Applications](https://arxiv.org/abs/2303.15446) by Abdelrahman Shaker, Muhammad Maaz, Hanoona Rasheed, Salman Khan, Ming-Hsuan Yang, Fahad Shahbaz Khan.
1. **[Swin Transformer](https://huggingface.co/docs/transformers/model_doc/swin)** (from Microsoft) released with the paper [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://arxiv.org/abs/2103.14030) by Ze Liu, Yutong Lin, Yue Cao, Han Hu, Yixuan Wei, Zheng Zhang, Stephen Lin, Baining Guo.
1. **[Swin Transformer V2](https://huggingface.co/docs/transformers/model_doc/swinv2)** (from Microsoft) released with the paper [Swin Transformer V2: Scaling Up Capacity and Resolution](https://arxiv.org/abs/2111.09883) by Ze Liu, Han Hu, Yutong Lin, Zhuliang Yao, Zhenda Xie, Yixuan Wei, Jia Ning, Yue Cao, Zheng Zhang, Li Dong, Furu Wei, Baining Guo.
1. **[Swin2SR](https://huggingface.co/docs/transformers/model_doc/swin2sr)** (from University of Würzburg) released with the paper [Swin2SR: SwinV2 Transformer for Compressed Image Super-Resolution and Restoration](https://arxiv.org/abs/2209.11345) by Marcos V. Conde, Ui-Jin Choi, Maxime Burchi, Radu Timofte.
1. **[SwitchTransformers](https://huggingface.co/docs/transformers/model_doc/switch_transformers)** (from Google) released with the paper [Switch Transformers: Scaling to Trillion Parameter Models with Simple and Efficient Sparsity](https://arxiv.org/abs/2101.03961) by William Fedus, Barret Zoph, Noam Shazeer.
1. **[T5](https://huggingface.co/docs/transformers/model_doc/t5)** (from Google AI) released with the paper [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://arxiv.org/abs/1910.10683) by Colin Raffel and Noam Shazeer and Adam Roberts and Katherine Lee and Sharan Narang and Michael Matena and Yanqi Zhou and Wei Li and Peter J. Liu.
1. **[T5v1.1](https://huggingface.co/docs/transformers/model_doc/t5v1.1)** (from Google AI) released with the paper [google-research/text-to-text-transfer-transformer](https://github.com/google-research/text-to-text-transfer-transformer/blob/main/released_checkpoints.md#t511) by Colin Raffel and Noam Shazeer and Adam Roberts and Katherine Lee and Sharan Narang and Michael Matena and Yanqi Zhou and Wei Li and Peter J. Liu.
1. **[Table Transformer](https://huggingface.co/docs/transformers/model_doc/table-transformer)** (from Microsoft Research) released with the paper [PubTables-1M: Towards Comprehensive Table Extraction From Unstructured Documents](https://arxiv.org/abs/2110.00061) by Brandon Smock, Rohith Pesala, Robin Abraham.
1. **[TAPAS](https://huggingface.co/docs/transformers/model_doc/tapas)** (from Google AI) released with the paper [TAPAS: Weakly Supervised Table Parsing via Pre-training](https://arxiv.org/abs/2004.02349) by Jonathan Herzig, Paweł Krzysztof Nowak, Thomas Müller, Francesco Piccinno and Julian Martin Eisenschlos.
1. **[TAPEX](https://huggingface.co/docs/transformers/model_doc/tapex)** (from Microsoft Research) released with the paper [TAPEX: Table Pre-training via Learning a Neural SQL Executor](https://arxiv.org/abs/2107.07653) by Qian Liu, Bei Chen, Jiaqi Guo, Morteza Ziyadi, Zeqi Lin, Weizhu Chen, Jian-Guang Lou.
1. **[Time Series Transformer](https://huggingface.co/docs/transformers/model_doc/time_series_transformer)** (from HuggingFace).
1. **[TimeSformer](https://huggingface.co/docs/transformers/model_doc/timesformer)** (from Facebook) released with the paper [Is Space-Time Attention All You Need for Video Understanding?](https://arxiv.org/abs/2102.05095) by Gedas Bertasius, Heng Wang, Lorenzo Torresani.
1. **[Trajectory Transformer](https://huggingface.co/docs/transformers/model_doc/trajectory_transformers)** (from the University of California at Berkeley) released with the paper [Offline Reinforcement Learning as One Big Sequence Modeling Problem](https://arxiv.org/abs/2106.02039) by Michael Janner, Qiyang Li, Sergey Levine
1. **[Transformer-XL](https://huggingface.co/docs/transformers/model_doc/transfo-xl)** (from Google/CMU) released with the paper [Transformer-XL: Attentive Language Models Beyond a Fixed-Length Context](https://arxiv.org/abs/1901.02860) by Zihang Dai*, Zhilin Yang*, Yiming Yang, Jaime Carbonell, Quoc V. Le, Ruslan Salakhutdinov.
1. **[TrOCR](https://huggingface.co/docs/transformers/model_doc/trocr)** (from Microsoft) released with the paper [TrOCR: Transformer-based Optical Character Recognition with Pre-trained Models](https://arxiv.org/abs/2109.10282) by Minghao Li, Tengchao Lv, Lei Cui, Yijuan Lu, Dinei Florencio, Cha Zhang, Zhoujun Li, Furu Wei.
1. **[TVLT](https://huggingface.co/docs/transformers/model_doc/tvlt)** (from UNC Chapel Hill) released with the paper [TVLT: Textless Vision-Language Transformer](https://arxiv.org/abs/2209.14156) by Zineng Tang, Jaemin Cho, Yixin Nie, Mohit Bansal.
1. **[TVP](https://huggingface.co/docs/transformers/model_doc/tvp)** (from Intel) released with the paper [Text-Visual Prompting for Efficient 2D Temporal Video Grounding](https://arxiv.org/abs/2303.04995) by Yimeng Zhang, Xin Chen, Jinghan Jia, Sijia Liu, Ke Ding.
1. **[UL2](https://huggingface.co/docs/transformers/model_doc/ul2)** (from Google Research) released with the paper [Unifying Language Learning Paradigms](https://arxiv.org/abs/2205.05131v1) by Yi Tay, Mostafa Dehghani, Vinh Q. Tran, Xavier Garcia, Dara Bahri, Tal Schuster, Huaixiu Steven Zheng, Neil Houlsby, Donald Metzler
1. **[UMT5](https://huggingface.co/docs/transformers/model_doc/umt5)** (from Google Research) released with the paper [UniMax: Fairer and More Effective Language Sampling for Large-Scale Multilingual Pretraining](https://openreview.net/forum?id=kXwdL1cWOAi) by Hyung Won Chung, Xavier Garcia, Adam Roberts, Yi Tay, Orhan Firat, Sharan Narang, Noah Constant.
1. **[UniSpeech](https://huggingface.co/docs/transformers/model_doc/unispeech)** (from Microsoft Research) released with the paper [UniSpeech: Unified Speech Representation Learning with Labeled and Unlabeled Data](https://arxiv.org/abs/2101.07597) by Chengyi Wang, Yu Wu, Yao Qian, Kenichi Kumatani, Shujie Liu, Furu Wei, Michael Zeng, Xuedong Huang.
1. **[UniSpeechSat](https://huggingface.co/docs/transformers/model_doc/unispeech-sat)** (from Microsoft Research) released with the paper [UNISPEECH-SAT: UNIVERSAL SPEECH REPRESENTATION LEARNING WITH SPEAKER AWARE PRE-TRAINING](https://arxiv.org/abs/2110.05752) by Sanyuan Chen, Yu Wu, Chengyi Wang, Zhengyang Chen, Zhuo Chen, Shujie Liu, Jian Wu, Yao Qian, Furu Wei, Jinyu Li, Xiangzhan Yu.
1. **[UnivNet](https://huggingface.co/docs/transformers/model_doc/univnet)** (from Kakao Corporation) released with the paper [UnivNet: A Neural Vocoder with Multi-Resolution Spectrogram Discriminators for High-Fidelity Waveform Generation](https://arxiv.org/abs/2106.07889) by Won Jang, Dan Lim, Jaesam Yoon, Bongwan Kim, and Juntae Kim.
1. **[UPerNet](https://huggingface.co/docs/transformers/model_doc/upernet)** (from Peking University) released with the paper [Unified Perceptual Parsing for Scene Understanding](https://arxiv.org/abs/1807.10221) by Tete Xiao, Yingcheng Liu, Bolei Zhou, Yuning Jiang, Jian Sun.
1. **[VAN](https://huggingface.co/docs/transformers/model_doc/van)** (from Tsinghua University and Nankai University) released with the paper [Visual Attention Network](https://arxiv.org/pdf/2202.09741.pdf) by Meng-Hao Guo, Cheng-Ze Lu, Zheng-Ning Liu, Ming-Ming Cheng, Shi-Min Hu.
1. **[VideoMAE](https://huggingface.co/docs/transformers/model_doc/videomae)** (from Multimedia Computing Group, Nanjing University) released with the paper [VideoMAE: Masked Autoencoders are Data-Efficient Learners for Self-Supervised Video Pre-Training](https://arxiv.org/abs/2203.12602) by Zhan Tong, Yibing Song, Jue Wang, Limin Wang.
1. **[ViLT](https://huggingface.co/docs/transformers/model_doc/vilt)** (from NAVER AI Lab/Kakao Enterprise/Kakao Brain) released with the paper [ViLT: Vision-and-Language Transformer Without Convolution or Region Supervision](https://arxiv.org/abs/2102.03334) by Wonjae Kim, Bokyung Son, Ildoo Kim.
1. **[VipLlava](https://huggingface.co/docs/transformers/model_doc/vipllava)** (from University of Wisconsin–Madison) released with the paper [Making Large Multimodal Models Understand Arbitrary Visual Prompts](https://arxiv.org/abs/2312.00784) by Mu Cai, Haotian Liu, Siva Karthik Mustikovela, Gregory P. Meyer, Yuning Chai, Dennis Park, Yong Jae Lee.
1. **[Vision Transformer (ViT)](https://huggingface.co/docs/transformers/model_doc/vit)** (from Google AI) released with the paper [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://arxiv.org/abs/2010.11929) by Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, Jakob Uszkoreit, Neil Houlsby.
1. **[VisualBERT](https://huggingface.co/docs/transformers/model_doc/visual_bert)** (from UCLA NLP) released with the paper [VisualBERT: A Simple and Performant Baseline for Vision and Language](https://arxiv.org/pdf/1908.03557) by Liunian Harold Li, Mark Yatskar, Da Yin, Cho-Jui Hsieh, Kai-Wei Chang.
1. **[ViT Hybrid](https://huggingface.co/docs/transformers/model_doc/vit_hybrid)** (from Google AI) released with the paper [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://arxiv.org/abs/2010.11929) by Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, Jakob Uszkoreit, Neil Houlsby.
1. **[VitDet](https://huggingface.co/docs/transformers/model_doc/vitdet)** (from Meta AI) released with the paper [Exploring Plain Vision Transformer Backbones for Object Detection](https://arxiv.org/abs/2203.16527) by Yanghao Li, Hanzi Mao, Ross Girshick, Kaiming He.
1. **[ViTMAE](https://huggingface.co/docs/transformers/model_doc/vit_mae)** (from Meta AI) released with the paper [Masked Autoencoders Are Scalable Vision Learners](https://arxiv.org/abs/2111.06377) by Kaiming He, Xinlei Chen, Saining Xie, Yanghao Li, Piotr Dollár, Ross Girshick.
1. **[ViTMatte](https://huggingface.co/docs/transformers/model_doc/vitmatte)** (from HUST-VL) released with the paper [ViTMatte: Boosting Image Matting with Pretrained Plain Vision Transformers](https://arxiv.org/abs/2305.15272) by Jingfeng Yao, Xinggang Wang, Shusheng Yang, Baoyuan Wang.
1. **[ViTMSN](https://huggingface.co/docs/transformers/model_doc/vit_msn)** (from Meta AI) released with the paper [Masked Siamese Networks for Label-Efficient Learning](https://arxiv.org/abs/2204.07141) by Mahmoud Assran, Mathilde Caron, Ishan Misra, Piotr Bojanowski, Florian Bordes, Pascal Vincent, Armand Joulin, Michael Rabbat, Nicolas Ballas.
1. **[VITS](https://huggingface.co/docs/transformers/model_doc/vits)** (from Kakao Enterprise) released with the paper [Conditional Variational Autoencoder with Adversarial Learning for End-to-End Text-to-Speech](https://arxiv.org/abs/2106.06103) by Jaehyeon Kim, Jungil Kong, Juhee Son.
1. **[ViViT](https://huggingface.co/docs/transformers/model_doc/vivit)** (from Google Research) released with the paper [ViViT: A Video Vision Transformer](https://arxiv.org/abs/2103.15691) by Anurag Arnab, Mostafa Dehghani, Georg Heigold, Chen Sun, Mario Lučić, Cordelia Schmid.
1. **[Wav2Vec2](https://huggingface.co/docs/transformers/model_doc/wav2vec2)** (from Facebook AI) released with the paper [wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations](https://arxiv.org/abs/2006.11477) by Alexei Baevski, Henry Zhou, Abdelrahman Mohamed, Michael Auli.
1. **[Wav2Vec2-Conformer](https://huggingface.co/docs/transformers/model_doc/wav2vec2-conformer)** (from Facebook AI) released with the paper [FAIRSEQ S2T: Fast Speech-to-Text Modeling with FAIRSEQ](https://arxiv.org/abs/2010.05171) by Changhan Wang, Yun Tang, Xutai Ma, Anne Wu, Sravya Popuri, Dmytro Okhonko, Juan Pino.
1. **[Wav2Vec2Phoneme](https://huggingface.co/docs/transformers/model_doc/wav2vec2_phoneme)** (from Facebook AI) released with the paper [Simple and Effective Zero-shot Cross-lingual Phoneme Recognition](https://arxiv.org/abs/2109.11680) by Qiantong Xu, Alexei Baevski, Michael Auli.
1. **[WavLM](https://huggingface.co/docs/transformers/model_doc/wavlm)** (from Microsoft Research) released with the paper [WavLM: Large-Scale Self-Supervised Pre-Training for Full Stack Speech Processing](https://arxiv.org/abs/2110.13900) by Sanyuan Chen, Chengyi Wang, Zhengyang Chen, Yu Wu, Shujie Liu, Zhuo Chen, Jinyu Li, Naoyuki Kanda, Takuya Yoshioka, Xiong Xiao, Jian Wu, Long Zhou, Shuo Ren, Yanmin Qian, Yao Qian, Jian Wu, Michael Zeng, Furu Wei.
1. **[Whisper](https://huggingface.co/docs/transformers/model_doc/whisper)** (from OpenAI) released with the paper [Robust Speech Recognition via Large-Scale Weak Supervision](https://cdn.openai.com/papers/whisper.pdf) by Alec Radford, Jong Wook Kim, Tao Xu, Greg Brockman, Christine McLeavey, Ilya Sutskever.
1. **[X-CLIP](https://huggingface.co/docs/transformers/model_doc/xclip)** (from Microsoft Research) released with the paper [Expanding Language-Image Pretrained Models for General Video Recognition](https://arxiv.org/abs/2208.02816) by Bolin Ni, Houwen Peng, Minghao Chen, Songyang Zhang, Gaofeng Meng, Jianlong Fu, Shiming Xiang, Haibin Ling.
1. **[X-MOD](https://huggingface.co/docs/transformers/model_doc/xmod)** (from Meta AI) released with the paper [Lifting the Curse of Multilinguality by Pre-training Modular Transformers](http://dx.doi.org/10.18653/v1/2022.naacl-main.255) by Jonas Pfeiffer, Naman Goyal, Xi Lin, Xian Li, James Cross, Sebastian Riedel, Mikel Artetxe.
1. **[XGLM](https://huggingface.co/docs/transformers/model_doc/xglm)** (From Facebook AI) released with the paper [Few-shot Learning with Multilingual Language Models](https://arxiv.org/abs/2112.10668) by Xi Victoria Lin, Todor Mihaylov, Mikel Artetxe, Tianlu Wang, Shuohui Chen, Daniel Simig, Myle Ott, Naman Goyal, Shruti Bhosale, Jingfei Du, Ramakanth Pasunuru, Sam Shleifer, Punit Singh Koura, Vishrav Chaudhary, Brian O'Horo, Jeff Wang, Luke Zettlemoyer, Zornitsa Kozareva, Mona Diab, Veselin Stoyanov, Xian Li.
1. **[XLM](https://huggingface.co/docs/transformers/model_doc/xlm)** (from Facebook) released together with the paper [Cross-lingual Language Model Pretraining](https://arxiv.org/abs/1901.07291) by Guillaume Lample and Alexis Conneau.
1. **[XLM-ProphetNet](https://huggingface.co/docs/transformers/model_doc/xlm-prophetnet)** (from Microsoft Research) released with the paper [ProphetNet: Predicting Future N-gram for Sequence-to-Sequence Pre-training](https://arxiv.org/abs/2001.04063) by Yu Yan, Weizhen Qi, Yeyun Gong, Dayiheng Liu, Nan Duan, Jiusheng Chen, Ruofei Zhang and Ming Zhou.
1. **[XLM-RoBERTa](https://huggingface.co/docs/transformers/model_doc/xlm-roberta)** (from Facebook AI), released together with the paper [Unsupervised Cross-lingual Representation Learning at Scale](https://arxiv.org/abs/1911.02116) by Alexis Conneau*, Kartikay Khandelwal*, Naman Goyal, Vishrav Chaudhary, Guillaume Wenzek, Francisco Guzmán, Edouard Grave, Myle Ott, Luke Zettlemoyer and Veselin Stoyanov.
1. **[XLM-RoBERTa-XL](https://huggingface.co/docs/transformers/model_doc/xlm-roberta-xl)** (from Facebook AI) released with the paper [Larger-Scale Transformers for Multilingual Masked Language Modeling](https://arxiv.org/abs/2105.00572) by Naman Goyal, Jingfei Du, Myle Ott, Giri Anantharaman, Alexis Conneau.
1. **[XLM-V](https://huggingface.co/docs/transformers/model_doc/xlm-v)** (from Meta AI) released with the paper [XLM-V: Overcoming the Vocabulary Bottleneck in Multilingual Masked Language Models](https://arxiv.org/abs/2301.10472) by Davis Liang, Hila Gonen, Yuning Mao, Rui Hou, Naman Goyal, Marjan Ghazvininejad, Luke Zettlemoyer, Madian Khabsa.
1. **[XLNet](https://huggingface.co/docs/transformers/model_doc/xlnet)** (from Google/CMU) released with the paper [XLNet: Generalized Autoregressive Pretraining for Language Understanding](https://arxiv.org/abs/1906.08237) by Zhilin Yang*, Zihang Dai*, Yiming Yang, Jaime Carbonell, Ruslan Salakhutdinov, Quoc V. Le.
1. **[XLS-R](https://huggingface.co/docs/transformers/model_doc/xls_r)** (from Facebook AI) released with the paper [XLS-R: Self-supervised Cross-lingual Speech Representation Learning at Scale](https://arxiv.org/abs/2111.09296) by Arun Babu, Changhan Wang, Andros Tjandra, Kushal Lakhotia, Qiantong Xu, Naman Goyal, Kritika Singh, Patrick von Platen, Yatharth Saraf, Juan Pino, Alexei Baevski, Alexis Conneau, Michael Auli.
1. **[XLSR-Wav2Vec2](https://huggingface.co/docs/transformers/model_doc/xlsr_wav2vec2)** (from Facebook AI) released with the paper [Unsupervised Cross-Lingual Representation Learning For Speech Recognition](https://arxiv.org/abs/2006.13979) by Alexis Conneau, Alexei Baevski, Ronan Collobert, Abdelrahman Mohamed, Michael Auli.
1. **[YOLOS](https://huggingface.co/docs/transformers/model_doc/yolos)** (from Huazhong University of Science & Technology) released with the paper [You Only Look at One Sequence: Rethinking Transformer in Vision through Object Detection](https://arxiv.org/abs/2106.00666) by Yuxin Fang, Bencheng Liao, Xinggang Wang, Jiemin Fang, Jiyang Qi, Rui Wu, Jianwei Niu, Wenyu Liu.
1. **[YOSO](https://huggingface.co/docs/transformers/model_doc/yoso)** (from the University of Wisconsin - Madison) released with the paper [You Only Sample (Almost) Once: Linear Cost Self-Attention Via Bernoulli Sampling](https://arxiv.org/abs/2111.09714) by Zhanpeng Zeng, Yunyang Xiong, Sathya N. Ravi, Shailesh Acharya, Glenn Fung, Vikas Singh.
1. 想要貢獻新的模型？我們這裡有一份**詳細指引和模板**來引導你加入新的模型。你可以在 [`templates`](./templates) 目錄中找到它們。記得查看[貢獻指引](./CONTRIBUTING.md)並在開始寫 PR 前聯繫維護人員或開一個新的 issue 來獲得 feedbacks。
要檢查某個模型是否已有 Flax、PyTorch 或 TensorFlow 的實作，或其是否在🤗 Tokenizers 函式庫中有對應的 tokenizer，敬請參閱[此表](https://huggingface.co/docs/transformers/index#supported-frameworks)。
這些實作均已於多個資料集測試（請參閱範例腳本）並應與原版實作表現相當。你可以在範例文件的[此節](https://huggingface.co/docs/transformers/examples)中了解實作的細節。
## 了解更多
| 章節 | 描述 |
|-|-|
| [文件](https://huggingface.co/transformers/) | 完整的 API 文件和教學 |
| [任務概覽](https://huggingface.co/docs/transformers/task_summary) | 🤗 Transformers 支援的任務 |
| [預處理教學](https://huggingface.co/docs/transformers/preprocessing) | 使用 `Tokenizer` 來為模型準備資料 |
| [訓練和微調](https://huggingface.co/docs/transformers/training) | 使用 PyTorch/TensorFlow 的內建的訓練方式或於 `Trainer` API 中使用 🤗 Transformers 提供的模型 |
| [快速上手：微調和範例腳本](https://github.com/huggingface/transformers/tree/main/examples) | 為各種任務提供的範例腳本 |
| [模型分享和上傳](https://huggingface.co/docs/transformers/model_sharing) | 上傳並與社群分享你微調的模型 |
| [遷移](https://huggingface.co/docs/transformers/migration) | 從 `pytorch-transformers` 或 `pytorch-pretrained-bert` 遷移到 🤗 Transformers |
## 引用
我們已將此函式庫的[論文](https://www.aclweb.org/anthology/2020.emnlp-demos.6/)正式發表。如果你使用了 🤗 Transformers 函式庫，可以引用：
```bibtex
@inproceedings{wolf-etal-2020-transformers,
title = "Transformers: State-of-the-Art Natural Language Processing",
author = "Thomas Wolf and Lysandre Debut and Victor Sanh and Julien Chaumond and Clement Delangue and Anthony Moi and Pierric Cistac and Tim Rault and Rémi Louf and Morgan Funtowicz and Joe Davison and Sam Shleifer and Patrick von Platen and Clara Ma and Yacine Jernite and Julien Plu and Canwen Xu and Teven Le Scao and Sylvain Gugger and Mariama Drame and Quentin Lhoest and Alexander M. Rush",
booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations",
month = oct,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/2020.emnlp-demos.6",
pages = "38--45"
}
```
| huggingface/transformers/blob/main/README_zh-hant.md |
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# LayoutLM
<a id='Overview'></a>
## Overview
The LayoutLM model was proposed in the paper [LayoutLM: Pre-training of Text and Layout for Document Image
Understanding](https://arxiv.org/abs/1912.13318) by Yiheng Xu, Minghao Li, Lei Cui, Shaohan Huang, Furu Wei, and
Ming Zhou. It's a simple but effective pretraining method of text and layout for document image understanding and
information extraction tasks, such as form understanding and receipt understanding. It obtains state-of-the-art results
on several downstream tasks:
- form understanding: the [FUNSD](https://guillaumejaume.github.io/FUNSD/) dataset (a collection of 199 annotated
forms comprising more than 30,000 words).
- receipt understanding: the [SROIE](https://rrc.cvc.uab.es/?ch=13) dataset (a collection of 626 receipts for
training and 347 receipts for testing).
- document image classification: the [RVL-CDIP](https://www.cs.cmu.edu/~aharley/rvl-cdip/) dataset (a collection of
400,000 images belonging to one of 16 classes).
The abstract from the paper is the following:
*Pre-training techniques have been verified successfully in a variety of NLP tasks in recent years. Despite the
widespread use of pretraining models for NLP applications, they almost exclusively focus on text-level manipulation,
while neglecting layout and style information that is vital for document image understanding. In this paper, we propose
the LayoutLM to jointly model interactions between text and layout information across scanned document images, which is
beneficial for a great number of real-world document image understanding tasks such as information extraction from
scanned documents. Furthermore, we also leverage image features to incorporate words' visual information into LayoutLM.
To the best of our knowledge, this is the first time that text and layout are jointly learned in a single framework for
document-level pretraining. It achieves new state-of-the-art results in several downstream tasks, including form
understanding (from 70.72 to 79.27), receipt understanding (from 94.02 to 95.24) and document image classification
(from 93.07 to 94.42).*
## Usage tips
- In addition to *input_ids*, [`~transformers.LayoutLMModel.forward`] also expects the input `bbox`, which are
the bounding boxes (i.e. 2D-positions) of the input tokens. These can be obtained using an external OCR engine such
as Google's [Tesseract](https://github.com/tesseract-ocr/tesseract) (there's a [Python wrapper](https://pypi.org/project/pytesseract/) available). Each bounding box should be in (x0, y0, x1, y1) format, where
(x0, y0) corresponds to the position of the upper left corner in the bounding box, and (x1, y1) represents the
position of the lower right corner. Note that one first needs to normalize the bounding boxes to be on a 0-1000
scale. To normalize, you can use the following function:
```python
def normalize_bbox(bbox, width, height):
return [
int(1000 * (bbox[0] / width)),
int(1000 * (bbox[1] / height)),
int(1000 * (bbox[2] / width)),
int(1000 * (bbox[3] / height)),
]
```
Here, `width` and `height` correspond to the width and height of the original document in which the token
occurs. Those can be obtained using the Python Image Library (PIL) library for example, as follows:
```python
from PIL import Image
# Document can be a png, jpg, etc. PDFs must be converted to images.
image = Image.open(name_of_your_document).convert("RGB")
width, height = image.size
```
## Resources
A list of official Hugging Face and community (indicated by 🌎) resources to help you get started with LayoutLM. If you're interested in submitting a resource to be included here, please feel free to open a Pull Request and we'll review it! The resource should ideally demonstrate something new instead of duplicating an existing resource.
<PipelineTag pipeline="document-question-answering" />
- A blog post on [fine-tuning
LayoutLM for document-understanding using Keras & Hugging Face
Transformers](https://www.philschmid.de/fine-tuning-layoutlm-keras).
- A blog post on how to [fine-tune LayoutLM for document-understanding using only Hugging Face Transformers](https://www.philschmid.de/fine-tuning-layoutlm).
- A notebook on how to [fine-tune LayoutLM on the FUNSD dataset with image embeddings](https://colab.research.google.com/github/NielsRogge/Transformers-Tutorials/blob/master/LayoutLM/Add_image_embeddings_to_LayoutLM.ipynb).
- See also: [Document question answering task guide](../tasks/document_question_answering)
<PipelineTag pipeline="text-classification" />
- A notebook on how to [fine-tune LayoutLM for sequence classification on the RVL-CDIP dataset](https://colab.research.google.com/github/NielsRogge/Transformers-Tutorials/blob/master/LayoutLM/Fine_tuning_LayoutLMForSequenceClassification_on_RVL_CDIP.ipynb).
- [Text classification task guide](../tasks/sequence_classification)
<PipelineTag pipeline="token-classification" />
- A notebook on how to [ fine-tune LayoutLM for token classification on the FUNSD dataset](https://github.com/NielsRogge/Transformers-Tutorials/blob/master/LayoutLM/Fine_tuning_LayoutLMForTokenClassification_on_FUNSD.ipynb).
- [Token classification task guide](../tasks/token_classification)
**Other resources**
- [Masked language modeling task guide](../tasks/masked_language_modeling)
🚀 Deploy
- A blog post on how to [Deploy LayoutLM with Hugging Face Inference Endpoints](https://www.philschmid.de/inference-endpoints-layoutlm).
## LayoutLMConfig
[[autodoc]] LayoutLMConfig
## LayoutLMTokenizer
[[autodoc]] LayoutLMTokenizer
## LayoutLMTokenizerFast
[[autodoc]] LayoutLMTokenizerFast
<frameworkcontent>
<pt>
## LayoutLMModel
[[autodoc]] LayoutLMModel
## LayoutLMForMaskedLM
[[autodoc]] LayoutLMForMaskedLM
## LayoutLMForSequenceClassification
[[autodoc]] LayoutLMForSequenceClassification
## LayoutLMForTokenClassification
[[autodoc]] LayoutLMForTokenClassification
## LayoutLMForQuestionAnswering
[[autodoc]] LayoutLMForQuestionAnswering
</pt>
<tf>
## TFLayoutLMModel
[[autodoc]] TFLayoutLMModel
## TFLayoutLMForMaskedLM
[[autodoc]] TFLayoutLMForMaskedLM
## TFLayoutLMForSequenceClassification
[[autodoc]] TFLayoutLMForSequenceClassification
## TFLayoutLMForTokenClassification
[[autodoc]] TFLayoutLMForTokenClassification
## TFLayoutLMForQuestionAnswering
[[autodoc]] TFLayoutLMForQuestionAnswering
</tf>
</frameworkcontent>
| huggingface/transformers/blob/main/docs/source/en/model_doc/layoutlm.md |
Wav2Vec2 Contrastive Loss PreTraining examples
The following example showcases how to pretrain a wav2vec2 model using the JAX/Flax backend.
Pretraining Wav2Vec2 is rather complex, so it is highly recommended to read the
[official paper](https://arxiv.org/abs/2006.11477).
JAX/Flax allows you to trace pure functions and compile them into efficient, fused accelerator code on both GPU and TPU.
Models written in JAX/Flax are **immutable** and updated in a purely functional
way which enables simple and efficient model parallelism.
`run_wav2vec2_pretrain_flax.py` is a lightweight example of how to download and preprocess a dataset from the 🤗 Datasets library or use your own files (jsonlines or csv), then pretrain the wav2vec2 architectures above on it.
For custom datasets in `jsonlines` format please see: [the Datasets documentation](https://huggingface.co/docs/datasets/loading_datasets#json-files) and you also will find examples of these below.
Let's start by creating a model repository to save the trained model and logs.
Here we call the model `"wav2vec2-base-robust"`, but you can change the model name as you like.
You can do this either directly on [huggingface.co](https://huggingface.co/new) (assuming that
you are logged in) or via the command line:
```
huggingface-cli repo create wav2vec2-base-robust
```
Next we clone the model repository to add the tokenizer and model files.
```
git clone https://huggingface.co/<your-username>/wav2vec2-base-robust
```
To ensure that all tensorboard traces will be uploaded correctly, we need to
track them. You can run the following command inside your model repo to do so.
```
cd wav2vec2-base-robust
git lfs track "*tfevents*"
```
Great, we have set up our model repository. During training, we will automatically
push the training logs and model weights to the repo.
Next, let's add a symbolic link to the `run_wav2vec2_pretrain_flax`.
```bash
export MODEL_DIR="./wav2vec2-base-robust"
ln -s ~/transformers/examples/research_projects/jax-projects/wav2vec2/run_wav2vec2_pretrain_flax.py ./
```
### Create the model configuration
Let's first create the model configuration and store it in the model repository.
Note that many training parameters can be set in the model configuration including
the configuration about the masking distribution (`mask_time_length`, `mask_time_prob`),
dropout (`attention_dropout`, ...), the trade-off between the contrastive loss and
the diversity loss, etc...
Mostly likely you will need to change these parameters depending on your use case.
Again, we highly recommend to read the [official paper](https://arxiv.org/abs/2006.11477)
to better understand which parameters can be set for pretraining.
For this example, we will be using a `"base"`-sized model of Wav2Vec2 with robust
layer norm and keep most of the default settings.
```python
model_dir="./wav2vec2-base-robust"
from transformers import Wav2Vec2Config
config = Wav2Vec2Config.from_pretrained(
"facebook/wav2vec2-base",
mask_time_length=10,
mask_time_prob=0.05,
diversity_loss_weight=0.1,
num_negatives=100,
do_stable_layer_norm=True,
feat_extract_norm="layer",
)
config.save_pretrained(model_dir)
```
### Create a feature extractor configuration
Before we can start the training, we need to define
a feature extractor that takes care of normalization, etc...
Here we can also re-use the feature extractor of [wav2vec2-base-960h](https://huggingface.co/facebook/wav2vec2-base) while making sure that padding is allowed.
```python
model_dir="./wav2vec2-base-robust"
from transformers import Wav2Vec2FeatureExtractor
config = Wav2Vec2FeatureExtractor.from_pretrained("facebook/wav2vec2-base", return_attention_mask=True)
config.save_pretrained(model_dir)
```
### Train the model
Finally, we can run the example script to train the model:
```bash
./run_wav2vec2_pretrain_flax.py \
--output_dir=${MODEL_DIR} \
--num_train_epochs="5" \
--per_device_train_batch_size="32" \
--per_device_eval_batch_size="32" \
--learning_rate="5e-4" \
--weight_decay="0.01" \
--warmup_steps="2000" \
--model_name_or_path=${MODEL_DIR} \
--dataset_name="librispeech_asr" \
--dataset_config_name="clean" \
--train_split_name="train.100" \
--preprocessing_num_workers="4" \
--max_duration_in_seconds="10.0" \
--adam_beta1="0.9" \
--adam_beta2="0.98" \
--pad_to_multiple_of="16384" \
--push_to_hub
```
Note that this script is not fully tested yet, so we cannot ensure that
the above script leads to satisfying results.
| huggingface/transformers/blob/main/examples/research_projects/jax-projects/wav2vec2/README.md |
!--Copyright 2022 The HuggingFace Team. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
the License. You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
specific language governing permissions and limitations under the License.
⚠️ Note that this file is in Markdown but contain specific syntax for our doc-builder (similar to MDX) that may not be
rendered properly in your Markdown viewer.
-->
# VideoMAE
## Overview
The VideoMAE model was proposed in [VideoMAE: Masked Autoencoders are Data-Efficient Learners for Self-Supervised Video Pre-Training](https://arxiv.org/abs/2203.12602) by Zhan Tong, Yibing Song, Jue Wang, Limin Wang.
VideoMAE extends masked auto encoders ([MAE](vit_mae)) to video, claiming state-of-the-art performance on several video classification benchmarks.
The abstract from the paper is the following:
*Pre-training video transformers on extra large-scale datasets is generally required to achieve premier performance on relatively small datasets. In this paper, we show that video masked autoencoders (VideoMAE) are data-efficient learners for self-supervised video pre-training (SSVP). We are inspired by the recent ImageMAE and propose customized video tube masking and reconstruction. These simple designs turn out to be effective for overcoming information leakage caused by the temporal correlation during video reconstruction. We obtain three important findings on SSVP: (1) An extremely high proportion of masking ratio (i.e., 90% to 95%) still yields favorable performance of VideoMAE. The temporally redundant video content enables higher masking ratio than that of images. (2) VideoMAE achieves impressive results on very small datasets (i.e., around 3k-4k videos) without using any extra data. This is partially ascribed to the challenging task of video reconstruction to enforce high-level structure learning. (3) VideoMAE shows that data quality is more important than data quantity for SSVP. Domain shift between pre-training and target datasets are important issues in SSVP. Notably, our VideoMAE with the vanilla ViT backbone can achieve 83.9% on Kinects-400, 75.3% on Something-Something V2, 90.8% on UCF101, and 61.1% on HMDB51 without using any extra data.*
<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/model_doc/videomae_architecture.jpeg"
alt="drawing" width="600"/>
<small> VideoMAE pre-training. Taken from the <a href="https://arxiv.org/abs/2203.12602">original paper</a>. </small>
This model was contributed by [nielsr](https://huggingface.co/nielsr).
The original code can be found [here](https://github.com/MCG-NJU/VideoMAE).
## Resources
A list of official Hugging Face and community (indicated by 🌎) resources to help you get started with VideoMAE. If
you're interested in submitting a resource to be included here, please feel free to open a Pull Request and we'll
review it! The resource should ideally demonstrate something new instead of duplicating an existing resource.
**Video classification**
- [A notebook](https://github.com/huggingface/notebooks/blob/main/examples/video_classification.ipynb) that shows how
to fine-tune a VideoMAE model on a custom dataset.
- [Video classification task guide](../tasks/video_classification)
- [A 🤗 Space](https://huggingface.co/spaces/sayakpaul/video-classification-ucf101-subset) showing how to perform inference with a video classification model.
## VideoMAEConfig
[[autodoc]] VideoMAEConfig
## VideoMAEFeatureExtractor
[[autodoc]] VideoMAEFeatureExtractor
- __call__
## VideoMAEImageProcessor
[[autodoc]] VideoMAEImageProcessor
- preprocess
## VideoMAEModel
[[autodoc]] VideoMAEModel
- forward
## VideoMAEForPreTraining
`VideoMAEForPreTraining` includes the decoder on top for self-supervised pre-training.
[[autodoc]] transformers.VideoMAEForPreTraining
- forward
## VideoMAEForVideoClassification
[[autodoc]] transformers.VideoMAEForVideoClassification
- forward
| huggingface/transformers/blob/main/docs/source/en/model_doc/videomae.md |
!--Copyright 2020 The HuggingFace Team. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
the License. You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
specific language governing permissions and limitations under the License.
⚠️ Note that this file is in Markdown but contain specific syntax for our doc-builder (similar to MDX) that may not be
rendered properly in your Markdown viewer.
-->
# DialoGPT
## Overview
DialoGPT was proposed in [DialoGPT: Large-Scale Generative Pre-training for Conversational Response Generation](https://arxiv.org/abs/1911.00536) by Yizhe Zhang, Siqi Sun, Michel Galley, Yen-Chun Chen, Chris Brockett, Xiang Gao,
Jianfeng Gao, Jingjing Liu, Bill Dolan. It's a GPT2 Model trained on 147M conversation-like exchanges extracted from
Reddit.
The abstract from the paper is the following:
*We present a large, tunable neural conversational response generation model, DialoGPT (dialogue generative pre-trained
transformer). Trained on 147M conversation-like exchanges extracted from Reddit comment chains over a period spanning
from 2005 through 2017, DialoGPT extends the Hugging Face PyTorch transformer to attain a performance close to human
both in terms of automatic and human evaluation in single-turn dialogue settings. We show that conversational systems
that leverage DialoGPT generate more relevant, contentful and context-consistent responses than strong baseline
systems. The pre-trained model and training pipeline are publicly released to facilitate research into neural response
generation and the development of more intelligent open-domain dialogue systems.*
The original code can be found [here](https://github.com/microsoft/DialoGPT).
## Usage tips
- DialoGPT is a model with absolute position embeddings so it's usually advised to pad the inputs on the right rather
than the left.
- DialoGPT was trained with a causal language modeling (CLM) objective on conversational data and is therefore powerful
at response generation in open-domain dialogue systems.
- DialoGPT enables the user to create a chat bot in just 10 lines of code as shown on [DialoGPT's model card](https://huggingface.co/microsoft/DialoGPT-medium).
Training:
In order to train or fine-tune DialoGPT, one can use causal language modeling training. To cite the official paper: *We
follow the OpenAI GPT-2 to model a multiturn dialogue session as a long text and frame the generation task as language
modeling. We first concatenate all dialog turns within a dialogue session into a long text x_1,..., x_N (N is the
sequence length), ended by the end-of-text token.* For more information please confer to the original paper.
<Tip>
DialoGPT's architecture is based on the GPT2 model, refer to [GPT2's documentation page](gpt2) for API reference and examples.
</Tip>
| huggingface/transformers/blob/main/docs/source/en/model_doc/dialogpt.md |
!--Copyright 2020 The HuggingFace Team. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
the License. You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
specific language governing permissions and limitations under the License.
⚠️ Note that this file is in Markdown but contain specific syntax for our doc-builder (similar to MDX) that may not be
rendered properly in your Markdown viewer.
-->
# SqueezeBERT
## Overview
The SqueezeBERT model was proposed in [SqueezeBERT: What can computer vision teach NLP about efficient neural networks?](https://arxiv.org/abs/2006.11316) by Forrest N. Iandola, Albert E. Shaw, Ravi Krishna, Kurt W. Keutzer. It's a
bidirectional transformer similar to the BERT model. The key difference between the BERT architecture and the
SqueezeBERT architecture is that SqueezeBERT uses [grouped convolutions](https://blog.yani.io/filter-group-tutorial)
instead of fully-connected layers for the Q, K, V and FFN layers.
The abstract from the paper is the following:
*Humans read and write hundreds of billions of messages every day. Further, due to the availability of large datasets,
large computing systems, and better neural network models, natural language processing (NLP) technology has made
significant strides in understanding, proofreading, and organizing these messages. Thus, there is a significant
opportunity to deploy NLP in myriad applications to help web users, social networks, and businesses. In particular, we
consider smartphones and other mobile devices as crucial platforms for deploying NLP models at scale. However, today's
highly-accurate NLP neural network models such as BERT and RoBERTa are extremely computationally expensive, with
BERT-base taking 1.7 seconds to classify a text snippet on a Pixel 3 smartphone. In this work, we observe that methods
such as grouped convolutions have yielded significant speedups for computer vision networks, but many of these
techniques have not been adopted by NLP neural network designers. We demonstrate how to replace several operations in
self-attention layers with grouped convolutions, and we use this technique in a novel network architecture called
SqueezeBERT, which runs 4.3x faster than BERT-base on the Pixel 3 while achieving competitive accuracy on the GLUE test
set. The SqueezeBERT code will be released.*
This model was contributed by [forresti](https://huggingface.co/forresti).
## Usage tips
- SqueezeBERT is a model with absolute position embeddings so it's usually advised to pad the inputs on the right
rather than the left.
- SqueezeBERT is similar to BERT and therefore relies on the masked language modeling (MLM) objective. It is therefore
efficient at predicting masked tokens and at NLU in general, but is not optimal for text generation. Models trained
with a causal language modeling (CLM) objective are better in that regard.
- For best results when finetuning on sequence classification tasks, it is recommended to start with the
*squeezebert/squeezebert-mnli-headless* checkpoint.
## Resources
- [Text classification task guide](../tasks/sequence_classification)
- [Token classification task guide](../tasks/token_classification)
- [Question answering task guide](../tasks/question_answering)
- [Masked language modeling task guide](../tasks/masked_language_modeling)
- [Multiple choice task guide](../tasks/multiple_choice)
## SqueezeBertConfig
[[autodoc]] SqueezeBertConfig
## SqueezeBertTokenizer
[[autodoc]] SqueezeBertTokenizer
- build_inputs_with_special_tokens
- get_special_tokens_mask
- create_token_type_ids_from_sequences
- save_vocabulary
## SqueezeBertTokenizerFast
[[autodoc]] SqueezeBertTokenizerFast
## SqueezeBertModel
[[autodoc]] SqueezeBertModel
## SqueezeBertForMaskedLM
[[autodoc]] SqueezeBertForMaskedLM
## SqueezeBertForSequenceClassification
[[autodoc]] SqueezeBertForSequenceClassification
## SqueezeBertForMultipleChoice
[[autodoc]] SqueezeBertForMultipleChoice
## SqueezeBertForTokenClassification
[[autodoc]] SqueezeBertForTokenClassification
## SqueezeBertForQuestionAnswering
[[autodoc]] SqueezeBertForQuestionAnswering
| huggingface/transformers/blob/main/docs/source/en/model_doc/squeezebert.md |
!--Copyright 2023 The HuggingFace Team. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
the License. You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
specific language governing permissions and limitations under the License.
⚠️ Note that this file is in Markdown but contain specific syntax for our doc-builder (similar to MDX) that may not be
rendered properly in your Markdown viewer.
-->
# Open-Llama
<Tip warning={true}>
This model is in maintenance mode only, we don't accept any new PRs changing its code.
If you run into any issues running this model, please reinstall the last version that supported this model: v4.31.0.
You can do so by running the following command: `pip install -U transformers==4.31.0`.
</Tip>
<Tip warning={true}>
This model differs from the [OpenLLaMA models](https://huggingface.co/models?search=openllama) on the Hugging Face Hub, which primarily use the [LLaMA](llama) architecture.
</Tip>
## Overview
The Open-Llama model was proposed in the open source Open-Llama project by community developer s-JoL.
The model is mainly based on LLaMA with some modifications, incorporating memory-efficient attention from Xformers, stable embedding from Bloom, and shared input-output embedding from PaLM.
And the model is pre-trained on both Chinese and English, which gives it better performance on Chinese language tasks.
This model was contributed by [s-JoL](https://huggingface.co/s-JoL).
The original code was released on GitHub by [s-JoL](https://github.com/s-JoL), but is now removed.
## OpenLlamaConfig
[[autodoc]] OpenLlamaConfig
## OpenLlamaModel
[[autodoc]] OpenLlamaModel
- forward
## OpenLlamaForCausalLM
[[autodoc]] OpenLlamaForCausalLM
- forward
## OpenLlamaForSequenceClassification
[[autodoc]] OpenLlamaForSequenceClassification
- forward
| huggingface/transformers/blob/main/docs/source/en/model_doc/open-llama.md |
!--Copyright 2023 The HuggingFace Team. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
the License. You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
specific language governing permissions and limitations under the License.
⚠️ Note that this file is in Markdown but contain specific syntax for our doc-builder (similar to MDX) that may not be
rendered properly in your Markdown viewer.
-->
# Agents & Tools
<Tip warning={true}>
Transformers Agents is an experimental API which is subject to change at any time. Results returned by the agents
can vary as the APIs or underlying models are prone to change.
</Tip>
To learn more about agents and tools make sure to read the [introductory guide](../transformers_agents). This page
contains the API docs for the underlying classes.
## Agents
We provide three types of agents: [`HfAgent`] uses inference endpoints for opensource models, [`LocalAgent`] uses a model of your choice locally and [`OpenAiAgent`] uses OpenAI closed models.
### HfAgent
[[autodoc]] HfAgent
### LocalAgent
[[autodoc]] LocalAgent
### OpenAiAgent
[[autodoc]] OpenAiAgent
### AzureOpenAiAgent
[[autodoc]] AzureOpenAiAgent
### Agent
[[autodoc]] Agent
- chat
- run
- prepare_for_new_chat
## Tools
### load_tool
[[autodoc]] load_tool
### Tool
[[autodoc]] Tool
### PipelineTool
[[autodoc]] PipelineTool
### RemoteTool
[[autodoc]] RemoteTool
### launch_gradio_demo
[[autodoc]] launch_gradio_demo
## Agent Types
Agents can handle any type of object in-between tools; tools, being completely multimodal, can accept and return
text, image, audio, video, among other types. In order to increase compatibility between tools, as well as to
correctly render these returns in ipython (jupyter, colab, ipython notebooks, ...), we implement wrapper classes
around these types.
The wrapped objects should continue behaving as initially; a text object should still behave as a string, an image
object should still behave as a `PIL.Image`.
These types have three specific purposes:
- Calling `to_raw` on the type should return the underlying object
- Calling `to_string` on the type should return the object as a string: that can be the string in case of an `AgentText`
but will be the path of the serialized version of the object in other instances
- Displaying it in an ipython kernel should display the object correctly
### AgentText
[[autodoc]] transformers.tools.agent_types.AgentText
### AgentImage
[[autodoc]] transformers.tools.agent_types.AgentImage
### AgentAudio
[[autodoc]] transformers.tools.agent_types.AgentAudio
| huggingface/transformers/blob/main/docs/source/en/main_classes/agent.md |
!--Copyright 2023 The HuggingFace Team. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
the License. You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
specific language governing permissions and limitations under the License.
⚠️ Note that this file is in Markdown but contain specific syntax for our doc-builder (similar to MDX) that may not be
rendered properly in your Markdown viewer.
-->
# Knowledge Distillation for Computer Vision
[[open-in-colab]]
Knowledge distillation is a technique used to transfer knowledge from a larger, more complex model (teacher) to a smaller, simpler model (student). To distill knowledge from one model to another, we take a pre-trained teacher model trained on a certain task (image classification for this case) and randomly initialize a student model to be trained on image classification. Next, we train the student model to minimize the difference between it's outputs and the teacher's outputs, thus making it mimic the behavior. It was first introduced in [Distilling the Knowledge in a Neural Network by Hinton et al](https://arxiv.org/abs/1503.02531). In this guide, we will do task-specific knowledge distillation. We will use the [beans dataset](https://huggingface.co/datasets/beans) for this.
This guide demonstrates how you can distill a [fine-tuned ViT model](https://huggingface.co/merve/vit-mobilenet-beans-224) (teacher model) to a [MobileNet](https://huggingface.co/google/mobilenet_v2_1.4_224) (student model) using the [Trainer API](https://huggingface.co/docs/transformers/en/main_classes/trainer#trainer) of 🤗 Transformers.
Let's install the libraries needed for distillation and evaluating the process.
```bash
pip install transformers datasets accelerate tensorboard evaluate --upgrade
```
In this example, we are using the `merve/beans-vit-224` model as teacher model. It's an image classification model, based on `google/vit-base-patch16-224-in21k` fine-tuned on beans dataset. We will distill this model to a randomly initialized MobileNetV2.
We will now load the dataset.
```python
from datasets import load_dataset
dataset = load_dataset("beans")
```
We can use an image processor from either of the models, as in this case they return the same output with same resolution. We will use the `map()` method of `dataset` to apply the preprocessing to every split of the dataset.
```python
from transformers import AutoImageProcessor
teacher_processor = AutoImageProcessor.from_pretrained("merve/beans-vit-224")
def process(examples):
processed_inputs = teacher_processor(examples["image"])
return processed_inputs
processed_datasets = dataset.map(process, batched=True)
```
Essentially, we want the student model (a randomly initialized MobileNet) to mimic the teacher model (fine-tuned vision transformer). To achieve this, we first get the logits output from the teacher and the student. Then, we divide each of them by the parameter `temperature` which controls the importance of each soft target. A parameter called `lambda` weighs the importance of the distillation loss. In this example, we will use `temperature=5` and `lambda=0.5`. We will use the Kullback-Leibler Divergence loss to compute the divergence between the student and teacher. Given two data P and Q, KL Divergence explains how much extra information we need to represent P using Q. If two are identical, their KL divergence is zero, as there's no other information needed to explain P from Q. Thus, in the context of knowledge distillation, KL divergence is useful.
```python
from transformers import TrainingArguments, Trainer
import torch
import torch.nn as nn
import torch.nn.functional as F
class ImageDistilTrainer(Trainer):
def __init__(self, teacher_model=None, student_model=None, temperature=None, lambda_param=None, *args, **kwargs):
super().__init__(model=student_model, *args, **kwargs)
self.teacher = teacher_model
self.student = student_model
self.loss_function = nn.KLDivLoss(reduction="batchmean")
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
self.teacher.to(device)
self.teacher.eval()
self.temperature = temperature
self.lambda_param = lambda_param
def compute_loss(self, student, inputs, return_outputs=False):
student_output = self.student(**inputs)
with torch.no_grad():
teacher_output = self.teacher(**inputs)
# Compute soft targets for teacher and student
soft_teacher = F.softmax(teacher_output.logits / self.temperature, dim=-1)
soft_student = F.log_softmax(student_output.logits / self.temperature, dim=-1)
# Compute the loss
distillation_loss = self.loss_function(soft_student, soft_teacher) * (self.temperature ** 2)
# Compute the true label loss
student_target_loss = student_output.loss
# Calculate final loss
loss = (1. - self.lambda_param) * student_target_loss + self.lambda_param * distillation_loss
return (loss, student_output) if return_outputs else loss
```
We will now login to Hugging Face Hub so we can push our model to the Hugging Face Hub through the `Trainer`.
```python
from huggingface_hub import notebook_login
notebook_login()
```
Let's set the `TrainingArguments`, the teacher model and the student model.
```python
from transformers import AutoModelForImageClassification, MobileNetV2Config, MobileNetV2ForImageClassification
training_args = TrainingArguments(
output_dir="my-awesome-model",
num_train_epochs=30,
fp16=True,
logging_dir=f"{repo_name}/logs",
logging_strategy="epoch",
evaluation_strategy="epoch",
save_strategy="epoch",
load_best_model_at_end=True,
metric_for_best_model="accuracy",
report_to="tensorboard",
push_to_hub=True,
hub_strategy="every_save",
hub_model_id=repo_name,
)
num_labels = len(processed_datasets["train"].features["labels"].names)
# initialize models
teacher_model = AutoModelForImageClassification.from_pretrained(
"merve/beans-vit-224",
num_labels=num_labels,
ignore_mismatched_sizes=True
)
# training MobileNetV2 from scratch
student_config = MobileNetV2Config()
student_config.num_labels = num_labels
student_model = MobileNetV2ForImageClassification(student_config)
```
We can use `compute_metrics` function to evaluate our model on the test set. This function will be used during the training process to compute the `accuracy` & `f1` of our model.
```python
import evaluate
import numpy as np
accuracy = evaluate.load("accuracy")
def compute_metrics(eval_pred):
predictions, labels = eval_pred
acc = accuracy.compute(references=labels, predictions=np.argmax(predictions, axis=1))
return {"accuracy": acc["accuracy"]}
```
Let's initialize the `Trainer` with the training arguments we defined. We will also initialize our data collator.
```python
from transformers import DefaultDataCollator
data_collator = DefaultDataCollator()
trainer = ImageDistilTrainer(
student_model=student_model,
teacher_model=teacher_model,
training_args=training_args,
train_dataset=processed_datasets["train"],
eval_dataset=processed_datasets["validation"],
data_collator=data_collator,
tokenizer=teacher_processor,
compute_metrics=compute_metrics,
temperature=5,
lambda_param=0.5
)
```
We can now train our model.
```python
trainer.train()
```
We can evaluate the model on the test set.
```python
trainer.evaluate(processed_datasets["test"])
```
On test set, our model reaches 72 percent accuracy. To have a sanity check over efficiency of distillation, we also trained MobileNet on the beans dataset from scratch with the same hyperparameters and observed 63 percent accuracy on the test set. We invite the readers to try different pre-trained teacher models, student architectures, distillation parameters and report their findings. The training logs and checkpoints for distilled model can be found in [this repository](https://huggingface.co/merve/vit-mobilenet-beans-224), and MobileNetV2 trained from scratch can be found in this [repository](https://huggingface.co/merve/resnet-mobilenet-beans-5).
| huggingface/transformers/blob/main/docs/source/en/tasks/knowledge_distillation_for_image_classification.md |
!---
Copyright 2021 The HuggingFace Team. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
-->
# Automatic Speech Recognition Examples
## Table of Contents
- [Automatic Speech Recognition with CTC](#connectionist-temporal-classification)
- [Single GPU example](#single-gpu-ctc)
- [Multi GPU example](#multi-gpu-ctc)
- [Examples](#examples-ctc)
- [TIMIT](#timit-ctc)
- [Librispeech](#librispeech-ctc)
- [Common Voice](#common-voice-ctc)
- [Multilingual Librispeech](#multilingual-librispeech-ctc)
- [Automatic Speech Recognition with CTC and Adapter Layers](#connectionist-temporal-classification-with-adapters)
- [Massive Multilingual Speech (MMS)](#mms-model)
- [Examples](#examples-ctc-adapter)
- [Common Voice](#common-voice-ctc-adapter)
- [Automatic Speech Recognition with Sequence-to-Sequence](#sequence-to-sequence)
- [Whisper Model](#whisper-model)
- [Speech-Encoder-Decoder Model](#warm-started-speech-encoder-decoder-model)
- [Examples](#examples-seq2seq)
- [Librispeech](#librispeech-seq2seq)
## Connectionist Temporal Classification
The script [`run_speech_recognition_ctc.py`](https://github.com/huggingface/transformers/blob/main/examples/pytorch/speech-recognition/run_speech_recognition_ctc.py) can be used to fine-tune any pretrained [Connectionist Temporal Classification Model](https://huggingface.co/docs/transformers/main/en/model_doc/auto#transformers.AutoModelForCTC) for automatic speech
recognition on one of the [official speech recognition datasets](https://huggingface.co/datasets?task_ids=task_ids:automatic-speech-recognition) or a custom dataset.
Speech recognition models that have been pretrained in unsupervised fashion on audio data alone, *e.g.* [Wav2Vec2](https://huggingface.co/transformers/main/model_doc/wav2vec2.html), [HuBERT](https://huggingface.co/transformers/main/model_doc/hubert.html), [XLSR-Wav2Vec2](https://huggingface.co/transformers/main/model_doc/xlsr_wav2vec2.html), have shown to require only
very little annotated data to yield good performance on automatic speech recognition datasets.
In the script [`run_speech_recognition_ctc`], we first create a vocabulary from all unique characters of both the training data and evaluation data. Then, we preprocesses the speech recognition dataset, which includes correct resampling, normalization and padding. Finally, the pretrained speech recognition model is fine-tuned on the annotated speech recognition datasets using CTC loss.
---
**NOTE**
If you encounter problems with data preprocessing by setting `--preprocessing_num_workers` > 1,
you might want to set the environment variable `OMP_NUM_THREADS` to 1 as follows:
```bash
OMP_NUM_THREADS=1 python run_speech_recognition_ctc ...
```
If the environment variable is not set, the training script might freeze, *i.e.* see: https://github.com/pytorch/audio/issues/1021#issuecomment-726915239
---
### Single GPU CTC
The following command shows how to fine-tune [XLSR-Wav2Vec2](https://huggingface.co/transformers/main/model_doc/xlsr_wav2vec2.html) on [Common Voice](https://huggingface.co/datasets/common_voice) using a single GPU in half-precision.
```bash
python run_speech_recognition_ctc.py \
--dataset_name="common_voice" \
--model_name_or_path="facebook/wav2vec2-large-xlsr-53" \
--dataset_config_name="tr" \
--output_dir="./wav2vec2-common_voice-tr-demo" \
--overwrite_output_dir \
--num_train_epochs="15" \
--per_device_train_batch_size="16" \
--gradient_accumulation_steps="2" \
--learning_rate="3e-4" \
--warmup_steps="500" \
--evaluation_strategy="steps" \
--text_column_name="sentence" \
--length_column_name="input_length" \
--save_steps="400" \
--eval_steps="100" \
--layerdrop="0.0" \
--save_total_limit="3" \
--freeze_feature_encoder \
--gradient_checkpointing \
--chars_to_ignore , ? . ! - \; \: \" “ % ‘ ” � \
--fp16 \
--group_by_length \
--push_to_hub \
--do_train --do_eval
```
On a single V100 GPU, this script should run in *ca.* 1 hour 20 minutes and yield a CTC loss of **0.39** and word error rate
of **0.35**.
### Multi GPU CTC
The following command shows how to fine-tune [XLSR-Wav2Vec2](https://huggingface.co/transformers/main/model_doc/xlsr_wav2vec2.html) on [Common Voice](https://huggingface.co/datasets/common_voice) using 8 GPUs in half-precision.
```bash
torchrun \
--nproc_per_node 8 run_speech_recognition_ctc.py \
--dataset_name="common_voice" \
--model_name_or_path="facebook/wav2vec2-large-xlsr-53" \
--dataset_config_name="tr" \
--output_dir="./wav2vec2-common_voice-tr-demo-dist" \
--overwrite_output_dir \
--num_train_epochs="15" \
--per_device_train_batch_size="4" \
--learning_rate="3e-4" \
--warmup_steps="500" \
--evaluation_strategy="steps" \
--text_column_name="sentence" \
--length_column_name="input_length" \
--save_steps="400" \
--eval_steps="100" \
--logging_steps="1" \
--layerdrop="0.0" \
--save_total_limit="3" \
--freeze_feature_encoder \
--gradient_checkpointing \
--chars_to_ignore , ? . ! - \; \: \" “ % ‘ ” � \
--fp16 \
--group_by_length \
--push_to_hub \
--do_train --do_eval
```
On 8 V100 GPUs, this script should run in *ca.* 18 minutes and yield a CTC loss of **0.39** and word error rate
of **0.36**.
### Multi GPU CTC with Dataset Streaming
The following command shows how to use [Dataset Streaming mode](https://huggingface.co/docs/datasets/dataset_streaming)
to fine-tune [XLS-R](https://huggingface.co/transformers/main/model_doc/xls_r.html)
on [Common Voice](https://huggingface.co/datasets/common_voice) using 4 GPUs in half-precision.
Streaming mode imposes several constraints on training:
1. We need to construct a tokenizer beforehand and define it via `--tokenizer_name_or_path`.
2. `--num_train_epochs` has to be replaced by `--max_steps`. Similarly, all other epoch-based arguments have to be
replaced by step-based ones.
3. Full dataset shuffling on each epoch is not possible, since we don't have the whole dataset available at once.
However, the `--shuffle_buffer_size` argument controls how many examples we can pre-download before shuffling them.
```bash
**torchrun \
--nproc_per_node 4 run_speech_recognition_ctc_streaming.py \
--dataset_name="common_voice" \
--model_name_or_path="facebook/wav2vec2-xls-r-300m" \
--tokenizer_name_or_path="anton-l/wav2vec2-tokenizer-turkish" \
--dataset_config_name="tr" \
--train_split_name="train+validation" \
--eval_split_name="test" \
--output_dir="wav2vec2-xls-r-common_voice-tr-ft" \
--overwrite_output_dir \
--max_steps="5000" \
--per_device_train_batch_size="8" \
--gradient_accumulation_steps="2" \
--learning_rate="5e-4" \
--warmup_steps="500" \
--evaluation_strategy="steps" \
--text_column_name="sentence" \
--save_steps="500" \
--eval_steps="500" \
--logging_steps="1" \
--layerdrop="0.0" \
--eval_metrics wer cer \
--save_total_limit="1" \
--mask_time_prob="0.3" \
--mask_time_length="10" \
--mask_feature_prob="0.1" \
--mask_feature_length="64" \
--freeze_feature_encoder \
--chars_to_ignore , ? . ! - \; \: \" “ % ‘ ” � \
--max_duration_in_seconds="20" \
--shuffle_buffer_size="500" \
--fp16 \
--push_to_hub \
--do_train --do_eval \
--gradient_checkpointing**
```
On 4 V100 GPUs, this script should run in *ca.* 3h 31min and yield a CTC loss of **0.35** and word error rate
of **0.29**.
### Examples CTC
The following tables present a couple of example runs on the most popular speech-recognition datasets.
The presented performances are by no means optimal as no hyper-parameter tuning was done. Nevertheless,
they can serve as a baseline to improve upon.
#### TIMIT CTC
- [TIMIT](https://huggingface.co/datasets/timit_asr)
| Dataset | Dataset Config | Pretrained Model | Word error rate on eval | Phoneme error rate on eval | GPU setup | Training time | Fine-tuned Model & Logs | Command to reproduce |
|-------|------------------------------|-------------|---------------|---------------|----------------------|-------------| -------------| ------- |
| [TIMIT](https://huggingface.co/datasets/timit_asr)| - | [wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) | 0.21 | - | 1 GPU TITAN RTX | 32min | [here](https://huggingface.co/patrickvonplaten/wav2vec2-base-timit-fine-tuned) | [run.sh](https://huggingface.co/patrickvonplaten/wav2vec2-base-timit-fine-tuned/blob/main/run.sh) |
| [TIMIT](https://huggingface.co/datasets/timit_asr)| - | [wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) | 0.21 | - | 1 GPU TITAN RTX | 32min | [here](https://huggingface.co/patrickvonplaten/wav2vec2-base-timit-fine-tuned) | [run.sh](https://huggingface.co/patrickvonplaten/wav2vec2-base-timit-fine-tuned/blob/main/run.sh) |
| [TIMIT](https://huggingface.co/datasets/timit_asr)| - | [unispeech-large-1500h-cv](https://huggingface.co/microsoft/unispeech-large-1500h-cv) | 0.22 | - | 1 GPU TITAN RTX | 35min | [here](https://huggingface.co/patrickvonplaten/unispeech-large-1500h-cv-timit) | [run.sh](https://huggingface.co/patrickvonplaten/unispeech-large-1500h-cv-timit/blob/main/run.sh) |
| [TIMIT](https://huggingface.co/datasets/timit_asr)| - | [asapp/sew-mid-100k](https://huggingface.co/asapp/sew-mid-100k) | 0.30 | - | 1 GPU TITAN RTX | 28min | [here](https://huggingface.co/patrickvonplaten/sew-small-100k-timit) | [run.sh](https://huggingface.co/patrickvonplaten/sew-small-100k-timit/blob/main/run.sh) |
| [TIMIT](https://huggingface.co/datasets/timit_asr)| - | [ntu-spml/distilhubert](https://huggingface.co/ntu-spml/distilhubert) | 0.68 | - | 1 GPU TITAN RTX | 26min | [here](https://huggingface.co/patrickvonplaten/distilhubert-timit) | [run.sh](https://huggingface.co/patrickvonplaten/distilhubert-timit/blob/main/run.sh) |
#### Librispeech CTC
- [Librispeech](https://huggingface.co/datasets/librispeech_asr)
| Dataset | Dataset Config | Pretrained Model | Word error rate on eval | Phoneme error rate on eval | GPU setup | Training time | Fine-tuned Model & Logs | Command to reproduce |
|-------|------------------------------|-------------|---------------|---------------|----------------------|-------------| -------------| ------- |
| [Librispeech](https://huggingface.co/datasets/librispeech_asr)| `"clean"` - `"train.100"` | [microsoft/wavlm-large](https://huggingface.co/microsoft/wavlm-large) | 0.049 | - | 8 GPU V100 | 1h30min | [here](https://huggingface.co/patrickvonplaten/wavlm-libri-clean-100h-large) | [run.sh](https://huggingface.co/patrickvonplaten/wavlm-libri-clean-100h-large/blob/main/run.sh) |
| [Librispeech](https://huggingface.co/datasets/librispeech_asr)| `"clean"` - `"train.100"` | [microsoft/wavlm-base-plus](https://huggingface.co/microsoft/wavlm-base-plus) | 0.068 | - | 8 GPU V100 | 1h30min | [here](https://huggingface.co/patrickvonplaten/wavlm-libri-clean-100h-base-plus) | [run.sh](https://huggingface.co/patrickvonplaten/wavlm-libri-clean-100h-base-plus/blob/main/run.sh) |
| [Librispeech](https://huggingface.co/datasets/librispeech_asr)| `"clean"` - `"train.100"` | [facebook/wav2vec2-large-lv60](https://huggingface.co/facebook/wav2vec2-large-lv60) | 0.042 | - | 8 GPU V100 | 1h30min | [here](https://huggingface.co/patrickvonplaten/wav2vec2-librispeech-clean-100h-demo-dist) | [run.sh](https://huggingface.co/patrickvonplaten/wav2vec2-librispeech-clean-100h-demo-dist/blob/main/run.sh) |
| [Librispeech](https://huggingface.co/datasets/librispeech_asr)| `"clean"` - `"train.100"` | [facebook/wav2vec2-large-lv60](https://huggingface.co/facebook/wav2vec2-large-lv60) | 0.042 | - | 8 GPU V100 | 1h30min | [here](https://huggingface.co/patrickvonplaten/wav2vec2-librispeech-clean-100h-demo-dist) | [run.sh](https://huggingface.co/patrickvonplaten/wav2vec2-librispeech-clean-100h-demo-dist/blob/main/run.sh) |
| [Librispeech](https://huggingface.co/datasets/librispeech_asr)| `"clean"` - `"train.100"` | [facebook/hubert-large-ll60k](https://huggingface.co/facebook/hubert-large-ll60k) | 0.088 | - | 8 GPU V100 | 1h30min | [here](https://huggingface.co/patrickvonplaten/hubert-librispeech-clean-100h-demo-dist) | [run.sh](https://huggingface.co/patrickvonplaten/hubert-librispeech-clean-100h-demo-dist/blob/main/run.sh) |
| [Librispeech](https://huggingface.co/datasets/librispeech_asr)| `"clean"` - `"train.100"` | [asapp/sew-mid-100k](https://huggingface.co/asapp/sew-mid-100k) | 0.167 | | 8 GPU V100 | 54min | [here](https://huggingface.co/patrickvonplaten/sew-mid-100k-librispeech-clean-100h-ft) | [run.sh](https://huggingface.co/patrickvonplaten/sew-mid-100k-librispeech-clean-100h-ft/blob/main/run.sh) |
#### Common Voice CTC
- [Common Voice](https://huggingface.co/datasets/common_voice)
| Dataset | Dataset Config | Pretrained Model | Word error rate on eval | Phoneme error rate on eval | GPU setup | Training time | Fine-tuned Model & Logs | Command to reproduce |
|-------|------------------------------|-------------|---------------|---------------|----------------------|-------------| -------------| ------- |
| [Common Voice](https://huggingface.co/datasets/mozilla-foundation/common_voice_3_0)| `"tr"` | [facebook/wav2vec2-large-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) | - | 0.099 | 8 GPU V100 | 23min | [here](https://huggingface.co/patrickvonplaten/xls-r-300m-tr-phoneme) | [run.sh](https://huggingface.co/patrickvonplaten/xls-r-300m-tr-phoneme/blob/main/run.sh) |
| [Common Voice](https://huggingface.co/datasets/mozilla-foundation/common_voice_3_0)| `"it"` | [facebook/wav2vec2-large-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) | - | 0.077 | 8 GPU V100 | 23min | [here](https://huggingface.co/patrickvonplaten/xls-r-300m-it-phoneme) | [run.sh](https://huggingface.co/patrickvonplaten/xls-r-300m-it-phoneme/blob/main/run.sh) |
| [Common Voice](https://huggingface.co/datasets/mozilla-foundation/common_voice_3_0)| `"sv-SE"` | [facebook/wav2vec2-large-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) | - | 0.099 | 8 GPU V100 | 23min | [here](https://huggingface.co/patrickvonplaten/xls-r-300m-sv-phoneme) | [run.sh](https://huggingface.co/patrickvonplaten/xls-r-300m-sv-phoneme/blob/main/run.sh) |
| [Common Voice](https://huggingface.co/datasets/common_voice)| `"tr"` | [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) | 0.36 | - | 8 GPU V100 | 18min | [here](https://huggingface.co/patrickvonplaten/wav2vec2-common_voice-tr-demo-dist) | [run.sh](https://huggingface.co/patrickvonplaten/wav2vec2-common_voice-tr-demo-dist/blob/main/run_dist.sh) |
| [Common Voice](https://huggingface.co/datasets/common_voice)| `"tr"` | [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) | 0.31 | - | 8 GPU V100 | 1h05 | [here](https://huggingface.co/patrickvonplaten/wav2vec2-large-xlsr-53-common_voice-tr-ft) | [run.sh](https://huggingface.co/patrickvonplaten/wav2vec2-large-xlsr-53-common_voice-tr-ft/blob/main/run.sh) |
| [Common Voice](https://huggingface.co/datasets/common_voice)| `"tr"` | [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) | 0.35 | - | 1 GPU V100 | 1h20min | [here](https://huggingface.co/patrickvonplaten/wav2vec2-common_voice-tr-demo) | [run.sh](https://huggingface.co/patrickvonplaten/wav2vec2-common_voice-tr-demo/blob/main/run.sh) |
| [Common Voice](https://huggingface.co/datasets/common_voice)| `"tr"` | [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) | 0.31 | - | 8 GPU V100 | 1h05 | [here](https://huggingface.co/patrickvonplaten/wav2vec2-large-xls-r-300m-common_voice-tr-ft) | [run.sh](https://huggingface.co/patrickvonplaten/wav2vec2-large-xls-r-300m-common_voice-tr-ft/blob/main/run.sh) |
| [Common Voice](https://huggingface.co/datasets/common_voice)| `"tr"` | [facebook/wav2vec2-xls-r-1b](https://huggingface.co/facebook/wav2vec2-xls-r-1b) | 0.21 | - | 2 GPU Titan 24 GB RAM | 15h10 | [here](https://huggingface.co/patrickvonplaten/wav2vec2-xls-r-1b-common_voice-tr-ft) | [run.sh](https://huggingface.co/patrickvonplaten/wav2vec2-large-xls-r-1b-common_voice-tr-ft/blob/main/run.sh) |
| [Common Voice](https://huggingface.co/datasets/common_voice)| `"tr"` in streaming mode | [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) | 0.29 | - | 4 GPU V100 | 3h31 | [here](https://huggingface.co/anton-l/wav2vec2-xls-r-common_voice-tr-ft-stream) | [run.sh](https://huggingface.co/anton-l/wav2vec2-xls-r-common_voice-tr-ft-stream/blob/main/run.sh) |
#### Multilingual Librispeech CTC
- [Multilingual Librispeech](https://huggingface.co/datasets/multilingual_librispeech)
| Dataset | Dataset Config | Pretrained Model | Word error rate on eval | Phoneme error rate on eval | GPU setup | Training time | Fine-tuned Model & Logs | Command to reproduce |
|-------|------------------------------|-------------|---------------|---------------|----------------------|-------------| -------------| ------- |
| [Multilingual Librispeech](https://huggingface.co/datasets/multilingual_librispeech)| `"german"` | [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) | 0.13 | - | 1 GPU Titan 24 GB RAM | 15h04 | [here](https://huggingface.co/patrickvonplaten/wav2vec2-xlsr-53-300m-mls-german-ft) | [run.sh](https://huggingface.co/patrickvonplaten/wav2vec2-xlsr-53-300m-mls-german-ft/blob/main/run.sh) |
| [Multilingual Librispeech](https://huggingface.co/datasets/multilingual_librispeech)| `"german"` | [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) | 0.15 | - | 1 GPU Titan 24 GB RAM | 15h04 | [here](https://huggingface.co/patrickvonplaten/wav2vec2-300m-mls-german-ft) | [run.sh](https://huggingface.co/patrickvonplaten/wav2vec2-300m-mls-german-ft/blob/main/run.sh) |
## Connectionist Temporal Classification With Adapters
The script [`run_speech_recognition_ctc_adapter.py`](https://github.com/huggingface/transformers/blob/main/examples/pytorch/speech-recognition/run_speech_recognition_ctc_adapter.py) can be used to fine-tune adapter layers for [Wav2Vec2-like models like MMS](https://huggingface.co/docs/transformers/main/en/model_doc/mms) for automatic speech recognition.
### MMS Model
The [Massive Multilingual Speech (MMS) model](https://huggingface.co/facebook/mms-1b-all) has been pre-trained and fine-tuned
on 1000+ languages. The model makes use of adapter attention layers to fine-tune only a small part
of the model on a specific language. The model already comes with fine-tuned adapter layers for 1000+ languages and
can be used for inference for 1000+ languages out of the box.
However, for improved performance or more specific use cases one can re-initialize the adapter weights, freeze all
other weights and fine-tune them on a specific dataset as shown in the [example below](#examples-ctc-adapter).
Note that the adapter weights include low dimensional linear layers for every attention block as well as the final language
model head layers.
### Examples CTC Adapter
In the following we will look at how one can fine-tune adapter weights for any of the
[MMS CTC checkpoints](https://huggingface.co/models?pipeline_tag=automatic-speech-recognition&other=mms&sort=downloads) in less than 1 hour.
#### Common Voice CTC Adapter
As in the examples [above](#examples-ctc), we fine-tune on Common Voice's 6 dataset in Turkish as an example.
Contrary to [`run_speech_recognition_ctc.py`](https://github.com/huggingface/transformers/blob/main/examples/pytorch/speech-recognition/run_speech_recognition_ctc.py) before there is a `--target_language` which has to be defined to state for which
language or concept the adapter layers shall be trained. The adapter weights will then
accordingly be called `adapter.{<target_language}.safetensors`.
Let's run an example script. Make sure to be logged in so that your model can be directly uploaded to the Hub.
```
huggingface-cli login
```
Now, let's run an example and upload it to the Hub under `wav2vec2-common_voice-tr-mms-demo`.
```sh
python run_speech_recognition_ctc.py \
--dataset_name="common_voice" \
--model_name_or_path="facebook/mms-1b-all" \
--dataset_config_name="tr" \
--output_dir="./wav2vec2-common_voice-tr-mms-demo" \
--num_train_epochs="4" \
--per_device_train_batch_size="32" \
--learning_rate="1e-3" \
--warmup_steps="100" \
--evaluation_strategy="steps" \
--text_column_name="sentence" \
--length_column_name="input_length" \
--save_steps="200" \
--eval_steps="100" \
--save_total_limit="3" \
--target_language="tur" \
--gradient_checkpointing \
--chars_to_ignore , ? . ! - \; \: \" “ % ‘ ” � \
--fp16 \
--group_by_length \
--do_train --do_eval \
--push_to_hub
```
This should take less than 10 minutes on most GPUs and you should very quickly get word error rates
below 27%.
For an example run, you can have a look at [`patrickvonplaten/wav2vec2-common_voice-tr-mms-demo`](https://huggingface.co/patrickvonplaten/wav2vec2-common_voice-tr-mms-demo).
If you'd like to train another adapter model with the same base model, you can simply re-use the same `--output_dir`,
but make sure to pass the `--output_dir` folder also to `--tokenizer_name_or_path` so that the vocabulary is not
overwritten but **extended**. Assuming you would like to train adapter weights on Swedish in addition to Turkish and save
the adapter weights in the same model repo, you can run:
```sh
python run_speech_recognition_ctc.py \
--dataset_name="common_voice" \
--model_name_or_path="facebook/mms-1b-all" \
--dataset_config_name="sw" \
--output_dir="./wav2vec2-common_voice-tr-mms-demo" \
--tokenizer_name_or_path="./wav2vec2-common_voice-tr-mms-demo" \
--num_train_epochs="4" \
--per_device_train_batch_size="32" \
--learning_rate="1e-3" \
--warmup_steps="100" \
--evaluation_strategy="steps" \
--text_column_name="sentence" \
--length_column_name="input_length" \
--save_steps="200" \
--eval_steps="100" \
--save_total_limit="3" \
--target_language="swe" \
--gradient_checkpointing \
--chars_to_ignore , ? . ! - \; \: \" “ % ‘ ” � \
--fp16 \
--group_by_length \
--do_train --do_eval \
--push_to_hub
```
Now you should have both `adapter.tur.safetensors` and `adapter.swe.safetensors` in the model repo
and you can load the respective language with:
```py
model.load_adapter("tur") # or "swe"
```
respectively.
## Sequence to Sequence
The script [`run_speech_recognition_seq2seq.py`](https://github.com/huggingface/transformers/blob/main/examples/pytorch/speech-recognition/run_speech_recognition_seq2seq.py) can be used to fine-tune any [Speech Sequence-to-Sequence Model](https://huggingface.co/docs/transformers/main/en/model_doc/auto#transformers.AutoModelForSpeechSeq2Seq) for automatic speech
recognition on one of the [official speech recognition datasets](https://huggingface.co/datasets?task_ids=task_ids:automatic-speech-recognition) or a custom dataset. This includes the Whisper model from OpenAI or a warm-started Speech-Encoder-Decoder Model, examples for which are included below.
### Whisper Model
We can load all components of the Whisper model directly from the pretrained checkpoint, including the pretrained model weights, feature extractor and tokenizer. We simply have to specify our fine-tuning dataset and training hyperparameters.
#### Single GPU Whisper Training
The following example shows how to fine-tune the [Whisper small](https://huggingface.co/openai/whisper-small) checkpoint on the Hindi subset of [Common Voice 11](https://huggingface.co/datasets/mozilla-foundation/common_voice_11_0) using a single GPU device in half-precision:
```bash
python run_speech_recognition_seq2seq.py \
--model_name_or_path="openai/whisper-small" \
--dataset_name="mozilla-foundation/common_voice_11_0" \
--dataset_config_name="hi" \
--language="hindi" \
--train_split_name="train+validation" \
--eval_split_name="test" \
--max_steps="5000" \
--output_dir="./whisper-small-hi" \
--per_device_train_batch_size="16" \
--gradient_accumulation_steps="2" \
--per_device_eval_batch_size="16" \
--logging_steps="25" \
--learning_rate="1e-5" \
--warmup_steps="500" \
--evaluation_strategy="steps" \
--eval_steps="1000" \
--save_strategy="steps" \
--save_steps="1000" \
--generation_max_length="225" \
--preprocessing_num_workers="16" \
--length_column_name="input_length" \
--max_duration_in_seconds="30" \
--text_column_name="sentence" \
--freeze_feature_encoder="False" \
--gradient_checkpointing \
--group_by_length \
--fp16 \
--overwrite_output_dir \
--do_train \
--do_eval \
--predict_with_generate \
--use_auth_token
```
On a single V100, training should take approximately 8 hours, with a final cross-entropy loss of **1e-4** and word error rate of **32.6%**.
If training on a different language, you should be sure to change the `language` argument. The `language` argument should be omitted for English speech recognition.
#### Multi GPU Whisper Training
The following example shows how to fine-tune the [Whisper small](https://huggingface.co/openai/whisper-small) checkpoint on the Hindi subset of [Common Voice 11](https://huggingface.co/datasets/mozilla-foundation/common_voice_11_0) using 2 GPU devices in half-precision:
```bash
torchrun \
--nproc_per_node 2 run_speech_recognition_seq2seq.py \
--model_name_or_path="openai/whisper-small" \
--dataset_name="mozilla-foundation/common_voice_11_0" \
--dataset_config_name="hi" \
--language="hindi" \
--train_split_name="train+validation" \
--eval_split_name="test" \
--max_steps="5000" \
--output_dir="./whisper-small-hi" \
--per_device_train_batch_size="16" \
--per_device_eval_batch_size="16" \
--logging_steps="25" \
--learning_rate="1e-5" \
--warmup_steps="500" \
--evaluation_strategy="steps" \
--eval_steps="1000" \
--save_strategy="steps" \
--save_steps="1000" \
--generation_max_length="225" \
--preprocessing_num_workers="16" \
--length_column_name="input_length" \
--max_duration_in_seconds="30" \
--text_column_name="sentence" \
--freeze_feature_encoder="False" \
--gradient_checkpointing \
--group_by_length \
--fp16 \
--overwrite_output_dir \
--do_train \
--do_eval \
--predict_with_generate \
--use_auth_token
```
On two V100s, training should take approximately 4 hours, with a final cross-entropy loss of **1e-4** and word error rate of **32.6%**.
### Warm-Started Speech-Encoder-Decoder Model
A very common use case is to leverage a pretrained speech encoder model,
*e.g.* [Wav2Vec2](https://huggingface.co/transformers/main/model_doc/wav2vec2.html), [HuBERT](https://huggingface.co/transformers/main/model_doc/hubert.html) or [XLSR-Wav2Vec2](https://huggingface.co/transformers/main/model_doc/xlsr_wav2vec2.html), with a pretrained text decoder model, *e.g.* [BART](https://huggingface.co/docs/transformers/main/en/model_doc/bart#transformers.BartForCausalLM) or [GPT-2](https://huggingface.co/docs/transformers/main/en/model_doc/gpt2#transformers.GPT2ForCausalLM), to create a [Speech-Encoder-Decoder Model](https://huggingface.co/docs/transformers/main/en/model_doc/speech-encoder-decoder#speech-encoder-decoder-models).
By pairing a pretrained speech model with a pretrained text model, the warm-started model has prior knowledge of both the source audio and target text domains. However, the cross-attention weights between the encoder and decoder are randomly initialised. Thus, the model requires fine-tuning to learn the cross-attention weights and align the encoder mapping with that of the decoder. We can perform this very fine-tuning procedure using the example script.
As an example, let's instantiate a *Wav2Vec2-2-Bart* model with the `SpeechEnocderDecoderModel` framework. First create an empty repo on `hf.co`:
```bash
huggingface-cli repo create wav2vec2-2-bart-base
git clone https://huggingface.co/<your-user-name>/wav2vec2-2-bart-base
cd wav2vec2-2-bart-base
```
Next, run the following script **inside** the just cloned repo:
```python
from transformers import SpeechEncoderDecoderModel, AutoFeatureExtractor, AutoTokenizer, Wav2Vec2Processor
# checkpoints to leverage
encoder_id = "facebook/wav2vec2-base"
decoder_id = "facebook/bart-base"
# load and save speech-encoder-decoder model
# set some hyper-parameters for training and evaluation
model = SpeechEncoderDecoderModel.from_encoder_decoder_pretrained(encoder_id, decoder_id, encoder_add_adapter=True, encoder_feat_proj_dropout=0.0, encoder_layerdrop=0.0, max_length=200, num_beams=5)
model.config.decoder_start_token_id = model.decoder.config.bos_token_id
model.config.pad_token_id = model.decoder.config.pad_token_id
model.config.eos_token_id = model.decoder.config.eos_token_id
model.save_pretrained("./")
# load and save processor
feature_extractor = AutoFeatureExtractor.from_pretrained(encoder_id)
tokenizer = AutoTokenizer.from_pretrained(decoder_id)
processor = Wav2Vec2Processor(feature_extractor, tokenizer)
processor.save_pretrained("./")
```
Finally, we can upload all files:
```bash
git lfs install
git add . && git commit -m "upload model files" && git push
```
and link the official `run_speech_recognition_seq2seq.py` script to the folder:
```bash
ln -s $(realpath <path/to/transformers>/examples/pytorch/speech-recognition/run_speech_recognition_seq2seq.py) ./
```
Note that we have added a randomly initialized _adapter layer_ to `wav2vec2-base` with the argument
`encoder_add_adapter=True`. This adapter sub-samples the output sequence of
`wav2vec2-base` along the time dimension. By default, a single
output vector of `wav2vec2-base` has a receptive field of *ca.* 25ms (*cf.*
Section *4.2* of the [official Wav2Vec2 paper](https://arxiv.org/pdf/2006.11477.pdf)), which represents a little less a single character. On the other hand, BART
makes use of a sentence-piece tokenizer as an input processor, so that a single
hidden vector of `bart-base` represents *ca.* 4 characters. To better align the
receptive field of the *Wav2Vec2* output vectors with *BART*'s hidden-states in the cross-attention
mechanism, we further subsample *Wav2Vec2*'s output by a factor of 8 by
adding a convolution-based adapter.
Having warm-started the speech-encoder-decoder model under `<your-user-name>/wav2vec2-2-bart`, we can now fine-tune it on the task of speech recognition.
In the script [`run_speech_recognition_seq2seq`], we load the warm-started model,
feature extractor, and tokenizer, process a speech recognition dataset,
and subsequently make use of the [`Seq2SeqTrainer`](https://huggingface.co/docs/transformers/main/en/main_classes/trainer#transformers.Seq2SeqTrainer) to train our system.
Note that it is important to align the target transcriptions with the decoder's vocabulary. For example, the [`Librispeech`](https://huggingface.co/datasets/librispeech_asr) dataset only contains captilized letters in the transcriptions,
whereas BART was pretrained mostly on normalized text. Thus, it is recommended to add the argument
`--do_lower_case` to the fine-tuning script when using a warm-started `SpeechEncoderDecoderModel`.
The model is fine-tuned on the standard cross-entropy language modeling
loss for sequence-to-sequence (just like *T5* or *BART* in natural language processing).
---
**NOTE**
If you encounter problems with data preprocessing by setting `--preprocessing_num_workers` > 1,
you might want to set the environment variable `OMP_NUM_THREADS` to 1 as follows:
```bash
OMP_NUM_THREADS=1 python run_speech_recognition_ctc ...
```
If the environment variable is not set, the training script might freeze, *i.e.* see: https://github.com/pytorch/audio/issues/1021#issuecomment-726915239.
---
#### Single GPU Seq2Seq
The following command shows how to fine-tune [XLSR-Wav2Vec2](https://huggingface.co/transformers/main/model_doc/xlsr_wav2vec2.html) on [Common Voice](https://huggingface.co/datasets/common_voice) using a single GPU in half-precision.
```bash
python run_speech_recognition_seq2seq.py \
--dataset_name="librispeech_asr" \
--model_name_or_path="./" \
--dataset_config_name="clean" \
--train_split_name="train.100" \
--eval_split_name="validation" \
--output_dir="./" \
--preprocessing_num_workers="16" \
--length_column_name="input_length" \
--overwrite_output_dir \
--num_train_epochs="5" \
--per_device_train_batch_size="8" \
--per_device_eval_batch_size="8" \
--gradient_accumulation_steps="8" \
--learning_rate="3e-4" \
--warmup_steps="400" \
--evaluation_strategy="steps" \
--text_column_name="text" \
--save_steps="400" \
--eval_steps="400" \
--logging_steps="10" \
--save_total_limit="1" \
--freeze_feature_encoder \
--gradient_checkpointing \
--fp16 \
--group_by_length \
--predict_with_generate \
--generation_max_length="40" \
--generation_num_beams="1" \
--do_train --do_eval \
--do_lower_case
```
On a single V100 GPU, this script should run in *ca.* 5 hours and yield a
cross-entropy loss of **0.405** and word error rate of **0.0728**.
#### Multi GPU Seq2Seq
The following command shows how to fine-tune [XLSR-Wav2Vec2](https://huggingface.co/transformers/main/model_doc/xlsr_wav2vec2.html) on [Common Voice](https://huggingface.co/datasets/common_voice) using 8 GPUs in half-precision.
```bash
torchrun \
--nproc_per_node 8 run_speech_recognition_seq2seq.py \
--dataset_name="librispeech_asr" \
--model_name_or_path="./" \
--dataset_config_name="clean" \
--train_split_name="train.100" \
--eval_split_name="validation" \
--output_dir="./" \
--preprocessing_num_workers="16" \
--length_column_name="input_length" \
--overwrite_output_dir \
--num_train_epochs="5" \
--per_device_train_batch_size="8" \
--per_device_eval_batch_size="8" \
--gradient_accumulation_steps="1" \
--learning_rate="3e-4" \
--warmup_steps="400" \
--evaluation_strategy="steps" \
--text_column_name="text" \
--save_steps="400" \
--eval_steps="400" \
--logging_steps="10" \
--save_total_limit="1" \
--freeze_feature_encoder \
--gradient_checkpointing \
--fp16 \
--group_by_length \
--predict_with_generate \
--do_train --do_eval \
--do_lower_case
```
On 8 V100 GPUs, this script should run in *ca.* 45 minutes and yield a cross-entropy loss of **0.405** and word error rate of **0.0728**
### Examples Seq2Seq
#### Librispeech Seq2Seq
- [Librispeech](https://huggingface.co/datasets/librispeech_asr)
| Dataset | Dataset Config | Pretrained Model | Word error rate on eval | Phoneme error rate on eval | GPU setup | Training time | Fine-tuned Model & Logs | Command to reproduce |
|----------------------------------------------------------------|---------------------------|-----------------------------------------------------------------------------------------------------------------------------------------------------------|-------------------------|----------------------------|------------|---------------|-----------------------------------------------------------------------|-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| [Librispeech](https://huggingface.co/datasets/librispeech_asr) | `"clean"` - `"train.100"` | [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) and [facebook/bart-base](https://huggingface.co/facebook/bart-base) | 0.0728 | - | 8 GPU V100 | 45min | [here](https://huggingface.co/patrickvonplaten/wav2vec2-2-bart-base) | [create_model.py](https://huggingface.co/patrickvonplaten/wav2vec2-2-bart-base/blob/main/create_model.py) & [run.sh](https://huggingface.co/patrickvonplaten/wav2vec2-2-bart-base/blob/main/run_librispeech.sh) |
| [Librispeech](https://huggingface.co/datasets/librispeech_asr) | `"clean"` - `"train.100"` | [facebook/wav2vec2-large-lv60](https://huggingface.co/facebook/wav2vec2-large-lv60) and [facebook/bart-large](https://huggingface.co/facebook/bart-large) | 0.0486 | - | 8 GPU V100 | 1h20min | [here](https://huggingface.co/patrickvonplaten/wav2vec2-2-bart-large) | [create_model.py](https://huggingface.co/patrickvonplaten/wav2vec2-2-bart-large/blob/main/create_model.py) & [run.sh](https://huggingface.co/patrickvonplaten/wav2vec2-2-bart-large/blob/main/run_librispeech.sh) |
| huggingface/transformers/blob/main/examples/pytorch/speech-recognition/README.md |
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# MobileViTV2
## Overview
The MobileViTV2 model was proposed in [Separable Self-attention for Mobile Vision Transformers](https://arxiv.org/abs/2206.02680) by Sachin Mehta and Mohammad Rastegari.
MobileViTV2 is the second version of MobileViT, constructed by replacing the multi-headed self-attention in MobileViT with separable self-attention.
The abstract from the paper is the following:
*Mobile vision transformers (MobileViT) can achieve state-of-the-art performance across several mobile vision tasks, including classification and detection. Though these models have fewer parameters, they have high latency as compared to convolutional neural network-based models. The main efficiency bottleneck in MobileViT is the multi-headed self-attention (MHA) in transformers, which requires O(k2) time complexity with respect to the number of tokens (or patches) k. Moreover, MHA requires costly operations (e.g., batch-wise matrix multiplication) for computing self-attention, impacting latency on resource-constrained devices. This paper introduces a separable self-attention method with linear complexity, i.e. O(k). A simple yet effective characteristic of the proposed method is that it uses element-wise operations for computing self-attention, making it a good choice for resource-constrained devices. The improved model, MobileViTV2, is state-of-the-art on several mobile vision tasks, including ImageNet object classification and MS-COCO object detection. With about three million parameters, MobileViTV2 achieves a top-1 accuracy of 75.6% on the ImageNet dataset, outperforming MobileViT by about 1% while running 3.2× faster on a mobile device.*
This model was contributed by [shehan97](https://huggingface.co/shehan97).
The original code can be found [here](https://github.com/apple/ml-cvnets).
## Usage tips
- MobileViTV2 is more like a CNN than a Transformer model. It does not work on sequence data but on batches of images. Unlike ViT, there are no embeddings. The backbone model outputs a feature map.
- One can use [`MobileViTImageProcessor`] to prepare images for the model. Note that if you do your own preprocessing, the pretrained checkpoints expect images to be in BGR pixel order (not RGB).
- The available image classification checkpoints are pre-trained on [ImageNet-1k](https://huggingface.co/datasets/imagenet-1k) (also referred to as ILSVRC 2012, a collection of 1.3 million images and 1,000 classes).
- The segmentation model uses a [DeepLabV3](https://arxiv.org/abs/1706.05587) head. The available semantic segmentation checkpoints are pre-trained on [PASCAL VOC](http://host.robots.ox.ac.uk/pascal/VOC/).
## MobileViTV2Config
[[autodoc]] MobileViTV2Config
## MobileViTV2Model
[[autodoc]] MobileViTV2Model
- forward
## MobileViTV2ForImageClassification
[[autodoc]] MobileViTV2ForImageClassification
- forward
## MobileViTV2ForSemanticSegmentation
[[autodoc]] MobileViTV2ForSemanticSegmentation
- forward
| huggingface/transformers/blob/main/docs/source/en/model_doc/mobilevitv2.md |
LXMERT DEMO
1. make a virtualenv: ``virtualenv venv`` and activate ``source venv/bin/activate``
2. install reqs: ``pip install -r ./requirements.txt``
3. usage is as shown in demo.ipynb
| huggingface/transformers/blob/main/examples/research_projects/lxmert/README.md |
!---
Copyright 2021 The HuggingFace Team. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
-->
# Language model training examples
The following example showcases how to train a language model from scratch
using the JAX/Flax backend.
JAX/Flax allows you to trace pure functions and compile them into efficient, fused accelerator code on both GPU and TPU.
Models written in JAX/Flax are **immutable** and updated in a purely functional
way which enables simple and efficient model parallelism.
## Masked language modeling
In the following, we demonstrate how to train a bi-directional transformer model
using masked language modeling objective as introduced in [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://arxiv.org/abs/1810.04805).
More specifically, we demonstrate how JAX/Flax can be leveraged
to pre-train [**`roberta-base`**](https://huggingface.co/roberta-base)
in Norwegian on a single TPUv3-8 pod.
The example script uses the 🤗 Datasets library. You can easily customize them to your needs if you need extra processing on your datasets.
To setup all relevant files for training, let's create a directory.
```bash
mkdir ./norwegian-roberta-base
```
### Train tokenizer
In the first step, we train a tokenizer to efficiently process the text input for the model. Similar to how it is shown in [How to train a new language model from scratch using Transformers and Tokenizers](https://huggingface.co/blog/how-to-train), we use a **`ByteLevelBPETokenizer`**.
The tokenizer is trained on the complete Norwegian dataset of OSCAR
and consequently saved in the cloned model directory.
This can take up to 10 minutes depending on your hardware ☕.
```python
from datasets import load_dataset
from tokenizers import trainers, Tokenizer, normalizers, ByteLevelBPETokenizer
# load dataset
dataset = load_dataset("oscar", "unshuffled_deduplicated_no", split="train")
# Instantiate tokenizer
tokenizer = ByteLevelBPETokenizer()
def batch_iterator(batch_size=1000):
for i in range(0, len(dataset), batch_size):
yield dataset[i: i + batch_size]["text"]
# Customized training
tokenizer.train_from_iterator(batch_iterator(), vocab_size=50265, min_frequency=2, special_tokens=[
"<s>",
"<pad>",
"</s>",
"<unk>",
"<mask>",
])
# Save files to disk
tokenizer.save("./norwegian-roberta-base/tokenizer.json")
```
### Create configuration
Next, we create the model's configuration file. This is as simple
as loading and storing [`**roberta-base**`](https://huggingface.co/roberta-base)
in the local model folder:
```python
from transformers import RobertaConfig
config = RobertaConfig.from_pretrained("roberta-base", vocab_size=50265)
config.save_pretrained("./norwegian-roberta-base")
```
Great, we have set up our model repository. During training, we will automatically
push the training logs and model weights to the repo.
### Train model
Next we can run the example script to pretrain the model:
```bash
python run_mlm_flax.py \
--output_dir="./norwegian-roberta-base" \
--model_type="roberta" \
--config_name="./norwegian-roberta-base" \
--tokenizer_name="./norwegian-roberta-base" \
--dataset_name="oscar" \
--dataset_config_name="unshuffled_deduplicated_no" \
--max_seq_length="128" \
--weight_decay="0.01" \
--per_device_train_batch_size="128" \
--per_device_eval_batch_size="128" \
--learning_rate="3e-4" \
--warmup_steps="1000" \
--overwrite_output_dir \
--num_train_epochs="18" \
--adam_beta1="0.9" \
--adam_beta2="0.98" \
--logging_steps="500" \
--save_steps="2500" \
--eval_steps="2500" \
--push_to_hub
```
Training should converge at a loss and accuracy
of 1.78 and 0.64 respectively after 18 epochs on a single TPUv3-8.
This should take less than 18 hours.
Training statistics can be accessed on [tfhub.dev](https://tensorboard.dev/experiment/GdYmdak2TWeVz0DDRYOrrg).
For a step-by-step walkthrough of how to do masked language modeling in Flax, please have a
look at [this](https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/masked_language_modeling_flax.ipynb) google colab.
## Causal language modeling
In the following, we demonstrate how to train an auto-regressive causal transformer model
in JAX/Flax.
More specifically, we pretrain a randomly initialized [**`gpt2`**](https://huggingface.co/gpt2) model in Norwegian on a single TPUv3-8.
to pre-train 124M [**`gpt2`**](https://huggingface.co/gpt2)
in Norwegian on a single TPUv3-8 pod.
The example script uses the 🤗 Datasets library. You can easily customize them to your needs if you need extra processing on your datasets.
To setup all relevant files for training, let's create a directory.
```bash
mkdir ./norwegian-gpt2
```
### Train tokenizer
In the first step, we train a tokenizer to efficiently process the text input for the model. Similar to how it is shown in [How to train a new language model from scratch using Transformers and Tokenizers](https://huggingface.co/blog/how-to-train), we use a **`ByteLevelBPETokenizer`**.
The tokenizer is trained on the complete Norwegian dataset of OSCAR
and consequently saved in the cloned model directory.
This can take up to 10 minutes depending on your hardware ☕.
```python
from datasets import load_dataset
from tokenizers import trainers, Tokenizer, normalizers, ByteLevelBPETokenizer
# load dataset
dataset = load_dataset("oscar", "unshuffled_deduplicated_no", split="train")
# Instantiate tokenizer
tokenizer = ByteLevelBPETokenizer()
def batch_iterator(batch_size=1000):
for i in range(0, len(dataset), batch_size):
yield dataset[i: i + batch_size]["text"]
# Customized training
tokenizer.train_from_iterator(batch_iterator(), vocab_size=50257, min_frequency=2, special_tokens=[
"<s>",
"<pad>",
"</s>",
"<unk>",
"<mask>",
])
# Save files to disk
tokenizer.save("./norwegian-gpt2/tokenizer.json")
```
### Create configuration
Next, we create the model's configuration file. This is as simple
as loading and storing [`**gpt2**`](https://huggingface.co/gpt2)
in the local model folder:
```python
from transformers import GPT2Config
config = GPT2Config.from_pretrained("gpt2", resid_pdrop=0.0, embd_pdrop=0.0, attn_pdrop=0.0, vocab_size=50257)
config.save_pretrained("./norwegian-gpt2")
```
Great, we have set up our model repository. During training, we will now automatically
push the training logs and model weights to the repo.
### Train model
Finally, we can run the example script to pretrain the model:
```bash
python run_clm_flax.py \
--output_dir="./norwegian-gpt2" \
--model_type="gpt2" \
--config_name="./norwegian-gpt2" \
--tokenizer_name="./norwegian-gpt2" \
--dataset_name="oscar" \
--dataset_config_name="unshuffled_deduplicated_no" \
--do_train --do_eval \
--block_size="512" \
--per_device_train_batch_size="64" \
--per_device_eval_batch_size="64" \
--learning_rate="5e-3" --warmup_steps="1000" \
--adam_beta1="0.9" --adam_beta2="0.98" --weight_decay="0.01" \
--overwrite_output_dir \
--num_train_epochs="20" \
--logging_steps="500" \
--save_steps="2500" \
--eval_steps="2500" \
--push_to_hub
```
Training should converge at a loss and perplexity
of 3.24 and 25.72 respectively after 20 epochs on a single TPUv3-8.
This should take less than ~21 hours.
Training statistics can be accessed on [tfhub.de](https://tensorboard.dev/experiment/2zEhLwJ0Qp2FAkI3WVH9qA).
For a step-by-step walkthrough of how to do causal language modeling in Flax, please have a
look at [this](https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/causal_language_modeling_flax.ipynb) google colab.
## T5-like span-masked language modeling
In the following, we demonstrate how to train a T5 model using the span-masked language model
objective as proposed in the [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://arxiv.org/abs/1910.10683).
More specifically, we demonstrate how JAX/Flax can be leveraged
to pre-train [**`google/t5-v1_1-base`**](https://huggingface.co/google/t5-v1_1-base)
in Norwegian on a single TPUv3-8 pod.
The example script uses the 🤗 Datasets library. You can easily customize them to your needs if you need extra processing on your datasets.
Let's start by creating a model repository to save the trained model and logs.
Here we call the model `"norwegian-t5-base"`, but you can change the model name as you like.
To setup all relevant files for training, let's create a directory.
```bash
cd ./norwegian-t5-base
```
### Train tokenizer
In the first step, we train a tokenizer to efficiently process the text input for the model.
We make use of the [tokenizers](https://github.com/huggingface/tokenizers) library to train
a sentencepiece unigram tokenizer as shown in [t5_tokenizer_model.py](https://github.com/huggingface/transformers/tree/main/examples/flax/language-modeling/t5_tokenizer_model.py)
which is heavily inspired from [yandex-research/DeDLOC's tokenizer model](https://github.com/yandex-research/DeDLOC/blob/5c994bc64e573702a9a79add3ecd68b38f14b548/sahajbert/tokenizer/tokenizer_model.py) .
The tokenizer is trained on the complete Norwegian dataset of OSCAR
and consequently saved in the cloned model directory.
This can take up to 120 minutes depending on your hardware ☕☕☕ .
```python
import datasets
from t5_tokenizer_model import SentencePieceUnigramTokenizer
vocab_size = 32_000
input_sentence_size = None
# Initialize a dataset
dataset = datasets.load_dataset("oscar", name="unshuffled_deduplicated_no", split="train")
tokenizer = SentencePieceUnigramTokenizer(unk_token="<unk>", eos_token="</s>", pad_token="<pad>")
# Build an iterator over this dataset
def batch_iterator(input_sentence_size=None):
if input_sentence_size is None:
input_sentence_size = len(dataset)
batch_length = 100
for i in range(0, input_sentence_size, batch_length):
yield dataset[i: i + batch_length]["text"]
# Train tokenizer
tokenizer.train_from_iterator(
iterator=batch_iterator(input_sentence_size=input_sentence_size),
vocab_size=vocab_size,
show_progress=True,
)
# Save files to disk
tokenizer.save("./norwegian-t5-base/tokenizer.json")
```
### Create configuration
Next, we create the model's configuration file. This is as simple
as loading and storing [`**google/t5-v1_1-base**`](https://huggingface.co/google/t5-v1_1-base)
in the local model folder:
```python
from transformers import T5Config
config = T5Config.from_pretrained("google/t5-v1_1-base", vocab_size=tokenizer.get_vocab_size())
config.save_pretrained("./norwegian-t5-base")
```
Great, we have set up our model repository. During training, we will automatically
push the training logs and model weights to the repo.
### Train model
Next we can run the example script to pretrain the model:
```bash
python run_t5_mlm_flax.py \
--output_dir="./norwegian-t5-base" \
--model_type="t5" \
--config_name="./norwegian-t5-base" \
--tokenizer_name="./norwegian-t5-base" \
--dataset_name="oscar" \
--dataset_config_name="unshuffled_deduplicated_no" \
--max_seq_length="512" \
--per_device_train_batch_size="32" \
--per_device_eval_batch_size="32" \
--adafactor \
--learning_rate="0.005" \
--weight_decay="0.001" \
--warmup_steps="2000" \
--overwrite_output_dir \
--logging_steps="500" \
--save_steps="10000" \
--eval_steps="2500" \
--push_to_hub
```
Training should converge at a loss and accuracy
of 2.36 and 57.0 respectively after 3 epochs on a single TPUv3-8.
This should take around 4.5 hours.
Training statistics can be accessed on directly on the 🤗 [hub](https://huggingface.co/patrickvonplaten/t5-base-norwegian/tensorboard)
## BART: Denoising language modeling
In the following, we demonstrate how to train a BART model
using denoising language modeling objective as introduced in [BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension](https://arxiv.org/abs/1910.13461).
More specifically, we demonstrate how JAX/Flax can be leveraged
to pre-train [**`bart-base`**](https://huggingface.co/facebook/bart-base)
in Norwegian on a single TPUv3-8 pod.
The example script uses the 🤗 Datasets library. You can easily customize them to your needs if you need extra processing on your datasets.
To setup all relevant files for training, let's create a directory.
```bash
mkdir ./norwegian-bart-base
```
### Train tokenizer
In the first step, we train a tokenizer to efficiently process the text input for the model. Similar to how it is shown in [How to train a new language model from scratch using Transformers and Tokenizers](https://huggingface.co/blog/how-to-train), we use a **`ByteLevelBPETokenizer`**.
The tokenizer is trained on the complete Norwegian dataset of OSCAR
and consequently saved in the cloned model directory.
This can take up to 10 minutes depending on your hardware ☕.
```python
from datasets import load_dataset
from tokenizers import trainers, Tokenizer, normalizers, ByteLevelBPETokenizer
# load dataset
dataset = load_dataset("oscar", "unshuffled_deduplicated_no", split="train")
# Instantiate tokenizer
tokenizer = ByteLevelBPETokenizer()
def batch_iterator(batch_size=1000):
for i in range(0, len(dataset), batch_size):
yield dataset[i: i + batch_size]["text"]
# Customized training
tokenizer.train_from_iterator(batch_iterator(), vocab_size=50265, min_frequency=2, special_tokens=[
"<s>",
"<pad>",
"</s>",
"<unk>",
"<mask>",
])
# Save files to disk
tokenizer.save("./norwegian-bart-base/tokenizer.json")
```
### Create configuration
Next, we create the model's configuration file. This is as simple
as loading and storing [`**facebook/bart-base**`](https://huggingface.co/facebook/bart-base)
in the local model folder:
```python
from transformers import BartConfig
config = BartConfig.from_pretrained("facebook/bart-base", vocab_size=50265)
config.save_pretrained("./norwegian-bart-base")
```
Great, we have set up our model repository. During training, we will automatically
push the training logs and model weights to the repo.
### Train model
Next we can run the example script to pretrain the model:
```bash
python run_bart_dlm_flax.py \
--output_dir="./norwegian-bart-base" \
--config_name="./norwegian-bart-base" \
--tokenizer_name="./norwegian-bart-base" \
--dataset_name="oscar" \
--dataset_config_name="unshuffled_deduplicated_no" \
--max_seq_length="1024" \
--per_device_train_batch_size="32" \
--per_device_eval_batch_size="32" \
--learning_rate="1e-4" \
--warmup_steps="2000" \
--overwrite_output_dir \
--logging_steps="500" \
--save_steps="2000" \
--eval_steps="2000" \
--push_to_hub
```
Training should converge at a loss and accuracy
of 1.36 and 0.77 respectively after 3 epochs on a single TPUv3-8.
This should take less than 6 hours.
Training statistics can be accessed on [tfhub.dev](https://tensorboard.dev/experiment/Maw62QlaSXWS0MOf2V2lbg/).
## Runtime evaluation
We also ran masked language modeling using PyTorch/XLA on a TPUv3-8, and PyTorch on 8 V100 GPUs. We report the
overall training time below.
For reproducibility, we state the training commands used for PyTorch/XLA and PyTorch further below.
| Task | [TPU v3-8 (Flax)](https://tensorboard.dev/experiment/GdYmdak2TWeVz0DDRYOrrg/) | [TPU v3-8 (Pytorch/XLA)](https://tensorboard.dev/experiment/7Jq1kcQQRAmy12KOdXek7A/)| [8 GPU (PyTorch)](https://tensorboard.dev/experiment/PJneV8FQRxa2unPw1QnVHA) |
|-------|-----------|------------|------------|
| MLM | 15h32m | 23h46m | 44h14m |
*All experiments are ran on Google Cloud Platform.
GPU experiments are ran without further optimizations besides JAX
transformations. GPU experiments are ran with full precision (fp32). "TPU v3-8"
are 8 TPU cores on 4 chips (each chips has 2 cores), while "8 GPU" are 8 GPU chips.
### Script to run MLM with PyTorch/XLA on TPUv3-8
For comparison one can run the same pre-training with PyTorch/XLA on TPU. To set up PyTorch/XLA on Cloud TPU VMs, please
refer to [this](https://cloud.google.com/tpu/docs/pytorch-xla-ug-tpu-vm) guide.
Having created the tokenzier and configuration in `norwegian-roberta-base`, we create the following symbolic links:
```bash
ln -s ~/transformers/examples/pytorch/language-modeling/run_mlm.py ./
ln -s ~/transformers/examples/pytorch/xla_spawn.py ./
```
, set the following environment variables:
```bash
export XRT_TPU_CONFIG="localservice;0;localhost:51011"
unset LD_PRELOAD
export NUM_TPUS=8
export TOKENIZERS_PARALLELISM=0
export MODEL_DIR="./norwegian-roberta-base"
mkdir -p ${MODEL_DIR}
```
, and start training as follows:
```bash
python3 xla_spawn.py --num_cores ${NUM_TPUS} run_mlm.py --output_dir="./runs" \
--model_type="roberta" \
--config_name="${MODEL_DIR}" \
--tokenizer_name="${MODEL_DIR}" \
--dataset_name="oscar" \
--dataset_config_name="unshuffled_deduplicated_no" \
--max_seq_length="128" \
--weight_decay="0.01" \
--per_device_train_batch_size="128" \
--per_device_eval_batch_size="128" \
--learning_rate="3e-4" \
--warmup_steps="1000" \
--overwrite_output_dir \
--num_train_epochs="18" \
--adam_beta1="0.9" \
--adam_beta2="0.98" \
--do_train \
--do_eval \
--logging_steps="500" \
--evaluation_strategy="epoch" \
--report_to="tensorboard" \
--save_strategy="no"
```
### Script to compare pre-training with PyTorch on 8 GPU V100's
For comparison you can run the same pre-training with PyTorch on GPU. Note that we have to make use of `gradient_accumulation`
because the maximum batch size that fits on a single V100 GPU is 32 instead of 128.
Having created the tokenzier and configuration in `norwegian-roberta-base`, we create the following symbolic links:
```bash
ln -s ~/transformers/examples/pytorch/language-modeling/run_mlm.py ./
```
, set some environment variables:
```bash
export NUM_GPUS=8
export TOKENIZERS_PARALLELISM=0
export MODEL_DIR="./norwegian-roberta-base"
mkdir -p ${MODEL_DIR}
```
, and can start training as follows:
```bash
python3 -m torch.distributed.launch --nproc_per_node ${NUM_GPUS} run_mlm.py \
--output_dir="${MODEL_DIR}" \
--model_type="roberta" \
--config_name="${MODEL_DIR}" \
--tokenizer_name="${MODEL_DIR}" \
--dataset_name="oscar" \
--dataset_config_name="unshuffled_deduplicated_no" \
--max_seq_length="128" \
--weight_decay="0.01" \
--per_device_train_batch_size="32" \
--per_device_eval_batch_size="32" \
--gradient_accumulation="4" \
--learning_rate="3e-4" \
--warmup_steps="1000" \
--overwrite_output_dir \
--num_train_epochs="18" \
--adam_beta1="0.9" \
--adam_beta2="0.98" \
--do_train \
--do_eval \
--logging_steps="500" \
--evaluation_strategy="steps" \
--report_to="tensorboard" \
--save_strategy="no"
```
| huggingface/transformers/blob/main/examples/flax/language-modeling/README.md |
!--Copyright 2020 The HuggingFace Team. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
the License. You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
specific language governing permissions and limitations under the License.
⚠️ Note that this file is in Markdown but contain specific syntax for our doc-builder (similar to MDX) that may not be
rendered properly in your Markdown viewer.
-->
# RAG
<div class="flex flex-wrap space-x-1">
<a href="https://huggingface.co/models?filter=rag">
<img alt="Models" src="https://img.shields.io/badge/All_model_pages-rag-blueviolet">
</a>
</div>
## Overview
Retrieval-augmented generation ("RAG") models combine the powers of pretrained dense retrieval (DPR) and
sequence-to-sequence models. RAG models retrieve documents, pass them to a seq2seq model, then marginalize to generate
outputs. The retriever and seq2seq modules are initialized from pretrained models, and fine-tuned jointly, allowing
both retrieval and generation to adapt to downstream tasks.
It is based on the paper [Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks](https://arxiv.org/abs/2005.11401) by Patrick Lewis, Ethan Perez, Aleksandara Piktus, Fabio Petroni, Vladimir
Karpukhin, Naman Goyal, Heinrich Küttler, Mike Lewis, Wen-tau Yih, Tim Rocktäschel, Sebastian Riedel, Douwe Kiela.
The abstract from the paper is the following:
*Large pre-trained language models have been shown to store factual knowledge in their parameters, and achieve
state-of-the-art results when fine-tuned on downstream NLP tasks. However, their ability to access and precisely
manipulate knowledge is still limited, and hence on knowledge-intensive tasks, their performance lags behind
task-specific architectures. Additionally, providing provenance for their decisions and updating their world knowledge
remain open research problems. Pre-trained models with a differentiable access mechanism to explicit nonparametric
memory can overcome this issue, but have so far been only investigated for extractive downstream tasks. We explore a
general-purpose fine-tuning recipe for retrieval-augmented generation (RAG) — models which combine pre-trained
parametric and non-parametric memory for language generation. We introduce RAG models where the parametric memory is a
pre-trained seq2seq model and the non-parametric memory is a dense vector index of Wikipedia, accessed with a
pre-trained neural retriever. We compare two RAG formulations, one which conditions on the same retrieved passages
across the whole generated sequence, the other can use different passages per token. We fine-tune and evaluate our
models on a wide range of knowledge-intensive NLP tasks and set the state-of-the-art on three open domain QA tasks,
outperforming parametric seq2seq models and task-specific retrieve-and-extract architectures. For language generation
tasks, we find that RAG models generate more specific, diverse and factual language than a state-of-the-art
parametric-only seq2seq baseline.*
This model was contributed by [ola13](https://huggingface.co/ola13).
## Usage tips
Retrieval-augmented generation ("RAG") models combine the powers of pretrained dense retrieval (DPR) and Seq2Seq models.
RAG models retrieve docs, pass them to a seq2seq model, then marginalize to generate outputs. The retriever and seq2seq
modules are initialized from pretrained models, and fine-tuned jointly, allowing both retrieval and generation to adapt
to downstream tasks.
## RagConfig
[[autodoc]] RagConfig
## RagTokenizer
[[autodoc]] RagTokenizer
## Rag specific outputs
[[autodoc]] models.rag.modeling_rag.RetrievAugLMMarginOutput
[[autodoc]] models.rag.modeling_rag.RetrievAugLMOutput
## RagRetriever
[[autodoc]] RagRetriever
<frameworkcontent>
<pt>
## RagModel
[[autodoc]] RagModel
- forward
## RagSequenceForGeneration
[[autodoc]] RagSequenceForGeneration
- forward
- generate
## RagTokenForGeneration
[[autodoc]] RagTokenForGeneration
- forward
- generate
</pt>
<tf>
## TFRagModel
[[autodoc]] TFRagModel
- call
## TFRagSequenceForGeneration
[[autodoc]] TFRagSequenceForGeneration
- call
- generate
## TFRagTokenForGeneration
[[autodoc]] TFRagTokenForGeneration
- call
- generate
</tf>
</frameworkcontent>
| huggingface/transformers/blob/main/docs/source/en/model_doc/rag.md |
Robust Speech Challenge 🤗
Welcome to the robust speech recognition challenge 🎙️ !
The goal of this event is to build **robust**, **real-world** speech recognition (ASR) systems in as many languages as possible 🌏🌍🌎.
If necessary and available, free access to a V100S 32 GB GPU will kindly be provided by the [OVHcloud team]( https://www.ovhcloud.com/) 🚀.
This document summarizes all the relevant information required for the speech community event 📋.
To sign-up, please see [this forum post](https://discuss.huggingface.co/t/open-to-the-community-robust-speech-recognition-challenge/13614) 🤗. Please make sure to:
- Read it in detail
- Fill the google form
- Join our Discord server in the #join-sprint channel.
## Table of Contents
- [TLDR;](#tldr)
- [Important dates](#important-dates)
- [How to install pytorch, transformers, datasets](#how-to-install-relevant-libraries)
- [Data and Preprocessing](#data-and-preprocessing)
- [How to fine-tune an acoustic model](#how-to-finetune-an-acoustic-model)
- [How to fine-tune with OVH could](#how-to-finetune-with-ovh-cloud)
- [How to combine n-gram language models with acoustic model](#how-to-combine-n-gram-with-acoustic-model)
- [Evaluation](#evaluation)
- [Prizes](#prizes)
- [Communication and Problems](#communication-and-problems)
- [Talks](#talks)
- [General Tips & Tricks](#general-tips-and-tricks)
## TLDR
Participants are encouraged to leverage pre-trained speech recognition checkpoints,
preferably [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53),
to train a speech recognition system in a language of their choice.
Speech recognition systems should be trained using **PyTorch**, **🤗 Transformers**, and, **🤗 Datasets**.
For more information on how to install the above libraries, please read through
[How to install pytorch, transformers, datasets](#how-to-install-relevant-libraries).
Participants can make use of whatever data they think is useful to build a
speech recognition system for **real-world** audio data -
**except** the Common Voice `"test"` split of their chosen language.
The section [Data and preprocessing](#data-and-preprocessing) explains
in more detail what audio data can be used, how to find suitable audio data, and
how the audio data can be processed.
For training, it is recommended to use the [official training script](https://github.com/huggingface/transformers/blob/main/examples/pytorch/speech-recognition/run_speech_recognition_ctc.py) or a modification thereof. A step-by-step guide on how to fine-tune
an acoustic model for a speech recognition system can be found under [How to fine-tune an acoustic model](#how-to-finetune-an-acoustic-model).
If possible it is encouraged to fine-tune the acoustic models on local GPU machines, but
if those are not available, the OVH could team kindly provides a limited
number of GPUs for the event. Simply fill out [this google form](https://forms.gle/GFZkMkKLiufi75g28) to get access to a GPU.
For more information on how to train an acoustic model on one of OVH's GPU - see [How to fine-tune a speech recognition model with OVHcould](#how-to-fine-tune-with-ovh-cloud).
The performance of speech recognition system can often significantly be improved by adding a
language model for decoding. For more information on how to add a language model, please
take a look at [How to combine n-gram language models with speech recognition models](#how-to-combine-n-gram-with-model).
During the event, the speech recognition system will be evaluated on both the Common Voice `"test"` split
of the participants' chosen language as well as the *real-world* `"dev"` data provided by
the Hugging Face team.
At the end of the robust speech recognition challenge, the speech recognition system will also be evaluated on the
*real-world* `"test"` data provided by the Hugging Face team. Each participant should add an
`eval.py` script to her/his model repository in a specific format that lets one easily
evaluate the speech recognition system on both Common Voice's `"test"` data as well as the *real-world* audio
data. Please read through the [Evaluation](#evaluation) section to make sure your evaluation script is in the correct format. Speech recognition systems
with evaluation scripts in an incorrect format can sadly not be considered for the Challenge.
At the end of the event, the best performing speech recognition system
will receive a prize 🏆 - more information regarding the prizes can be found under [Prizes](#prizes).
We believe that framing the event as a competition is more fun, but at the core, the event is about
creating speech recognition systems in as many languages as possible as a community.
This can be achieved by working together, helping each other to solve bugs, share important findings, etc...🤗
**Note**:
Please, read through the section on [Communication & Problems](#communication-and-problems) to make sure you
know how to ask for help, etc...
All important announcements will be made on discord. Please make sure that
you've joined [this discord channel](https://discord.gg/SHr5wC7m)
Also, please make sure that you have been added to the [Speech Event Organization](https://huggingface.co/speech-recognition-community-v2).
You should have received an invite by email. If you didn't receive an invite, please contact the organizers, *e.g.* Anton, Patrick, or Omar directly on discord.
## Important dates
## Data and preprocessing
In this section, we will quickly go over how to find suitable training data and
how to preprocess it.
To begin with, **all data except Common Voice's `"test"` data can be used as training data.**
The exception includes all Common Voice versions as the test data split of later Common Voice versions often
overlaps with the one of previous versions, *e.g.* the test data of Common Voice 7 in English is
to a big part identical to the test data of Common Voice 6 in English:
```python
load_dataset("mozilla-foundation/common_voice_7_0", "en", split="test")
```
includes more or less the same data as
```python
load_dataset("mozilla-foundation/common_voice_6_1", "en", split="test")
```
However, we strongly encourage participants to make use of Common Voice's other splits, *e.g.* `"train"` and `"validation"`.
For most languages, the Common Voice dataset offers already a decent amount of training data. It is usually
always advantageous to collect additional data. To do so, the participants are in a first step encouraged to search the
Hugging Face Hub for additional audio data, for example by selecting the category
["speech-processing"](https://huggingface.co/datasets?task_categories=task_categories:speech-processing&sort=downloads).
All datasets that are available on the Hub can be downloaded via the 🤗 Datasets library in the same way Common Voice is downloaded.
If one wants to combine multiple datasets for training, it might make sense to take a look at
the [`interleave_datasets`](https://huggingface.co/docs/datasets/package_reference/main_classes?highlight=interleave#datasets.interleave_datasets) function.
In addition, participants can also make use of their audio data. Here, please make sure that you **are allowed to use the audio data**. E.g., if audio data
is taken from media platforms, such as YouTube, it should be verified that the media platform and the owner of the data have given her/his approval to use the audio
data in the context of machine learning research. If you are not sure whether the data you want to use has the appropriate licensing, please contact the Hugging Face
team on discord.
Next, let's talk about preprocessing. Audio data and transcriptions have to be brought into the correct format when
training the acoustic model (example shown in [How to fine-tune an acoustic model](#how-to-finetune-an-acoustic-model)).
It is recommended that this is done by using 🤗 Datasets `.map()` function as shown
[here](https://github.com/huggingface/transformers/blob/9a2dabae7002258e41419491c73dd43ad61b5de7/examples/pytorch/speech-recognition/run_speech_recognition_ctc.py#L444). As can be
see we can pass some characters that will be removed from the transcriptions, *e.g.*: `--chars_to_ignore , ? . ! - \; \: \" “ % ‘ ” � \`
on the official ["Single GPU Example"](https://github.com/huggingface/transformers/tree/main/examples/pytorch/speech-recognition#single-gpu-ctc).
The participants are free to modify this preprocessing by removing more characters or even replacing characters as
it is done in the [official blog post](https://github.com/huggingface/transformers/blob/9a2dabae7002258e41419491c73dd43ad61b5de7/examples/pytorch/speech-recognition/run_speech_recognition_ctc.py#L444).
**However**, there are some rules regarding what characters are allowed to be removed/replaced and which are not.
These rules are not this straightforward and therefore often have to be evaluated case-by-case.
It is allowed (and recommended) to normalize the data to only have lower-case characters. It is also allowed (and recommended) to remove typographical
symbols and punctuation marks. A list of such symbols can *e.g.* be found [here](https://en.wikipedia.org/wiki/List_of_typographical_symbols_and_punctuation_marks) - however here we already must be careful. We should **not** remove a symbol that would change the meaning of the words, *e.g.* in English,
we should not remove the single quotation mark `'` since it would change the meaning of the word `"it's"` to `"its"` which would then be incorrect.
So the golden rule here is to not remove any characters that could change the meaning of a word into another word. This is not always obvious and should
be given some consideration. As another example, it is fine to remove the "Hyphen-minus" sign "`-`" since it doesn't change the
meaning of a word to another one. *E.g.* "`fine-tuning`" would be changed to "`finetuning`" which has still the same meaning.
Since those choices are not always obvious when in doubt feel free to ask on Discord or even better post your question on the forum, as was
done, *e.g.* [here](https://discuss.huggingface.co/t/spanish-asr-fine-tuning-wav2vec2/4586).
## How to install relevant libraries
The following libraries are required to fine-tune a speech model with 🤗 Transformers and 🤗 Datasets in PyTorch.
- [PyTorch](https://pytorch.org/)
- [Transformers](https://github.com/huggingface/transformers)
- [Datasets](https://github.com/huggingface/datasets)
We recommend installing the above libraries in a [virtual environment](https://docs.python.org/3/library/venv.html).
If you're unfamiliar with Python virtual environments, check out the [user guide](https://packaging.python.org/guides/installing-using-pip-and-virtual-environments/). Create a virtual environment with the version of Python you're going
to use and activate it.
You should be able to run the command:
```bash
python3 -m venv <your-venv-name>
```
You can activate your venv by running
```bash
source ~/<your-venv-name>/bin/activate
```
To begin with please make sure you have PyTorch and CUDA correctly installed.
The following command should return ``True``:
```bash
python -c "import torch; print(torch.cuda.is_available())"
```
If the above command doesn't print ``True``, in the first step, please follow the
instructions [here](https://pytorch.org/) to install PyTorch with CUDA.
We strongly recommend making use of the provided PyTorch examples scripts in [transformers/examples/pytorch/speech-recognition](https://github.com/huggingface/transformers/tree/main/examples/pytorch/speech-recognition) to train your speech recognition
system.
In all likelihood, you will adjust one of the example scripts, so we recommend forking and cloning the 🤗 Transformers repository as follows.
1. Fork the [repository](https://github.com/huggingface/transformers) by
clicking on the 'Fork' button on the repository's page. This creates a copy of the code
under your GitHub user account.
2. Clone your fork to your local disk, and add the base repository as a remote:
```bash
$ git clone https://github.com/<your Github handle>/transformers.git
$ cd transformers
$ git remote add upstream https://github.com/huggingface/transformers.git
```
3. Create a new branch to hold your development changes. This is especially useful to share code changes with your team:
```bash
$ git checkout -b a-descriptive-name-for-my-project
```
4. Set up a PyTorch environment by running the following command your virtual environment:
```bash
$ pip install -e ".[torch-speech]"
```
(If transformers was already installed in the virtual environment, remove
it with `pip uninstall transformers` before reinstalling it in editable
mode with the `-e` flag.)
If you have already cloned that repo, you might need to `git pull` to get the most recent changes in the `transformers`
library.
Running this command will automatically install `torch` and the most relevant
libraries required for fine-tuning a speech recognition system.
Next, you should also install the 🤗 Datasets library. We strongly recommend installing the
library from source to profit from the most current additions during the community week.
Simply run the following steps:
```
$ cd ~/
$ git clone https://github.com/huggingface/datasets.git
$ cd datasets
$ pip install -e ".[streaming]"
```
If you plan on contributing a specific dataset during
the community week, please fork the datasets repository and follow the instructions
[here](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-create-a-pull-request).
To verify that all libraries are correctly installed, you can run the following command in a Python shell.
It verifies that both `transformers` and `datasets` have been correclty installed.
```python
from transformers import AutoModelForCTC, AutoProcessor
from datasets import load_dataset
dummy_dataset = load_dataset("common_voice", "ab", split="test")
model = AutoModelForCTC.from_pretrained("hf-internal-testing/tiny-random-wav2vec2")
model.to("cuda")
processor = AutoProcessor.from_pretrained("hf-internal-testing/tiny-random-wav2vec2")
input_values = processor(dummy_dataset[0]["audio"]["array"], return_tensors="pt", sampling_rate=16_000).input_values
input_values = input_values.to("cuda")
logits = model(input_values).logits
assert logits.shape[-1] == 32
```
## How to finetune an acoustic model
In this section, we show you how to fine-tune a pre-trained [XLS-R Model](https://huggingface.co/docs/transformers/model_doc/xls_r) on the [Common Voice 7 dataset](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
We recommend fine-tuning one of the following pre-trained XLS-R checkpoints:
- [300M parameters version](https://huggingface.co/facebook/wav2vec2-xls-r-300m)
- [1B parameters version](https://huggingface.co/facebook/wav2vec2-xls-r-1b)
- [2B parameters version](https://huggingface.co/facebook/wav2vec2-xls-r-2b)
To begin with, please note that to use the Common Voice dataset, you
have to accept that **your email address** and **username** are shared with the
mozilla-foundation. To get access to the dataset please click on "*Access repository*" [here](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
Next, we recommended that you get familiar with the XLS-R model and its capabilities.
In collaboration with [Fairseq's Wav2Vec2 team](https://github.com/pytorch/fairseq/tree/main/examples/wav2vec),
we've written ["Fine-tuning XLS-R for Multi-Lingual ASR with 🤗 Transformers"](https://huggingface.co/blog/fine-tune-xlsr-wav2vec2) which gives an in-detail explanation of how XLS-R functions and how it can be fine-tuned.
The blog can also be opened and directly fine-tuned in a google colab notebook.
In this section, we will explain how to fine-tune the model on a local machine.
1. **Log in**
To begin with, you should check that you are correctly logged in and that you have `git-lfs` installed so that your fine-tuned model can automatically be uploaded.
Run:
```bash
huggingface-cli login
```
to login. It is recommended to login with your access token that can be found under your hugging face profile (icon in the top right corner on [hf.co](http://hf.co/), then Settings -> Access Tokens -> User Access Tokens -> New Token (if haven't generated one already)
You can then copy-paste this token to log in locally.
2. **Create your model repository**
First, let's make sure that `git-lfs` is correctly installed. To so, simply run:
```bash
git-lfs -v
```
The output should show something like `git-lfs/2.13.2 (GitHub; linux amd64; go 1.15.4)`. If your console states that the `git-lfs` command was not found, please make
sure to install it [here](https://git-lfs.github.com/) or simply via:
```bash
sudo apt-get install git-lfs
```
Now you can create your model repository which will contain all relevant files to
reproduce your training. You can either directly create the model repository on the
Hub (Settings -> New Model) or via the CLI. Here we choose to use the CLI instead.
Assuming that we want to call our model repository *xls-r-ab-test*, we can run the
following command:
```bash
huggingface-cli repo create xls-r-ab-test
```
You can now see the model on the Hub, *e.g.* under https://huggingface.co/hf-test/xls-r-ab-test .
Let's clone the repository so that we can define our training script inside.
```bash
git lfs install
git clone https://huggingface.co/hf-test/xls-r-ab-test
```
3. **Add your training script and `run`-command to the repository**
We encourage participants to add all relevant files for training directly to the
directory so that everything is fully reproducible.
Let's first copy-paste the official training script from our clone
of `transformers` to our just created directory:
```bash
cp ~/transformers/examples/pytorch/speech-recognition/run_speech_recognition_ctc.py ./
```
Next, we'll create a bash file to define the hyper-parameters and configurations
for training. More detailed information on different settings (single-GPU vs. multi-GPU) can be found [here](https://github.com/huggingface/transformers/tree/main/examples/pytorch/speech-recognition#connectionist-temporal-classification).
For demonstration purposes, we will use a dummy XLS-R model `model_name_or_path="hf-test/xls-r-dummy"` on the very low-resource language of "Abkhaz" of [Common Voice 7](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0): `dataset_config_name="ab"` for just a single epoch.
Before starting to train, let's make sure we have installed all the required libraries. You might want to run:
```bash
pip install -r ~/transformers/examples/pytorch/speech-recognition/requirements.txt
```
Alright, finally we can define the training script. We'll simply use some
dummy hyper-parameters and configurations for demonstration purposes.
Note that we add the flag `--use_auth_token` so that datasets requiring access,
such as [Common Voice 7](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0) can be downloaded. In addition, we add the `--push_to_hub` flag to make use of the
[Trainers `push_to-hub` functionality](https://huggingface.co/docs/transformers/main/en/main_classes/trainer#transformers.Trainer.push_to_hub) so that your model will be automatically uploaded to the Hub.
Let's copy the following code snippet in a file called `run.sh`
```bash
echo '''python run_speech_recognition_ctc.py \
--dataset_name="mozilla-foundation/common_voice_7_0" \
--model_name_or_path="hf-test/xls-r-dummy" \
--dataset_config_name="ab" \
--output_dir="./" \
--overwrite_output_dir \
--max_steps="10" \
--per_device_train_batch_size="2" \
--learning_rate="3e-4" \
--save_total_limit="1" \
--evaluation_strategy="steps" \
--text_column_name="sentence" \
--length_column_name="input_length" \
--save_steps="5" \
--layerdrop="0.0" \
--freeze_feature_encoder \
--gradient_checkpointing \
--fp16 \
--group_by_length \
--push_to_hub \
--use_auth_token \
--do_train --do_eval''' > run.sh
```
4. **Start training**
Now all that is left to do is to start training the model by executing the
run file.
```bash
bash run.sh
```
The training should not take more than a couple of minutes.
During the training intermediate saved checkpoints are automatically uploaded to
your model repository as can be seen [on this commit](https://huggingface.co/hf-test/xls-r-ab-test/commit/0eb19a0fca4d7d163997b59663d98cd856022aa6) .
At the end of the training, the [Trainer](https://huggingface.co/docs/transformers/main/en/main_classes/trainer) automatically creates a nice model card and all
relevant files are uploaded.
5. **Tips for real model training**
The above steps illustrate how a model can technically be fine-tuned.
However as you can see on the model card [hf-test/xls-r-ab-test](https://huggingface.co/hf-test/xls-r-ab-test), our demonstration has a very poor performance which is
not surprising given that we trained for just 10 steps on a randomly initialized
model.
For real model training, it is recommended to use one of the actual pre-trained XLS-R models:
- [300M parameters version](https://huggingface.co/facebook/wav2vec2-xls-r-300m)
- [1B parameters version](https://huggingface.co/facebook/wav2vec2-xls-r-1b)
- [2B parameters version](https://huggingface.co/facebook/wav2vec2-xls-r-2b)
Also, the hyper-parameters should be carefully chosen depending on the dataset.
As an example, we will fine-tune the 300M parameters model on Swedish on a single
TITAN RTX 24GB GPU.
The model will be called `"xls-r-300m-sv"`.
Following the above steps we first create the model:
```bash
huggingface-cli repo create xls-r-300m-sv
```
, clone it locally (assuming the `<username>` is `hf-test`)
```bash
git clone hf-test/xls-r-300m-sv
```
, and, define the following hyperparameters for training
```bash
echo '''python run_speech_recognition_ctc.py \
--dataset_name="mozilla-foundation/common_voice_7_0" \
--model_name_or_path="facebook/wav2vec2-xls-r-300m" \
--dataset_config_name="sv-SE" \
--output_dir="./" \
--overwrite_output_dir \
--num_train_epochs="50" \
--per_device_train_batch_size="8" \
--per_device_eval_batch_size="8" \
--gradient_accumulation_steps="4" \
--learning_rate="7.5e-5" \
--warmup_steps="2000" \
--length_column_name="input_length" \
--evaluation_strategy="steps" \
--text_column_name="sentence" \
--chars_to_ignore , ? . ! \- \; \: \" “ % ‘ ” � — ’ … – \
--save_steps="500" \
--eval_steps="500" \
--logging_steps="100" \
--layerdrop="0.0" \
--activation_dropout="0.1" \
--save_total_limit="3" \
--freeze_feature_encoder \
--feat_proj_dropout="0.0" \
--mask_time_prob="0.75" \
--mask_time_length="10" \
--mask_feature_prob="0.25" \
--mask_feature_length="64" \
--gradient_checkpointing \
--use_auth_token \
--fp16 \
--group_by_length \
--do_train --do_eval \
--push_to_hub''' > run.sh
```
The training takes *ca.* 7 hours and yields a reasonable test word
error rate of 27% as can be seen on the automatically generated [model card](https://huggingface.co/hf-test/xls-r-300m-sv).
The above-chosen hyperparameters probably work quite well on a range of different
datasets and languages but are by no means optimal. It is up to you to find a good set of
hyperparameters.
## How to finetune with OVH cloud
[](https://youtu.be/XkMnYocAEO0) For a more detailed guide on setting up OVHcloud please watch this video: https://youtu.be/XkMnYocAEO0
### Creating an OVHCloud account
*TIP*: If you haven't created a project on OVHcloud yet, make sure you've received your GPU voucher code *beforehand*,
so that you can skip entering the credit card information.
1. If you're a US citizen, create an account via [OVHcloud.CA](https://ovhcloud.ca/).
If you're from anywhere else in the world, create an account via [OVHcloud.COM](https://ovhcloud.com/).
2. Once logged in, click `Public Cloud` from the top menu and then click `Create your first OVH Public Cloud project`.
Then enter a project name (e.g. "huggingface"), enter your voucher code, and click `Continue` -> `Create my project`.
*Note: if you see a request for credit card details during the last step, and you can't skip it, then your voucher code
is invalid. Please report it to the [#ovh-support](https://discord.gg/p4qqDV3M) channel on Discord.*
### Setting up an AI notebook
1. Go to the `Public Cloud` page and select `Project Management` -> `Users & Roles` from the menu on the left.
2. Click `+ Add user`. Write a user description (e.g. `AI Trainer`), and select an `AI Training Operator` user role.
Click `Confirm`.
3. Write down the *username* and *password* (at the top of the screen) somewhere. They will be needed during step 7.
4. Select `AI & Machine Learning` -> `AI Training` from the menu on the left.
Click `+ Launch a new job` on the AI Training page.
5. On the `Launch a new job` page:
* In `1. Choose a region` select a region closest to you.
* In `2. Enter the Docker image` select `Custom image` -> `baaastijn/ovh_huggingface`.
* You can skip steps `3.` and `4.` if you will be using the Hugging Face Hub to store the models after training.
* In `5. Configure your job` select **1** `GPU`.
* Validate the info and Create the job.
6. On the `AI Training Jobs` screen wait until the job's status changes from `Pending` to `Running`.
7. Click `HTTP Access` from the Job's details page and log in with the AI training user you've created earlier.
Once logged in, you can close the page and click `HTTP Access` to launch a JupyterLab notebook.
8. Awesome, now you have a free GPU-enabled Jupyter instance!
**Note**: If you're an experienced Docker user, feel free to create a custom docker image with all of the needed packages
like the one in step 5. The Dockerfile for it is available here:
[baaastijn/Dockerimages](https://github.com/baaastijn/Dockerimages/tree/main/Hugginface_challenge_speech).
Once you've built your image, push it to https://hub.docker.com/ and select it during the OVHcloud job creation.
For more quick tutorials about OVHcloud AI products, check out the showcase https://vimeo.com/showcase/8903300
## How to combine n-gram with acoustic model
Having trained a speech recognition model with CTC as shown in the section above,
one can further improve the model's performance by adding an **n-gram language model**
to the decoding process of the model. By doing so, we are replacing the naive greedy decoding
with **n-gram-boosted** beam search decoding.
N-gram language models can be built on CPU in just a few minutes. *N-gram-boosted* beam search decoding noticeably slows down the
inference time, but also yields significant word error rates improvements - usually between 10-40 %.
You can find an in-detail blog post on how to build an *n-gram* [here](https://huggingface.co/blog/wav2vec2-with-ngram).
The blog post can be opened in a google colab and by adapting three lines of the example for your use case, one can directly
create an *n-gram* in the google colab.
The blog post gives in-detail instructions on how to build an n-gram and how to add it to your trained speech recognition model.
- why one should add an *n-gram* to her/his speech recognition system,
- how to build an *n-gram*, and,
- how to add the built *n-gram* the speech recognition system for seamless decoding
Our previously trained model - [xls-r-300m-sv](https://huggingface.co/hf-test/xls-r-300m-sv) - enjoys a 30% word error rate reduction after
having added an n-gram. As shown in the example of the blog post, we strongly advise participants to upload all files required for combining
the *n-gram* with a trained speech recognition model directly into the same model repository.
## Evaluation
Finally, we have arrived at the most fun part of the challenge - sitting back and
watching the model transcribe audio. If possible, every participant should evaluate
the speech recognition system on the test set of Common Voice 7 and
ideally also on the real-world audio data (if available).
For languages that have neither a Common Voice evaluation dataset nor a real world
evaluation dataset, please contact the organizers on Discord so that we can work
together to find some evaluation data.
As a first step, one should copy the official `eval.py` script to her/his model
repository. Let's use our previously trained [xls-r-300m-sv](https://huggingface.co/hf-test/xls-r-300m-sv) again as an example.
Assuming that we have a clone of the model's repo under `~/xls-r-300m-sv`, we can
copy the `eval.py` script to the repo.
```bash
cp ~/transformers/examples/research_projects/robust-speech-event/eval.py ~/xls-r-300m-sv
```
Next, we should adapt `eval.py` so that it fits our evaluation data. Here it is
important to keep the `eval.py` file in the following format:
- 1. The following input arguments should not be changed and keep their original functionality/meaning (being to load the model and dataset): `"--model_id"`, `"--dataset"`, `"--config"`, `"--split"`. We recommend to not change any of the code written under `if __name__ == "__main__":`.
- 2. The function `def log_results(result: Dataset, args: Dict[str, str])` should also not be changed. The function expects the above names attached to the `args` object as well as a `datasets.Dataset` object, called `result` which includes all predictions and target transcriptions under the names `"predictions"` and `"targets"` respectively.
- 3. All other code can be changed and adapted. Participants are especially invited to change the `def normalize_text(text: str) -> str:` function as this might be a very language and model-training specific function.
- 4. **Important**: It is not allowed to "cheat" in any way when in comes to pre-and postprocessing. In short, "cheating" refers to any of the following:
- a. Somehow giving the model access to the target transcriptions to improve performance. The model is not allowed to use the target transcriptions to generate its predictions.
- b. Pre-processing the target transcriptions in a way that makes the target transcriptions lose their original meaning. This corresponds to what has already been said in [Data and Preprocessing](#data-and-preprocessing) and is somewhat of a grey zone. It means that one should not remove characters that would make a word to lose its meaning. E.g., it is not allowed to replace all `e` in English with `i` and simply make the model learn that `e` and `i` are the same letter for a better word error rate. This would destroy the meaning of words such as `fell -> fill`. However, it is totally fine to normalize (*e.g.* lowercase) all letters, remove punctuation. There can be a lot of language-specific exceptions and in case you are not sure whether your target transcription pre-processing is allowed, please ask on the Discord channel.
Uff, that was a lot of text describing how to make sure your `eval.py` script
is in the correct format. If you have any questions, please ask openly in Discord.
Great, now that we have adapted the `eval.py` script, we can lean back and run the
evaluation.
First, one should evaluate the model on Common Voice 7's test data. This might
already have been done for your acoustic model during training but in case you
added an *n-gram* language model after having fine-tuned the acoustic model, you
should now see a nice improvement.
The command to evaluate our test model [xls-r-300m-sv](https://huggingface.co/hf-test/xls-r-300m-sv) on Common Voice 7's test data is the following:
```bash
cd xls-r-300m-sv
./eval.py --model_id ./ --dataset mozilla-foundation/common_voice_7_0 --config sv-SE --split test --log_outputs
```
To log each of the model's predictions with the target transcriptions, you can just
add the `--log_outputs` flag.
Running this command should automatically create the file:
`mozilla-foundation_common_voice_7_0_sv-SE_test_eval_results.txt` that contains
both the word- and character error rate.
In a few days, we will give everybody access to some real-world audio data for as many languages as possible.
If your language has real-world audio data, it will most likely have audio input
of multiple minutes. 🤗Transformer's [ASR pipeline](https://huggingface.co/docs/transformers/main/en/main_classes/pipelines#transformers.AutomaticSpeechRecognitionPipeline) supports audio chunking out-of-the-box. You only need to specify
how song each audio chunk should be (`chunk_length_s`) and how much audio stride
(`stride_length_s`) each chunk should use.
For more information on the chunking works, please have a look at [this nice blog post](TODO: ).
In the case of `xls-r-300m-sv`, the following command can be run:
```bash
cd xls-r-300m-sv
./eval.py --model_id hf-test/xls-r-300m-sv --dataset <to-be-announced> --config sv --split validation --chunk_length_s 5.0 --stride_length_s 1.0 --log_outputs
```
Great, now you should have successfully evaluated your model. Finally, there is one
**important** thing you should do so that your model is taken into account
for the final evaluation. You should add two tags to your model, one being `robust-speech-event`, one being the ISO code of your chosen language, *e.g.* `"sv"` for the
exemplary model we used above. You can find a list of all available languages and
their ISO code [here](https://huggingface.co/languages).
To add the tags, simply edit the README.md of your model repository and add
```
- "sv"
- "robust-speech-event"
```
under `tags:` as done [here](https://huggingface.co/hf-test/xls-r-300m-sv/commit/a495fd70c96bb7d019729be9273a265c2557345e).
To verify that you've added the tags correctly make sure that your model
appears when clicking on [this link](https://huggingface.co/models?other=robust-speech-event).
Great that's it! This should give you all the necessary information to evaluate
your model. For the final evaluation, we will verify each evaluation result to
determine the final score and thereby the winning models for each language.
The final score is calculated as follows:
```bash
FINAL_SCORE = 1/3 * WER_Common_Voice_7_test + 1/3 * WER_REAL_AUDIO_DEV + 1/3 * WER_REAL_AUDIO_TEST
```
The dataset `WER_REAL_AUDIO_TEST` is hidden and will only be published
at the end of the robust speech challenge.
If there is no real audio data for your language the final score will be
computed solely based on the Common Voice 7 test dataset. If there is also
no Common Voice 7 test dataset for your language, we will see together how to
score your model - if this is the case, please don't be discouraged. We are
especially excited about speech recognition systems of such low-resource
languages and will make sure that we'll decide on a good approach to evaluating
your model.
## Prizes
TODO(Patrick, Omar, ...)
## Communication and Problems
If you encounter any problems or have any questions, you should use one of the following platforms
depending on your type of problem. Hugging Face is an "open-source-first" organization meaning
that we'll try to solve all problems in the most public and most transparent way possible so that everybody
in the community profits.
The following table summarizes what platform to use for which problem.
- Problem/question/bug with the 🤗 Datasets library that you think is a general problem that also impacts other people, please open an [Issues on Datasets](https://github.com/huggingface/datasets/issues/new?assignees=&labels=bug&template=bug-report.md&title=) and ping @anton-l and @patrickvonplaten.
- Problem/question/bug with the 🤗 Transformers library that you think is a general problem that also impacts other people, please open an [Issues on Transformers](https://github.com/huggingface/transformers/issues/new?assignees=&labels=&template=bug-report.md&title=) and ping @anton-l and @patrickvonplaten.
- Problem/question with a modified, customized training script that is less likely to impact other people, please post your problem/question [on the forum](https://discuss.huggingface.co/) and ping @anton-l and @patrickvonplaten.
- Questions regarding access to the OVHcloud GPU, please ask in the Discord channel **#ovh-support**.
- Other questions regarding the event, rules of the event, or if you are not sure where to post your question, please ask in the Discord channel **#sprint-discussions**.
## Talks
We are very excited to be hosting 2 days of talks from Kensho-Technologies, Mozilla's Common Voice, Meta AI Research and Hugging Face.
### Thursday, January 20th
Speaker | Topic | Time | Video |
|-------------|---------------------------------|------------------------|------------------------|
| Patrick von Platen, Hugging Face | Introduction to Robust Speech Challenge | 4h30pm - 5h00pm UTC | [](https://www.youtube.com/watch?v=X9e5Tto-Iuk)
| Raymond Grossman and Jeremy Lopez, Kensho-Technologies | Pyctcdecode & Speech2text decoding | 5h30pm - 6h00pm UTC | [](https://www.youtube.com/watch?v=mp7fHMTnK9A)
### Friday, January 21th
Speaker | Topic | Time | Video |
|-------------|---------------------------------|------------------------|------------------------|
| Gabriel Habayeb, Mozilla Common Voice | Unlocking global speech with Mozilla Common Voice | 4h30pm - 5h00pm UTC | [](https://www.youtube.com/watch?v=Vvn984QmAVg)
| Changhan Wang, Meta AI Research | XLS-R: Large-Scale Cross-lingual Speech Representation Learning on 128 Languages | 5h30pm - 6h00pm UTC | [](https://www.youtube.com/watch?v=ic_J7ZCROBM)
### Talks & Speakers
#### Patrick von Platen, Research Engineer, Hugging Face
- Talk: Introduction to Robust Speech Challenge
- Abstract: In this talk, Patrick outlines the Robust Speech Challenge and gives tips and tricks on how to train and evaluate speech recognition systems with 🤗 Transformers and 🤗 Datasets, and PyTorch.
- Speaker info: Patrick von Platen is a research engineer at Hugging Face and one of the core maintainers of the popular Transformers library. He specializes in speech recognition, encoder-decoder models, and long-range sequence modeling. Before joining Hugging Face, Patrick researched speech recognition at Uber AI, Cambridge University, and RWTH Aachen University.
#### Raymond Grossman, Jeremy Lopez, Machine Learning Engineer, Kensho Technologies
- Talk: PyCTCDecode & Speech2text decoding
- Abstract: PyCTCDecode is a fast and feature-rich CTC beam search decoder for speech recognition written in Python, providing n-gram (kenlm) language model support similar to PaddlePaddle's decoder, but incorporating many new features such as byte pair encoding and real-time decoding to support models like Nvidia's Conformer-CTC or Facebook's Wav2Vec2.
- Speaker info :
- Raymond works as a machine learning engineer at Kensho Technologies, specializing in speech and natural language domains. Before coming to Kensho, he studied mathematics at Princeton and was an avid Kaggler under the moniker @ToTrainThemIsMyCause.
- Jeremy is a machine learning engineer at Kensho Technologies and has worked on a variety of different topics including search and speech recognition. Before working at Kensho, he earned a PhD in experimental particle physics at MIT and continued doing physics research as a postdoc at the University of Colorado Boulder.
#### Gabriel Habayeb, Data Engineer, Common Voice @ Mozilla
- Talk: Unlocking global speech with Mozilla Common Voice
- Abstract: Hear from Common Voice Data Engineer Gabriel Habayeb (Mozilla Foundation) as he talks about how Common Voice makes it easy to crowdsource voice data in global languages, as well as getting key insights into the dataset itself, how we maintain quality, use metadata - and our plans for the future!
- Speaker info: Gabriel is a software developer with the Common Voice team at the Mozilla Foundation with a focus on data engineering. Before joining the Foundation, he spent the last six years working across different industries, including education, enterprise and not-for-profit organizations.
#### Changhan Wang, Main author of XLS-R and Research Engineer, Meta AI Research
- Talk: XLS-R: Large-Scale Cross-lingual Speech Representation Learning on 128 Languages
- Abstract: In this talk, Changhan will present XLS-R, a large-scale model for cross-lingual speech representation learning based on wav2vec 2.0. XLS-R has up to 2B parameters and was trained on nearly half a million hours of publicly available speech audio in 128 languages, an order of magnitude more public data than the largest known prior work. On the CoVoST-2 speech translation benchmark, XLS-R improves the previous state of the art by an average of 7.4 BLEU over 21 translation directions into English. For speech recognition, XLS-R improves over the best known prior work on BABEL, MLS, CommonVoice as well as VoxPopuli, lowering error rates by 14-34% relative on average. XLS-R also sets a new state of the art on VoxLingua107 language identification. The XLS-R team hopes to work together with the open-source community to improve speech processing tasks for many more languages of the world.
## General Tips and Tricks
- Memory efficient training:
In case, you are getting out-of-memory errors on your GPU, we recommend to use
[bitsandbytes](https://github.com/TimDettmers/bitsandbytes) to replace the
native memory-intensive Adam optimizer with the one of `bitsandbytes`. You
can simply run the script `./run_speech_recognition_ctc_bnb.py` provided in this
folder that makes use of `bitsandbytes` instead of the official one.
- Dataset streaming
TODO(Patrick)
| huggingface/transformers/blob/main/examples/research_projects/robust-speech-event/README.md |
!--Copyright 2022 The HuggingFace Team. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
the License. You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
specific language governing permissions and limitations under the License.
⚠️ Note that this file is in Markdown but contain specific syntax for our doc-builder (similar to MDX) that may not be
rendered properly in your Markdown viewer.
-->
# GPT-Sw3
## Overview
The GPT-Sw3 model was first proposed in
[Lessons Learned from GPT-SW3: Building the First Large-Scale Generative Language Model for Swedish](http://www.lrec-conf.org/proceedings/lrec2022/pdf/2022.lrec-1.376.pdf)
by Ariel Ekgren, Amaru Cuba Gyllensten, Evangelia Gogoulou, Alice Heiman, Severine Verlinden, Joey Öhman,
Fredrik Carlsson, Magnus Sahlgren.
Since that first paper the authors have extended their work and trained new models on their new 1.2TB corpora named The Nordic Pile.
GPT-Sw3 is a collection of large decoder-only pretrained transformer language models that were developed by AI Sweden
in collaboration with RISE and the WASP WARA for Media and Language. GPT-Sw3 has been trained on a dataset containing
320B tokens in Swedish, Norwegian, Danish, Icelandic, English, and programming code. The model was pretrained using a
causal language modeling (CLM) objective utilizing the NeMo Megatron GPT implementation.
This model was contributed by [AI Sweden](https://huggingface.co/AI-Sweden).
## Usage example
```python
>>> from transformers import AutoTokenizer, AutoModelForCausalLM
>>> tokenizer = AutoTokenizer.from_pretrained("AI-Sweden/gpt-sw3-356m")
>>> model = AutoModelForCausalLM.from_pretrained("AI-Sweden/gpt-sw3-356m")
>>> input_ids = tokenizer("Träd är fina för att", return_tensors="pt")["input_ids"]
>>> generated_token_ids = model.generate(inputs=input_ids, max_new_tokens=10, do_sample=True)[0]
>>> print(tokenizer.decode(generated_token_ids))
Träd är fina för att de är färgstarka. Men ibland är det fint
```
## Resources
- [Text classification task guide](../tasks/sequence_classification)
- [Token classification task guide](../tasks/token_classification)
- [Causal language modeling task guide](../tasks/language_modeling)
<Tip>
The implementation uses the `GPT2Model` coupled with our `GPTSw3Tokenizer`. Refer to [GPT2Model documentation](gpt2)
for API reference and examples.
Note that sentencepiece is required to use our tokenizer and can be installed with `pip install transformers[sentencepiece]` or `pip install sentencepiece`
</Tip>
## GPTSw3Tokenizer
[[autodoc]] GPTSw3Tokenizer
- save_vocabulary
| huggingface/transformers/blob/main/docs/source/en/model_doc/gpt-sw3.md |
!---
Copyright 2021 The HuggingFace Team. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
-->
# Bart + Beam Search to ONNX
Author: [@fatcat-z](https://github.com/fatcat-z)
This folder contains an example of exporting Bart + Beam Search generation (`BartForConditionalGeneration`) to ONNX.
Beam Search contains a for-loop workflow, so we need to make them TorchScript-compatible for exporting to ONNX. This example shows how to make a Bart model be TorchScript-compatible by wrapping up it into a new model. In addition, some changes were made to the `beam_search()` function to make it TorchScript-compatible.
## How to run the example
To make sure you can successfully run the latest versions of the example scripts, you have to **install the library from source** and install some example-specific requirements. To do this, execute the following steps in a new virtual environment:
```bash
git clone https://github.com/huggingface/transformers
cd transformers
pip install '.[onnxruntime]'
```
Then cd in this example folder and run
```bash
pip install -r requirements.txt
```
Now you can run the example command below to get the example ONNX file:
```bash
python run_onnx_exporter.py --model_name_or_path facebook/bart-base
```
| huggingface/transformers/blob/main/examples/research_projects/onnx/summarization/README.md |
Testing mixed int8 quantization
The following is the recipe on how to effectively debug `bitsandbytes` integration on Hugging Face `transformers`.
## Library requirements
+ `transformers>=4.22.0`
+ `accelerate>=0.12.0`
+ `bitsandbytes>=0.31.5`.
## Hardware requirements
The following instructions are tested with 2 NVIDIA-Tesla T4 GPUs. To run successfully `bitsandbytes` you would need a 8-bit core tensor supported GPU. Note that Turing, Ampere or newer architectures - e.g. T4, RTX20s RTX30s, A40-A100, A6000 should be supported.
## Virutal envs
```bash
conda create --name int8-testing python==3.8
pip install bitsandbytes>=0.31.5
pip install accelerate>=0.12.0
pip install transformers>=4.23.0
```
if `transformers>=4.23.0` is not released yet, then use:
```
pip install git+https://github.com/huggingface/transformers.git
```
## Troubleshooting
A list of common errors:
### Torch does not correctly do the operations on GPU
First check that:
```py
import torch
vec = torch.randn(1, 2, 3).to(0)
```
Works without any error. If not, install torch using `conda` like:
```bash
conda create --name int8-testing python==3.8
conda install pytorch torchvision torchaudio cudatoolkit=11.6 -c pytorch -c conda-forge
pip install bitsandbytes>=0.31.5
pip install accelerate>=0.12.0
pip install transformers>=4.23.0
```
For the latest pytorch instructions please see [this](https://pytorch.org/get-started/locally/)
and the snippet above should work.
### ` bitsandbytes operations are not supported under CPU!`
This happens when some Linear weights are set to the CPU when using `accelerate`. Please check carefully `model.hf_device_map` and make sure that there is no `Linear` module that is assigned to CPU. It is fine to have the last module (usually the Lm_head) set on CPU.
### `To use the type as a Parameter, please correct the detach() semantics defined by __torch_dispatch__() implementation.`
Use the latest version of `accelerate` with a command such as: `pip install -U accelerate` and the problem should be solved.
### `Parameter has no attribue .CB`
Same solution as above.
### `RuntimeError: CUDA error: an illegal memory access was encountered ... consider passing CUDA_LAUNCH_BLOCKING=1`
Run your script by pre-pending `CUDA_LAUNCH_BLOCKING=1` and you should observe an error as described in the next section.
### `CUDA illegal memory error: an illegal memory access at line...`:
Check the CUDA verisons with:
```
nvcc --version
```
and confirm it is the same version as the one detected by `bitsandbytes`. If not, run:
```
ls -l $CONDA_PREFIX/lib/libcudart.so
```
or
```
ls -l $LD_LIBRARY_PATH
```
Check if `libcudart.so` has a correct symlink that is set. Sometimes `nvcc` detects the correct CUDA version but `bitsandbytes` doesn't. You have to make sure that the symlink that is set for the file `libcudart.so` is redirected to the correct CUDA file.
Here is an example of a badly configured CUDA installation:
`nvcc --version` gives:
which means that the detected CUDA version is 11.3 but `bitsandbytes` outputs:
First check:
```bash
echo $LD_LIBRARY_PATH
```
If this contains multiple paths separated by `:`. Then you have to make sure that the correct CUDA version is set. By doing:
```bash
ls -l $path/libcudart.so
```
On each path (`$path`) separated by `:`.
If not, simply run
```bash
ls -l $LD_LIBRARY_PATH/libcudart.so
```
and you can see
If you see that the file is linked to the wrong CUDA version (here 10.2), find the correct location for `libcudart.so` (`find --name libcudart.so`) and replace the environment variable `LD_LIBRARY_PATH` with the one containing the correct `libcudart.so` file. | huggingface/transformers/blob/main/tests/quantization/bnb/README.md |
!--Copyright 2023 The HuggingFace Team. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
the License. You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
specific language governing permissions and limitations under the License.
⚠️ Note that this file is in Markdown but contain specific syntax for our doc-builder (similar to MDX) that may not be
rendered properly in your Markdown viewer.
-->
# ConvNeXt V2
## Overview
The ConvNeXt V2 model was proposed in [ConvNeXt V2: Co-designing and Scaling ConvNets with Masked Autoencoders](https://arxiv.org/abs/2301.00808) by Sanghyun Woo, Shoubhik Debnath, Ronghang Hu, Xinlei Chen, Zhuang Liu, In So Kweon, Saining Xie.
ConvNeXt V2 is a pure convolutional model (ConvNet), inspired by the design of Vision Transformers, and a successor of [ConvNeXT](convnext).
The abstract from the paper is the following:
*Driven by improved architectures and better representation learning frameworks, the field of visual recognition has enjoyed rapid modernization and performance boost in the early 2020s. For example, modern ConvNets, represented by ConvNeXt, have demonstrated strong performance in various scenarios. While these models were originally designed for supervised learning with ImageNet labels, they can also potentially benefit from self-supervised learning techniques such as masked autoencoders (MAE). However, we found that simply combining these two approaches leads to subpar performance. In this paper, we propose a fully convolutional masked autoencoder framework and a new Global Response Normalization (GRN) layer that can be added to the ConvNeXt architecture to enhance inter-channel feature competition. This co-design of self-supervised learning techniques and architectural improvement results in a new model family called ConvNeXt V2, which significantly improves the performance of pure ConvNets on various recognition benchmarks, including ImageNet classification, COCO detection, and ADE20K segmentation. We also provide pre-trained ConvNeXt V2 models of various sizes, ranging from an efficient 3.7M-parameter Atto model with 76.7% top-1 accuracy on ImageNet, to a 650M Huge model that achieves a state-of-the-art 88.9% accuracy using only public training data.*
<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/convnextv2_architecture.png"
alt="drawing" width="600"/>
<small> ConvNeXt V2 architecture. Taken from the <a href="https://arxiv.org/abs/2301.00808">original paper</a>.</small>
This model was contributed by [adirik](https://huggingface.co/adirik). The original code can be found [here](https://github.com/facebookresearch/ConvNeXt-V2).
## Resources
A list of official Hugging Face and community (indicated by 🌎) resources to help you get started with ConvNeXt V2.
<PipelineTag pipeline="image-classification"/>
- [`ConvNextV2ForImageClassification`] is supported by this [example script](https://github.com/huggingface/transformers/tree/main/examples/pytorch/image-classification) and [notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/image_classification.ipynb).
If you're interested in submitting a resource to be included here, please feel free to open a Pull Request and we'll review it! The resource should ideally demonstrate something new instead of duplicating an existing resource.
## ConvNextV2Config
[[autodoc]] ConvNextV2Config
## ConvNextV2Model
[[autodoc]] ConvNextV2Model
- forward
## ConvNextV2ForImageClassification
[[autodoc]] ConvNextV2ForImageClassification
- forward
## TFConvNextV2Model
[[autodoc]] TFConvNextV2Model
- call
## TFConvNextV2ForImageClassification
[[autodoc]] TFConvNextV2ForImageClassification
- call
| huggingface/transformers/blob/main/docs/source/en/model_doc/convnextv2.md |
!--Copyright 2021 The HuggingFace Team. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
the License. You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
specific language governing permissions and limitations under the License.
⚠️ Note that this file is in Markdown but contain specific syntax for our doc-builder (similar to MDX) that may not be
rendered properly in your Markdown viewer.
-->
# CANINE
## Overview
The CANINE model was proposed in [CANINE: Pre-training an Efficient Tokenization-Free Encoder for Language
Representation](https://arxiv.org/abs/2103.06874) by Jonathan H. Clark, Dan Garrette, Iulia Turc, John Wieting. It's
among the first papers that trains a Transformer without using an explicit tokenization step (such as Byte Pair
Encoding (BPE), WordPiece or SentencePiece). Instead, the model is trained directly at a Unicode character-level.
Training at a character-level inevitably comes with a longer sequence length, which CANINE solves with an efficient
downsampling strategy, before applying a deep Transformer encoder.
The abstract from the paper is the following:
*Pipelined NLP systems have largely been superseded by end-to-end neural modeling, yet nearly all commonly-used models
still require an explicit tokenization step. While recent tokenization approaches based on data-derived subword
lexicons are less brittle than manually engineered tokenizers, these techniques are not equally suited to all
languages, and the use of any fixed vocabulary may limit a model's ability to adapt. In this paper, we present CANINE,
a neural encoder that operates directly on character sequences, without explicit tokenization or vocabulary, and a
pre-training strategy that operates either directly on characters or optionally uses subwords as a soft inductive bias.
To use its finer-grained input effectively and efficiently, CANINE combines downsampling, which reduces the input
sequence length, with a deep transformer stack, which encodes context. CANINE outperforms a comparable mBERT model by
2.8 F1 on TyDi QA, a challenging multilingual benchmark, despite having 28% fewer model parameters.*
This model was contributed by [nielsr](https://huggingface.co/nielsr). The original code can be found [here](https://github.com/google-research/language/tree/master/language/canine).
## Usage tips
- CANINE uses no less than 3 Transformer encoders internally: 2 "shallow" encoders (which only consist of a single
layer) and 1 "deep" encoder (which is a regular BERT encoder). First, a "shallow" encoder is used to contextualize
the character embeddings, using local attention. Next, after downsampling, a "deep" encoder is applied. Finally,
after upsampling, a "shallow" encoder is used to create the final character embeddings. Details regarding up- and
downsampling can be found in the paper.
- CANINE uses a max sequence length of 2048 characters by default. One can use [`CanineTokenizer`]
to prepare text for the model.
- Classification can be done by placing a linear layer on top of the final hidden state of the special [CLS] token
(which has a predefined Unicode code point). For token classification tasks however, the downsampled sequence of
tokens needs to be upsampled again to match the length of the original character sequence (which is 2048). The
details for this can be found in the paper.
Model checkpoints:
- [google/canine-c](https://huggingface.co/google/canine-c): Pre-trained with autoregressive character loss,
12-layer, 768-hidden, 12-heads, 121M parameters (size ~500 MB).
- [google/canine-s](https://huggingface.co/google/canine-s): Pre-trained with subword loss, 12-layer,
768-hidden, 12-heads, 121M parameters (size ~500 MB).
## Usage example
CANINE works on raw characters, so it can be used **without a tokenizer**:
```python
>>> from transformers import CanineModel
>>> import torch
>>> model = CanineModel.from_pretrained("google/canine-c") # model pre-trained with autoregressive character loss
>>> text = "hello world"
>>> # use Python's built-in ord() function to turn each character into its unicode code point id
>>> input_ids = torch.tensor([[ord(char) for char in text]])
>>> outputs = model(input_ids) # forward pass
>>> pooled_output = outputs.pooler_output
>>> sequence_output = outputs.last_hidden_state
```
For batched inference and training, it is however recommended to make use of the tokenizer (to pad/truncate all
sequences to the same length):
```python
>>> from transformers import CanineTokenizer, CanineModel
>>> model = CanineModel.from_pretrained("google/canine-c")
>>> tokenizer = CanineTokenizer.from_pretrained("google/canine-c")
>>> inputs = ["Life is like a box of chocolates.", "You never know what you gonna get."]
>>> encoding = tokenizer(inputs, padding="longest", truncation=True, return_tensors="pt")
>>> outputs = model(**encoding) # forward pass
>>> pooled_output = outputs.pooler_output
>>> sequence_output = outputs.last_hidden_state
```
## Resources
- [Text classification task guide](../tasks/sequence_classification)
- [Token classification task guide](../tasks/token_classification)
- [Question answering task guide](../tasks/question_answering)
- [Multiple choice task guide](../tasks/multiple_choice)
## CanineConfig
[[autodoc]] CanineConfig
## CanineTokenizer
[[autodoc]] CanineTokenizer
- build_inputs_with_special_tokens
- get_special_tokens_mask
- create_token_type_ids_from_sequences
## CANINE specific outputs
[[autodoc]] models.canine.modeling_canine.CanineModelOutputWithPooling
## CanineModel
[[autodoc]] CanineModel
- forward
## CanineForSequenceClassification
[[autodoc]] CanineForSequenceClassification
- forward
## CanineForMultipleChoice
[[autodoc]] CanineForMultipleChoice
- forward
## CanineForTokenClassification
[[autodoc]] CanineForTokenClassification
- forward
## CanineForQuestionAnswering
[[autodoc]] CanineForQuestionAnswering
- forward
| huggingface/transformers/blob/main/docs/source/en/model_doc/canine.md |
!--Copyright 2022 The HuggingFace Team. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
the License. You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
specific language governing permissions and limitations under the License.
⚠️ Note that this file is in Markdown but contain specific syntax for our doc-builder (similar to MDX) that may not be
rendered properly in your Markdown viewer.
-->
# Mask2Former
## Overview
The Mask2Former model was proposed in [Masked-attention Mask Transformer for Universal Image Segmentation](https://arxiv.org/abs/2112.01527) by Bowen Cheng, Ishan Misra, Alexander G. Schwing, Alexander Kirillov, Rohit Girdhar. Mask2Former is a unified framework for panoptic, instance and semantic segmentation and features significant performance and efficiency improvements over [MaskFormer](maskformer).
The abstract from the paper is the following:
*Image segmentation groups pixels with different semantics, e.g., category or instance membership. Each choice
of semantics defines a task. While only the semantics of each task differ, current research focuses on designing specialized architectures for each task. We present Masked-attention Mask Transformer (Mask2Former), a new architecture capable of addressing any image segmentation task (panoptic, instance or semantic). Its key components include masked attention, which extracts localized features by constraining cross-attention within predicted mask regions. In addition to reducing the research effort by at least three times, it outperforms the best specialized architectures by a significant margin on four popular datasets. Most notably, Mask2Former sets a new state-of-the-art for panoptic segmentation (57.8 PQ on COCO), instance segmentation (50.1 AP on COCO) and semantic segmentation (57.7 mIoU on ADE20K).*
<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/model_doc/mask2former_architecture.jpg" alt="drawing" width="600"/>
<small> Mask2Former architecture. Taken from the <a href="https://arxiv.org/abs/2112.01527">original paper.</a> </small>
This model was contributed by [Shivalika Singh](https://huggingface.co/shivi) and [Alara Dirik](https://huggingface.co/adirik). The original code can be found [here](https://github.com/facebookresearch/Mask2Former).
## Usage tips
- Mask2Former uses the same preprocessing and postprocessing steps as [MaskFormer](maskformer). Use [`Mask2FormerImageProcessor`] or [`AutoImageProcessor`] to prepare images and optional targets for the model.
- To get the final segmentation, depending on the task, you can call [`~Mask2FormerImageProcessor.post_process_semantic_segmentation`] or [`~Mask2FormerImageProcessor.post_process_instance_segmentation`] or [`~Mask2FormerImageProcessor.post_process_panoptic_segmentation`]. All three tasks can be solved using [`Mask2FormerForUniversalSegmentation`] output, panoptic segmentation accepts an optional `label_ids_to_fuse` argument to fuse instances of the target object/s (e.g. sky) together.
## Resources
A list of official Hugging Face and community (indicated by 🌎) resources to help you get started with Mask2Former.
- Demo notebooks regarding inference + fine-tuning Mask2Former on custom data can be found [here](https://github.com/NielsRogge/Transformers-Tutorials/tree/master/Mask2Former).
If you're interested in submitting a resource to be included here, please feel free to open a Pull Request and we will review it.
The resource should ideally demonstrate something new instead of duplicating an existing resource.
## Mask2FormerConfig
[[autodoc]] Mask2FormerConfig
## MaskFormer specific outputs
[[autodoc]] models.mask2former.modeling_mask2former.Mask2FormerModelOutput
[[autodoc]] models.mask2former.modeling_mask2former.Mask2FormerForUniversalSegmentationOutput
## Mask2FormerModel
[[autodoc]] Mask2FormerModel
- forward
## Mask2FormerForUniversalSegmentation
[[autodoc]] Mask2FormerForUniversalSegmentation
- forward
## Mask2FormerImageProcessor
[[autodoc]] Mask2FormerImageProcessor
- preprocess
- encode_inputs
- post_process_semantic_segmentation
- post_process_instance_segmentation
- post_process_panoptic_segmentation | huggingface/transformers/blob/main/docs/source/en/model_doc/mask2former.md |
Plug and Play Language Models: a Simple Approach to Controlled Text Generation
Authors: [Sumanth Dathathri](https://dathath.github.io/), [Andrea Madotto](https://andreamad8.github.io/), Janice Lan, Jane Hung, Eric Frank, [Piero Molino](https://w4nderlu.st/), [Jason Yosinski](http://yosinski.com/), and [Rosanne Liu](http://www.rosanneliu.com/)
This folder contains the original code used to run the Plug and Play Language Model (PPLM).
Paper link: https://arxiv.org/abs/1912.02164
Blog link: https://eng.uber.com/pplm
Please check out the repo under uber-research for more information: https://github.com/uber-research/PPLM
# Note
⚠️ This project should be run with pytorch-lightning==1.0.4 which has a potential security vulnerability
## Setup
```bash
git clone https://github.com/huggingface/transformers && cd transformers
pip install .
pip install nltk torchtext # additional requirements.
cd examples/research_projects/pplm
```
## PPLM-BoW
### Example command for bag-of-words control
```bash
python run_pplm.py -B military --cond_text "The potato" --length 50 --gamma 1.5 --num_iterations 3 --num_samples 10 --stepsize 0.03 --window_length 5 --kl_scale 0.01 --gm_scale 0.99 --colorama --sample
```
### Tuning hyperparameters for bag-of-words control
1. Increase `--stepsize` to intensify topic control, and decrease its value to soften the control. `--stepsize 0` recovers the original uncontrolled GPT-2 model.
2. If the language being generated is repetitive (For e.g. "science science experiment experiment"), there are several options to consider: </br>
a) Reduce the `--stepsize` </br>
b) Increase `--kl_scale` (the KL-loss coefficient) or decrease `--gm_scale` (the gm-scaling term) </br>
c) Add `--grad-length xx` where xx is an (integer <= length, e.g. `--grad-length 30`).</br>
## PPLM-Discrim
### Example command for discriminator based sentiment control
```bash
python run_pplm.py -D sentiment --class_label 2 --cond_text "My dog died" --length 50 --gamma 1.0 --num_iterations 10 --num_samples 10 --stepsize 0.04 --kl_scale 0.01 --gm_scale 0.95 --sample
```
### Tuning hyperparameters for discriminator control
1. Increase `--stepsize` to intensify topic control, and decrease its value to soften the control. `--stepsize 0` recovers the original uncontrolled GPT-2 model.
2. Use `--class_label 3` for negative, and `--class_label 2` for positive
| huggingface/transformers/blob/main/examples/research_projects/pplm/README.md |
!--Copyright 2022 The HuggingFace Team. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
the License. You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
specific language governing permissions and limitations under the License.
⚠️ Note that this file is in Markdown but contain specific syntax for our doc-builder (similar to MDX) that may not be
rendered properly in your Markdown viewer.
-->
# Utilities for Image Processors
This page lists all the utility functions used by the image processors, mainly the functional
transformations used to process the images.
Most of those are only useful if you are studying the code of the image processors in the library.
## Image Transformations
[[autodoc]] image_transforms.center_crop
[[autodoc]] image_transforms.center_to_corners_format
[[autodoc]] image_transforms.corners_to_center_format
[[autodoc]] image_transforms.id_to_rgb
[[autodoc]] image_transforms.normalize
[[autodoc]] image_transforms.pad
[[autodoc]] image_transforms.rgb_to_id
[[autodoc]] image_transforms.rescale
[[autodoc]] image_transforms.resize
[[autodoc]] image_transforms.to_pil_image
## ImageProcessingMixin
[[autodoc]] image_processing_utils.ImageProcessingMixin
| huggingface/transformers/blob/main/docs/source/en/internal/image_processing_utils.md |
!---
Copyright 2020 The HuggingFace Team. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
-->
## Language model training
Fine-tuning (or training from scratch) the library models for language modeling on a text dataset for GPT, GPT-2,
ALBERT, BERT, DistilBERT, RoBERTa, XLNet... GPT and GPT-2 are trained or fine-tuned using a causal language modeling
(CLM) loss while ALBERT, BERT, DistilBERT and RoBERTa are trained or fine-tuned using a masked language modeling (MLM)
loss. XLNet uses permutation language modeling (PLM), you can find more information about the differences between those
objectives in our [model summary](https://huggingface.co/transformers/model_summary.html).
There are two sets of scripts provided. The first set leverages the Trainer API. The second set with `no_trainer` in the suffix uses a custom training loop and leverages the 🤗 Accelerate library . Both sets use the 🤗 Datasets library. You can easily customize them to your needs if you need extra processing on your datasets.
**Note:** The old script `run_language_modeling.py` is still available [here](https://github.com/huggingface/transformers/blob/main/examples/legacy/run_language_modeling.py).
The following examples, will run on datasets hosted on our [hub](https://huggingface.co/datasets) or with your own
text files for training and validation. We give examples of both below.
### GPT-2/GPT and causal language modeling
The following example fine-tunes GPT-2 on WikiText-2. We're using the raw WikiText-2 (no tokens were replaced before
the tokenization). The loss here is that of causal language modeling.
```bash
python run_clm.py \
--model_name_or_path gpt2 \
--dataset_name wikitext \
--dataset_config_name wikitext-2-raw-v1 \
--per_device_train_batch_size 8 \
--per_device_eval_batch_size 8 \
--do_train \
--do_eval \
--output_dir /tmp/test-clm
```
This takes about half an hour to train on a single K80 GPU and about one minute for the evaluation to run. It reaches
a score of ~20 perplexity once fine-tuned on the dataset.
To run on your own training and validation files, use the following command:
```bash
python run_clm.py \
--model_name_or_path gpt2 \
--train_file path_to_train_file \
--validation_file path_to_validation_file \
--per_device_train_batch_size 8 \
--per_device_eval_batch_size 8 \
--do_train \
--do_eval \
--output_dir /tmp/test-clm
```
This uses the built in HuggingFace `Trainer` for training. If you want to use a custom training loop, you can utilize or adapt the `run_clm_no_trainer.py` script. Take a look at the script for a list of supported arguments. An example is shown below:
```bash
python run_clm_no_trainer.py \
--dataset_name wikitext \
--dataset_config_name wikitext-2-raw-v1 \
--model_name_or_path gpt2 \
--output_dir /tmp/test-clm
```
### RoBERTa/BERT/DistilBERT and masked language modeling
The following example fine-tunes RoBERTa on WikiText-2. Here too, we're using the raw WikiText-2. The loss is different
as BERT/RoBERTa have a bidirectional mechanism; we're therefore using the same loss that was used during their
pre-training: masked language modeling.
In accordance to the RoBERTa paper, we use dynamic masking rather than static masking. The model may, therefore,
converge slightly slower (over-fitting takes more epochs).
```bash
python run_mlm.py \
--model_name_or_path roberta-base \
--dataset_name wikitext \
--dataset_config_name wikitext-2-raw-v1 \
--per_device_train_batch_size 8 \
--per_device_eval_batch_size 8 \
--do_train \
--do_eval \
--output_dir /tmp/test-mlm
```
To run on your own training and validation files, use the following command:
```bash
python run_mlm.py \
--model_name_or_path roberta-base \
--train_file path_to_train_file \
--validation_file path_to_validation_file \
--per_device_train_batch_size 8 \
--per_device_eval_batch_size 8 \
--do_train \
--do_eval \
--output_dir /tmp/test-mlm
```
If your dataset is organized with one sample per line, you can use the `--line_by_line` flag (otherwise the script
concatenates all texts and then splits them in blocks of the same length).
This uses the built in HuggingFace `Trainer` for training. If you want to use a custom training loop, you can utilize or adapt the `run_mlm_no_trainer.py` script. Take a look at the script for a list of supported arguments. An example is shown below:
```bash
python run_mlm_no_trainer.py \
--dataset_name wikitext \
--dataset_config_name wikitext-2-raw-v1 \
--model_name_or_path roberta-base \
--output_dir /tmp/test-mlm
```
**Note:** On TPU, you should use the flag `--pad_to_max_length` in conjunction with the `--line_by_line` flag to make
sure all your batches have the same length.
### Whole word masking
This part was moved to `examples/research_projects/mlm_wwm`.
### XLNet and permutation language modeling
XLNet uses a different training objective, which is permutation language modeling. It is an autoregressive method
to learn bidirectional contexts by maximizing the expected likelihood over all permutations of the input
sequence factorization order.
We use the `--plm_probability` flag to define the ratio of length of a span of masked tokens to surrounding
context length for permutation language modeling.
The `--max_span_length` flag may also be used to limit the length of a span of masked tokens used
for permutation language modeling.
Here is how to fine-tune XLNet on wikitext-2:
```bash
python run_plm.py \
--model_name_or_path=xlnet-base-cased \
--dataset_name wikitext \
--dataset_config_name wikitext-2-raw-v1 \
--per_device_train_batch_size 8 \
--per_device_eval_batch_size 8 \
--do_train \
--do_eval \
--output_dir /tmp/test-plm
```
To fine-tune it on your own training and validation file, run:
```bash
python run_plm.py \
--model_name_or_path=xlnet-base-cased \
--train_file path_to_train_file \
--validation_file path_to_validation_file \
--per_device_train_batch_size 8 \
--per_device_eval_batch_size 8 \
--do_train \
--do_eval \
--output_dir /tmp/test-plm
```
If your dataset is organized with one sample per line, you can use the `--line_by_line` flag (otherwise the script
concatenates all texts and then splits them in blocks of the same length).
**Note:** On TPU, you should use the flag `--pad_to_max_length` in conjunction with the `--line_by_line` flag to make
sure all your batches have the same length.
## Streaming
To use the streaming dataset mode which can be very useful for large datasets, add `--streaming` to the command line. This is currently supported by `run_mlm.py` and `run_clm.py`.
## Low Cpu Memory Usage
To use low cpu memory mode which can be very useful for LLM, add `--low_cpu_mem_usage` to the command line. This is currently supported by `run_clm.py`,`run_mlm.py`, `run_plm.py`,`run_mlm_no_trainer.py` and `run_clm_no_trainer.py`.
## Creating a model on the fly
When training a model from scratch, configuration values may be overridden with the help of `--config_overrides`:
```bash
python run_clm.py --model_type gpt2 --tokenizer_name gpt2 \ --config_overrides="n_embd=1024,n_head=16,n_layer=48,n_positions=102" \
[...]
```
This feature is only available in `run_clm.py`, `run_plm.py` and `run_mlm.py`.
| huggingface/transformers/blob/main/examples/pytorch/language-modeling/README.md |
!--Copyright 2022 The HuggingFace Team. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
the License. You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
specific language governing permissions and limitations under the License.
⚠️ Note that this file is in Markdown but contain specific syntax for our doc-builder (similar to MDX) that may not be
rendered properly in your Markdown viewer.
-->
# Trajectory Transformer
<Tip warning={true}>
This model is in maintenance mode only, so we won't accept any new PRs changing its code.
If you run into any issues running this model, please reinstall the last version that supported this model: v4.30.0.
You can do so by running the following command: `pip install -U transformers==4.30.0`.
</Tip>
## Overview
The Trajectory Transformer model was proposed in [Offline Reinforcement Learning as One Big Sequence Modeling Problem](https://arxiv.org/abs/2106.02039) by Michael Janner, Qiyang Li, Sergey Levine.
The abstract from the paper is the following:
*Reinforcement learning (RL) is typically concerned with estimating stationary policies or single-step models,
leveraging the Markov property to factorize problems in time. However, we can also view RL as a generic sequence
modeling problem, with the goal being to produce a sequence of actions that leads to a sequence of high rewards.
Viewed in this way, it is tempting to consider whether high-capacity sequence prediction models that work well
in other domains, such as natural-language processing, can also provide effective solutions to the RL problem.
To this end, we explore how RL can be tackled with the tools of sequence modeling, using a Transformer architecture
to model distributions over trajectories and repurposing beam search as a planning algorithm. Framing RL as sequence
modeling problem simplifies a range of design decisions, allowing us to dispense with many of the components common
in offline RL algorithms. We demonstrate the flexibility of this approach across long-horizon dynamics prediction,
imitation learning, goal-conditioned RL, and offline RL. Further, we show that this approach can be combined with
existing model-free algorithms to yield a state-of-the-art planner in sparse-reward, long-horizon tasks.*
This model was contributed by [CarlCochet](https://huggingface.co/CarlCochet). The original code can be found [here](https://github.com/jannerm/trajectory-transformer).
## Usage tips
This Transformer is used for deep reinforcement learning. To use it, you need to create sequences from
actions, states and rewards from all previous timesteps. This model will treat all these elements together
as one big sequence (a trajectory).
## TrajectoryTransformerConfig
[[autodoc]] TrajectoryTransformerConfig
## TrajectoryTransformerModel
[[autodoc]] TrajectoryTransformerModel
- forward
| huggingface/transformers/blob/main/docs/source/en/model_doc/trajectory_transformer.md |
!---
Copyright 2020 The HuggingFace Team. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
-->
# Generating the documentation
To generate the documentation, you first have to build it. Several packages are necessary to build the doc,
you can install them with the following command, at the root of the code repository:
```bash
pip install -e ".[docs]"
```
Then you need to install our special tool that builds the documentation:
```bash
pip install git+https://github.com/huggingface/doc-builder
```
---
**NOTE**
You only need to generate the documentation to inspect it locally (if you're planning changes and want to
check how they look before committing for instance). You don't have to commit the built documentation.
---
## Building the documentation
Once you have setup the `doc-builder` and additional packages, you can generate the documentation by
typing the following command:
```bash
doc-builder build transformers docs/source/en/ --build_dir ~/tmp/test-build
```
You can adapt the `--build_dir` to set any temporary folder that you prefer. This command will create it and generate
the MDX files that will be rendered as the documentation on the main website. You can inspect them in your favorite
Markdown editor.
## Previewing the documentation
To preview the docs, first install the `watchdog` module with:
```bash
pip install watchdog
```
Then run the following command:
```bash
doc-builder preview {package_name} {path_to_docs}
```
For example:
```bash
doc-builder preview transformers docs/source/en/
```
The docs will be viewable at [http://localhost:3000](http://localhost:3000). You can also preview the docs once you have opened a PR. You will see a bot add a comment to a link where the documentation with your changes lives.
---
**NOTE**
The `preview` command only works with existing doc files. When you add a completely new file, you need to update `_toctree.yml` & restart `preview` command (`ctrl-c` to stop it & call `doc-builder preview ...` again).
---
## Adding a new element to the navigation bar
Accepted files are Markdown (.md).
Create a file with its extension and put it in the source directory. You can then link it to the toc-tree by putting
the filename without the extension in the [`_toctree.yml`](https://github.com/huggingface/transformers/blob/main/docs/source/en/_toctree.yml) file.
## Renaming section headers and moving sections
It helps to keep the old links working when renaming the section header and/or moving sections from one document to another. This is because the old links are likely to be used in Issues, Forums, and Social media and it'd make for a much more superior user experience if users reading those months later could still easily navigate to the originally intended information.
Therefore, we simply keep a little map of moved sections at the end of the document where the original section was. The key is to preserve the original anchor.
So if you renamed a section from: "Section A" to "Section B", then you can add at the end of the file:
```
Sections that were moved:
[ <a href="#section-b">Section A</a><a id="section-a"></a> ]
```
and of course, if you moved it to another file, then:
```
Sections that were moved:
[ <a href="../new-file#section-b">Section A</a><a id="section-a"></a> ]
```
Use the relative style to link to the new file so that the versioned docs continue to work.
For an example of a rich moved section set please see the very end of [the Trainer doc](https://github.com/huggingface/transformers/blob/main/docs/source/en/main_classes/trainer.md).
## Writing Documentation - Specification
The `huggingface/transformers` documentation follows the
[Google documentation](https://sphinxcontrib-napoleon.readthedocs.io/en/latest/example_google.html) style for docstrings,
although we can write them directly in Markdown.
### Adding a new tutorial
Adding a new tutorial or section is done in two steps:
- Add a new file under `./source`. This file can either be ReStructuredText (.rst) or Markdown (.md).
- Link that file in `./source/_toctree.yml` on the correct toc-tree.
Make sure to put your new file under the proper section. It's unlikely to go in the first section (*Get Started*), so
depending on the intended targets (beginners, more advanced users, or researchers) it should go in sections two, three, or
four.
### Translating
When translating, refer to the guide at [./TRANSLATING.md](https://github.com/huggingface/transformers/blob/main/docs/TRANSLATING.md).
### Adding a new model
When adding a new model:
- Create a file `xxx.md` or under `./source/model_doc` (don't hesitate to copy an existing file as template).
- Link that file in `./source/_toctree.yml`.
- Write a short overview of the model:
- Overview with paper & authors
- Paper abstract
- Tips and tricks and how to use it best
- Add the classes that should be linked in the model. This generally includes the configuration, the tokenizer, and
every model of that class (the base model, alongside models with additional heads), both in PyTorch and TensorFlow.
The order is generally:
- Configuration
- Tokenizer
- PyTorch base model
- PyTorch head models
- TensorFlow base model
- TensorFlow head models
- Flax base model
- Flax head models
These classes should be added using our Markdown syntax. Usually as follows:
```
## XXXConfig
[[autodoc]] XXXConfig
```
This will include every public method of the configuration that is documented. If for some reason you wish for a method
not to be displayed in the documentation, you can do so by specifying which methods should be in the docs:
```
## XXXTokenizer
[[autodoc]] XXXTokenizer
- build_inputs_with_special_tokens
- get_special_tokens_mask
- create_token_type_ids_from_sequences
- save_vocabulary
```
If you just want to add a method that is not documented (for instance magic methods like `__call__` are not documented
by default) you can put the list of methods to add in a list that contains `all`:
```
## XXXTokenizer
[[autodoc]] XXXTokenizer
- all
- __call__
```
### Writing source documentation
Values that should be put in `code` should either be surrounded by backticks: \`like so\`. Note that argument names
and objects like True, None, or any strings should usually be put in `code`.
When mentioning a class, function, or method, it is recommended to use our syntax for internal links so that our tool
adds a link to its documentation with this syntax: \[\`XXXClass\`\] or \[\`function\`\]. This requires the class or
function to be in the main package.
If you want to create a link to some internal class or function, you need to
provide its path. For instance: \[\`utils.ModelOutput\`\]. This will be converted into a link with
`utils.ModelOutput` in the description. To get rid of the path and only keep the name of the object you are
linking to in the description, add a ~: \[\`~utils.ModelOutput\`\] will generate a link with `ModelOutput` in the description.
The same works for methods so you can either use \[\`XXXClass.method\`\] or \[~\`XXXClass.method\`\].
#### Defining arguments in a method
Arguments should be defined with the `Args:` (or `Arguments:` or `Parameters:`) prefix, followed by a line return and
an indentation. The argument should be followed by its type, with its shape if it is a tensor, a colon, and its
description:
```
Args:
n_layers (`int`): The number of layers of the model.
```
If the description is too long to fit in one line, another indentation is necessary before writing the description
after the argument.
Here's an example showcasing everything so far:
```
Args:
input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
Indices of input sequence tokens in the vocabulary.
Indices can be obtained using [`AlbertTokenizer`]. See [`~PreTrainedTokenizer.encode`] and
[`~PreTrainedTokenizer.__call__`] for details.
[What are input IDs?](../glossary#input-ids)
```
For optional arguments or arguments with defaults we follow the following syntax: imagine we have a function with the
following signature:
```
def my_function(x: str = None, a: float = 1):
```
then its documentation should look like this:
```
Args:
x (`str`, *optional*):
This argument controls ...
a (`float`, *optional*, defaults to 1):
This argument is used to ...
```
Note that we always omit the "defaults to \`None\`" when None is the default for any argument. Also note that even
if the first line describing your argument type and its default gets long, you can't break it on several lines. You can
however write as many lines as you want in the indented description (see the example above with `input_ids`).
#### Writing a multi-line code block
Multi-line code blocks can be useful for displaying examples. They are done between two lines of three backticks as usual in Markdown:
````
```
# first line of code
# second line
# etc
```
````
We follow the [doctest](https://docs.python.org/3/library/doctest.html) syntax for the examples to automatically test
the results to stay consistent with the library.
#### Writing a return block
The return block should be introduced with the `Returns:` prefix, followed by a line return and an indentation.
The first line should be the type of the return, followed by a line return. No need to indent further for the elements
building the return.
Here's an example of a single value return:
```
Returns:
`List[int]`: A list of integers in the range [0, 1] --- 1 for a special token, 0 for a sequence token.
```
Here's an example of a tuple return, comprising several objects:
```
Returns:
`tuple(torch.FloatTensor)` comprising various elements depending on the configuration ([`BertConfig`]) and inputs:
- ** loss** (*optional*, returned when `masked_lm_labels` is provided) `torch.FloatTensor` of shape `(1,)` --
Total loss is the sum of the masked language modeling loss and the next sequence prediction (classification) loss.
- **prediction_scores** (`torch.FloatTensor` of shape `(batch_size, sequence_length, config.vocab_size)`) --
Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).
```
#### Adding an image
Due to the rapidly growing repository, it is important to make sure that no files that would significantly weigh down the repository are added. This includes images, videos, and other non-text files. We prefer to leverage a hf.co hosted `dataset` like
the ones hosted on [`hf-internal-testing`](https://huggingface.co/hf-internal-testing) in which to place these files and reference
them by URL. We recommend putting them in the following dataset: [huggingface/documentation-images](https://huggingface.co/datasets/huggingface/documentation-images).
If an external contribution, feel free to add the images to your PR and ask a Hugging Face member to migrate your images
to this dataset.
## Styling the docstring
We have an automatic script running with the `make style` comment that will make sure that:
- the docstrings fully take advantage of the line width
- all code examples are formatted using black, like the code of the Transformers library
This script may have some weird failures if you made a syntax mistake or if you uncover a bug. Therefore, it's
recommended to commit your changes before running `make style`, so you can revert the changes done by that script
easily.
# Testing documentation examples
Good documentation often comes with an example of how a specific function or class should be used.
Each model class should contain at least one example showcasing
how to use this model class in inference. *E.g.* the class [Wav2Vec2ForCTC](https://huggingface.co/docs/transformers/model_doc/wav2vec2#transformers.Wav2Vec2ForCTC)
includes an example of how to transcribe speech to text in the
[docstring of its forward function](https://huggingface.co/docs/transformers/model_doc/wav2vec2#transformers.Wav2Vec2ForCTC.forward).
## Writing documentation examples
The syntax for Example docstrings can look as follows:
```
Example:
```python
>>> from transformers import Wav2Vec2Processor, Wav2Vec2ForCTC
>>> from datasets import load_dataset
>>> import torch
>>> dataset = load_dataset("hf-internal-testing/librispeech_asr_demo", "clean", split="validation")
>>> dataset = dataset.sort("id")
>>> sampling_rate = dataset.features["audio"].sampling_rate
>>> processor = Wav2Vec2Processor.from_pretrained("facebook/wav2vec2-base-960h")
>>> model = Wav2Vec2ForCTC.from_pretrained("facebook/wav2vec2-base-960h")
>>> # audio file is decoded on the fly
>>> inputs = processor(dataset[0]["audio"]["array"], sampling_rate=sampling_rate, return_tensors="pt")
>>> with torch.no_grad():
... logits = model(**inputs).logits
>>> predicted_ids = torch.argmax(logits, dim=-1)
>>> # transcribe speech
>>> transcription = processor.batch_decode(predicted_ids)
>>> transcription[0]
'MISTER QUILTER IS THE APOSTLE OF THE MIDDLE CLASSES AND WE ARE GLAD TO WELCOME HIS GOSPEL'
```
```
The docstring should give a minimal, clear example of how the respective model
is to be used in inference and also include the expected (ideally sensible)
output.
Often, readers will try out the example before even going through the function
or class definitions. Therefore, it is of utmost importance that the example
works as expected.
## Docstring testing
To do so each example should be included in the doctests.
We use pytests' [doctest integration](https://docs.pytest.org/doctest.html) to verify that all of our examples run correctly.
For Transformers, the doctests are run on a daily basis via GitHub Actions as can be
seen [here](https://github.com/huggingface/transformers/actions/workflows/doctests.yml).
### For Python files
Run all the tests in the docstrings of a given file with the following command, here is how we test the modeling file of Wav2Vec2 for instance:
```bash
pytest --doctest-modules src/transformers/models/wav2vec2/modeling_wav2vec2.py -sv --doctest-continue-on-failure
```
If you want to isolate a specific docstring, just add `::` after the file name then type the whole path of the function/class/method whose docstring you want to test. For instance, here is how to just test the forward method of `Wav2Vec2ForCTC`:
```bash
pytest --doctest-modules src/transformers/models/wav2vec2/modeling_wav2vec2.py::transformers.models.wav2vec2.modeling_wav2vec2.Wav2Vec2ForCTC.forward -sv --doctest-continue-on-failure
```
### For Markdown files
You can test locally a given file with this command (here testing the quicktour):
```bash
pytest --doctest-modules docs/source/quicktour.md -sv --doctest-continue-on-failure --doctest-glob="*.md"
```
### Writing doctests
Here are a few tips to help you debug the doctests and make them pass:
- The outputs of the code need to match the expected output **exactly**, so make sure you have the same outputs. In particular doctest will see a difference between single quotes and double quotes, or a missing parenthesis. The only exceptions to that rule are:
* whitespace: one give whitespace (space, tabulation, new line) is equivalent to any number of whitespace, so you can add new lines where there are spaces to make your output more readable.
* numerical values: you should never put more than 4 or 5 digits to expected results as different setups or library versions might get you slightly different results. `doctest` is configured to ignore any difference lower than the precision to which you wrote (so 1e-4 if you write 4 digits).
- Don't leave a block of code that is very long to execute. If you can't make it fast, you can either not use the doctest syntax on it (so that it's ignored), or if you want to use the doctest syntax to show the results, you can add a comment `# doctest: +SKIP` at the end of the lines of code too long to execute
- Each line of code that produces a result needs to have that result written below. You can ignore an output if you don't want to show it in your code example by adding a comment ` # doctest: +IGNORE_RESULT` at the end of the line of code producing it.
| huggingface/transformers/blob/main/docs/README.md |
!---
Copyright 2020 The HuggingFace Team. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
-->
## Language generation
Based on the script [`run_generation.py`](https://github.com/huggingface/transformers/blob/main/examples/pytorch/text-generation/run_generation.py).
Conditional text generation using the auto-regressive models of the library: GPT, GPT-2, GPTJ, Transformer-XL, XLNet, CTRL, BLOOM, LLAMA, OPT.
A similar script is used for our official demo [Write With Transfomer](https://transformer.huggingface.co), where you
can try out the different models available in the library.
Example usage:
```bash
python run_generation.py \
--model_type=gpt2 \
--model_name_or_path=gpt2
```
| huggingface/transformers/blob/main/examples/pytorch/text-generation/README.md |
!--Copyright 2020 The HuggingFace Team. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
the License. You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
specific language governing permissions and limitations under the License.
⚠️ Note that this file is in Markdown but contain specific syntax for our doc-builder (similar to MDX) that may not be
rendered properly in your Markdown viewer.
-->
# Processors
Processors can mean two different things in the Transformers library:
- the objects that pre-process inputs for multi-modal models such as [Wav2Vec2](../model_doc/wav2vec2) (speech and text)
or [CLIP](../model_doc/clip) (text and vision)
- deprecated objects that were used in older versions of the library to preprocess data for GLUE or SQUAD.
## Multi-modal processors
Any multi-modal model will require an object to encode or decode the data that groups several modalities (among text,
vision and audio). This is handled by objects called processors, which group together two or more processing objects
such as tokenizers (for the text modality), image processors (for vision) and feature extractors (for audio).
Those processors inherit from the following base class that implements the saving and loading functionality:
[[autodoc]] ProcessorMixin
## Deprecated processors
All processors follow the same architecture which is that of the
[`~data.processors.utils.DataProcessor`]. The processor returns a list of
[`~data.processors.utils.InputExample`]. These
[`~data.processors.utils.InputExample`] can be converted to
[`~data.processors.utils.InputFeatures`] in order to be fed to the model.
[[autodoc]] data.processors.utils.DataProcessor
[[autodoc]] data.processors.utils.InputExample
[[autodoc]] data.processors.utils.InputFeatures
## GLUE
[General Language Understanding Evaluation (GLUE)](https://gluebenchmark.com/) is a benchmark that evaluates the
performance of models across a diverse set of existing NLU tasks. It was released together with the paper [GLUE: A
multi-task benchmark and analysis platform for natural language understanding](https://openreview.net/pdf?id=rJ4km2R5t7)
This library hosts a total of 10 processors for the following tasks: MRPC, MNLI, MNLI (mismatched), CoLA, SST2, STSB,
QQP, QNLI, RTE and WNLI.
Those processors are:
- [`~data.processors.utils.MrpcProcessor`]
- [`~data.processors.utils.MnliProcessor`]
- [`~data.processors.utils.MnliMismatchedProcessor`]
- [`~data.processors.utils.Sst2Processor`]
- [`~data.processors.utils.StsbProcessor`]
- [`~data.processors.utils.QqpProcessor`]
- [`~data.processors.utils.QnliProcessor`]
- [`~data.processors.utils.RteProcessor`]
- [`~data.processors.utils.WnliProcessor`]
Additionally, the following method can be used to load values from a data file and convert them to a list of
[`~data.processors.utils.InputExample`].
[[autodoc]] data.processors.glue.glue_convert_examples_to_features
## XNLI
[The Cross-Lingual NLI Corpus (XNLI)](https://www.nyu.edu/projects/bowman/xnli/) is a benchmark that evaluates the
quality of cross-lingual text representations. XNLI is crowd-sourced dataset based on [*MultiNLI*](http://www.nyu.edu/projects/bowman/multinli/): pairs of text are labeled with textual entailment annotations for 15
different languages (including both high-resource language such as English and low-resource languages such as Swahili).
It was released together with the paper [XNLI: Evaluating Cross-lingual Sentence Representations](https://arxiv.org/abs/1809.05053)
This library hosts the processor to load the XNLI data:
- [`~data.processors.utils.XnliProcessor`]
Please note that since the gold labels are available on the test set, evaluation is performed on the test set.
An example using these processors is given in the [run_xnli.py](https://github.com/huggingface/transformers/tree/main/examples/pytorch/text-classification/run_xnli.py) script.
## SQuAD
[The Stanford Question Answering Dataset (SQuAD)](https://rajpurkar.github.io/SQuAD-explorer//) is a benchmark that
evaluates the performance of models on question answering. Two versions are available, v1.1 and v2.0. The first version
(v1.1) was released together with the paper [SQuAD: 100,000+ Questions for Machine Comprehension of Text](https://arxiv.org/abs/1606.05250). The second version (v2.0) was released alongside the paper [Know What You Don't
Know: Unanswerable Questions for SQuAD](https://arxiv.org/abs/1806.03822).
This library hosts a processor for each of the two versions:
### Processors
Those processors are:
- [`~data.processors.utils.SquadV1Processor`]
- [`~data.processors.utils.SquadV2Processor`]
They both inherit from the abstract class [`~data.processors.utils.SquadProcessor`]
[[autodoc]] data.processors.squad.SquadProcessor
- all
Additionally, the following method can be used to convert SQuAD examples into
[`~data.processors.utils.SquadFeatures`] that can be used as model inputs.
[[autodoc]] data.processors.squad.squad_convert_examples_to_features
These processors as well as the aforementioned method can be used with files containing the data as well as with the
*tensorflow_datasets* package. Examples are given below.
### Example usage
Here is an example using the processors as well as the conversion method using data files:
```python
# Loading a V2 processor
processor = SquadV2Processor()
examples = processor.get_dev_examples(squad_v2_data_dir)
# Loading a V1 processor
processor = SquadV1Processor()
examples = processor.get_dev_examples(squad_v1_data_dir)
features = squad_convert_examples_to_features(
examples=examples,
tokenizer=tokenizer,
max_seq_length=max_seq_length,
doc_stride=args.doc_stride,
max_query_length=max_query_length,
is_training=not evaluate,
)
```
Using *tensorflow_datasets* is as easy as using a data file:
```python
# tensorflow_datasets only handle Squad V1.
tfds_examples = tfds.load("squad")
examples = SquadV1Processor().get_examples_from_dataset(tfds_examples, evaluate=evaluate)
features = squad_convert_examples_to_features(
examples=examples,
tokenizer=tokenizer,
max_seq_length=max_seq_length,
doc_stride=args.doc_stride,
max_query_length=max_query_length,
is_training=not evaluate,
)
```
Another example using these processors is given in the [run_squad.py](https://github.com/huggingface/transformers/tree/main/examples/legacy/question-answering/run_squad.py) script.
| huggingface/transformers/blob/main/docs/source/en/main_classes/processors.md |
!--Copyright 2023 The HuggingFace Team. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
the License. You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
specific language governing permissions and limitations under the License.
-->
# BROS
## Overview
The BROS model was proposed in [BROS: A Pre-trained Language Model Focusing on Text and Layout for Better Key Information Extraction from Documents](https://arxiv.org/abs/2108.04539) by Teakgyu Hong, Donghyun Kim, Mingi Ji, Wonseok Hwang, Daehyun Nam, Sungrae Park.
BROS stands for *BERT Relying On Spatiality*. It is an encoder-only Transformer model that takes a sequence of tokens and their bounding boxes as inputs and outputs a sequence of hidden states. BROS encode relative spatial information instead of using absolute spatial information.
It is pre-trained with two objectives: a token-masked language modeling objective (TMLM) used in BERT, and a novel area-masked language modeling objective (AMLM)
In TMLM, tokens are randomly masked, and the model predicts the masked tokens using spatial information and other unmasked tokens.
AMLM is a 2D version of TMLM. It randomly masks text tokens and predicts with the same information as TMLM, but it masks text blocks (areas).
`BrosForTokenClassification` has a simple linear layer on top of BrosModel. It predicts the label of each token.
`BrosSpadeEEForTokenClassification` has an `initial_token_classifier` and `subsequent_token_classifier` on top of BrosModel. `initial_token_classifier` is used to predict the first token of each entity, and `subsequent_token_classifier` is used to predict the next token of within entity. `BrosSpadeELForTokenClassification` has an `entity_linker` on top of BrosModel. `entity_linker` is used to predict the relation between two entities.
`BrosForTokenClassification` and `BrosSpadeEEForTokenClassification` essentially perform the same job. However, `BrosForTokenClassification` assumes input tokens are perfectly serialized (which is very challenging task since they exist in a 2D space), while `BrosSpadeEEForTokenClassification` allows for more flexibility in handling serialization errors as it predicts next connection tokens from one token.
`BrosSpadeELForTokenClassification` perform the intra-entity linking task. It predicts relation from one token (of one entity) to another token (of another entity) if these two entities share some relation.
BROS achieves comparable or better result on Key Information Extraction (KIE) benchmarks such as FUNSD, SROIE, CORD and SciTSR, without relying on explicit visual features.
The abstract from the paper is the following:
*Key information extraction (KIE) from document images requires understanding the contextual and spatial semantics of texts in two-dimensional (2D) space. Many recent studies try to solve the task by developing pre-trained language models focusing on combining visual features from document images with texts and their layout. On the other hand, this paper tackles the problem by going back to the basic: effective combination of text and layout. Specifically, we propose a pre-trained language model, named BROS (BERT Relying On Spatiality), that encodes relative positions of texts in 2D space and learns from unlabeled documents with area-masking strategy. With this optimized training scheme for understanding texts in 2D space, BROS shows comparable or better performance compared to previous methods on four KIE benchmarks (FUNSD, SROIE*, CORD, and SciTSR) without relying on visual features. This paper also reveals two real-world challenges in KIE tasks-(1) minimizing the error from incorrect text ordering and (2) efficient learning from fewer downstream examples-and demonstrates the superiority of BROS over previous methods.*
This model was contributed by [jinho8345](https://huggingface.co/jinho8345). The original code can be found [here](https://github.com/clovaai/bros).
## Usage tips and examples
- [`~transformers.BrosModel.forward`] requires `input_ids` and `bbox` (bounding box). Each bounding box should be in (x0, y0, x1, y1) format (top-left corner, bottom-right corner). Obtaining of Bounding boxes depends on external OCR system. The `x` coordinate should be normalized by document image width, and the `y` coordinate should be normalized by document image height.
```python
def expand_and_normalize_bbox(bboxes, doc_width, doc_height):
# here, bboxes are numpy array
# Normalize bbox -> 0 ~ 1
bboxes[:, [0, 2]] = bboxes[:, [0, 2]] / width
bboxes[:, [1, 3]] = bboxes[:, [1, 3]] / height
```
- [`~transformers.BrosForTokenClassification.forward`, `~transformers.BrosSpadeEEForTokenClassification.forward`, `~transformers.BrosSpadeEEForTokenClassification.forward`] require not only `input_ids` and `bbox` but also `box_first_token_mask` for loss calculation. It is a mask to filter out non-first tokens of each box. You can obtain this mask by saving start token indices of bounding boxes when creating `input_ids` from words. You can make `box_first_token_mask` with following code,
```python
def make_box_first_token_mask(bboxes, words, tokenizer, max_seq_length=512):
box_first_token_mask = np.zeros(max_seq_length, dtype=np.bool_)
# encode(tokenize) each word from words (List[str])
input_ids_list: List[List[int]] = [tokenizer.encode(e, add_special_tokens=False) for e in words]
# get the length of each box
tokens_length_list: List[int] = [len(l) for l in input_ids_list]
box_end_token_indices = np.array(list(itertools.accumulate(tokens_length_list)))
box_start_token_indices = box_end_token_indices - np.array(tokens_length_list)
# filter out the indices that are out of max_seq_length
box_end_token_indices = box_end_token_indices[box_end_token_indices < max_seq_length - 1]
if len(box_start_token_indices) > len(box_end_token_indices):
box_start_token_indices = box_start_token_indices[: len(box_end_token_indices)]
# set box_start_token_indices to True
box_first_token_mask[box_start_token_indices] = True
return box_first_token_mask
```
## Resources
- Demo scripts can be found [here](https://github.com/clovaai/bros).
## BrosConfig
[[autodoc]] BrosConfig
## BrosProcessor
[[autodoc]] BrosProcessor
- __call__
## BrosModel
[[autodoc]] BrosModel
- forward
## BrosForTokenClassification
[[autodoc]] BrosForTokenClassification
- forward
## BrosSpadeEEForTokenClassification
[[autodoc]] BrosSpadeEEForTokenClassification
- forward
## BrosSpadeELForTokenClassification
[[autodoc]] BrosSpadeELForTokenClassification
- forward
| huggingface/transformers/blob/main/docs/source/en/model_doc/bros.md |
!--Copyright 2022 The HuggingFace Team. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
the License. You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
specific language governing permissions and limitations under the License.
⚠️ Note that this file is in Markdown but contain specific syntax for our doc-builder (similar to MDX) that may not be
rendered properly in your Markdown viewer.
-->
# PEGASUS-X
## Overview
The PEGASUS-X model was proposed in [Investigating Efficiently Extending Transformers for Long Input Summarization](https://arxiv.org/abs/2208.04347) by Jason Phang, Yao Zhao and Peter J. Liu.
PEGASUS-X (PEGASUS eXtended) extends the PEGASUS models for long input summarization through additional long input pretraining and using staggered block-local attention with global tokens in the encoder.
The abstract from the paper is the following:
*While large pretrained Transformer models have proven highly capable at tackling natural language tasks, handling long sequence inputs continues to be a significant challenge. One such task is long input summarization, where inputs are longer than the maximum input context of most pretrained models. Through an extensive set of experiments, we investigate what model architectural changes and pretraining paradigms can most efficiently adapt a pretrained Transformer for long input summarization. We find that a staggered, block-local Transformer with global encoder tokens strikes a good balance of performance and efficiency, and that an additional pretraining phase on long sequences meaningfully improves downstream summarization performance. Based on our findings, we introduce PEGASUS-X, an extension of the PEGASUS model with additional long input pretraining to handle inputs of up to 16K tokens. PEGASUS-X achieves strong performance on long input summarization tasks comparable with much larger models while adding few additional parameters and not requiring model parallelism to train.*
This model was contributed by [zphang](<https://huggingface.co/zphang). The original code can be found [here](https://github.com/google-research/pegasus).
## Documentation resources
- [Translation task guide](../tasks/translation)
- [Summarization task guide](../tasks/summarization)
<Tip>
PEGASUS-X uses the same tokenizer as [PEGASUS](pegasus).
</Tip>
## PegasusXConfig
[[autodoc]] PegasusXConfig
## PegasusXModel
[[autodoc]] PegasusXModel
- forward
## PegasusXForConditionalGeneration
[[autodoc]] PegasusXForConditionalGeneration
- forward
| huggingface/transformers/blob/main/docs/source/en/model_doc/pegasus_x.md |