Datasets:

davanstrien

/

model_cards_with_readmes

like 1

nejox/distilbert-base-uncased-distilled-squad-coffee20230108

nejox

distilbert

transformers

question-answering

apache-2.0

['generated_from_trainer']

<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # distilbert-base-uncased-distilled-squad-coffee20230108 This model is a fine-tuned version of [distilbert-base-uncased-distilled-squad](https://huggingface.co/distilbert-base-uncased-distilled-squad) on the None dataset. It achieves the following results on the evaluation set: - Loss: 4.3444 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 15 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | No log | 1.0 | 89 | 1.9198 | | 2.3879 | 2.0 | 178 | 1.8526 | | 1.5528 | 3.0 | 267 | 1.8428 | | 1.1473 | 4.0 | 356 | 2.4035 | | 0.7375 | 5.0 | 445 | 2.3232 | | 0.5986 | 6.0 | 534 | 2.4550 | | 0.4252 | 7.0 | 623 | 3.2831 | | 0.2612 | 8.0 | 712 | 3.2129 | | 0.143 | 9.0 | 801 | 3.7849 | | 0.143 | 10.0 | 890 | 3.8476 | | 0.0984 | 11.0 | 979 | 4.1742 | | 0.0581 | 12.0 | 1068 | 4.3476 | | 0.0157 | 13.0 | 1157 | 4.3818 | | 0.0131 | 14.0 | 1246 | 4.3357 | | 0.0059 | 15.0 | 1335 | 4.3444 | ### Framework versions - Transformers 4.25.1 - Pytorch 1.11.0+cu113 - Datasets 2.8.0 - Tokenizers 0.13.2

ac50711e4c51d792b26d642b1aa8a847

gokuls/mobilebert_sa_GLUE_Experiment_logit_kd_stsb_128

gokuls

mobilebert

transformers

text-classification

apache-2.0

['en']

['glue']

['generated_from_trainer']

<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # mobilebert_sa_GLUE_Experiment_logit_kd_stsb_128 This model is a fine-tuned version of [google/mobilebert-uncased](https://huggingface.co/google/mobilebert-uncased) on the GLUE STSB dataset. It achieves the following results on the evaluation set: - Loss: 1.1533 - Pearson: 0.0554 - Spearmanr: 0.0563 - Combined Score: 0.0558 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 128 - eval_batch_size: 128 - seed: 10 - distributed_type: multi-GPU - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 50 ### Training results | Training Loss | Epoch | Step | Validation Loss | Pearson | Spearmanr | Combined Score | |:-------------:|:-----:|:----:|:---------------:|:-------:|:---------:|:--------------:| | 2.5973 | 1.0 | 45 | 1.2342 | -0.0353 | -0.0325 | -0.0339 | | 1.0952 | 2.0 | 90 | 1.1740 | 0.0434 | 0.0419 | 0.0426 | | 1.0581 | 3.0 | 135 | 1.1533 | 0.0554 | 0.0563 | 0.0558 | | 1.0455 | 4.0 | 180 | 1.2131 | 0.0656 | 0.0690 | 0.0673 | | 0.9795 | 5.0 | 225 | 1.3883 | 0.0868 | 0.0858 | 0.0863 | | 0.9197 | 6.0 | 270 | 1.4141 | 0.1181 | 0.1148 | 0.1165 | | 0.8182 | 7.0 | 315 | 1.3460 | 0.1771 | 0.1853 | 0.1812 | | 0.6796 | 8.0 | 360 | 1.1577 | 0.2286 | 0.2340 | 0.2313 | ### Framework versions - Transformers 4.26.0 - Pytorch 1.14.0a0+410ce96 - Datasets 2.9.0 - Tokenizers 0.13.2

d67261525a22e75eab30846a0dbc5531

microsoft/xclip-base-patch16-hmdb-2-shot

microsoft

xclip

transformers

feature-extraction

mit

['en']

['vision', 'video-classification']

# X-CLIP (base-sized model) X-CLIP model (base-sized, patch resolution of 16) trained in a few-shot fashion (K=2) on [HMDB-51](https://serre-lab.clps.brown.edu/resource/hmdb-a-large-human-motion-database/). It was introduced in the paper [Expanding Language-Image Pretrained Models for General Video Recognition](https://arxiv.org/abs/2208.02816) by Ni et al. and first released in [this repository](https://github.com/microsoft/VideoX/tree/master/X-CLIP). This model was trained using 32 frames per video, at a resolution of 224x224. Disclaimer: The team releasing X-CLIP did not write a model card for this model so this model card has been written by the Hugging Face team. ## Model description X-CLIP is a minimal extension of [CLIP](https://huggingface.co/docs/transformers/model_doc/clip) for general video-language understanding. The model is trained in a contrastive way on (video, text) pairs.  This allows the model to be used for tasks like zero-shot, few-shot or fully supervised video classification and video-text retrieval. ## Intended uses & limitations You can use the raw model for determining how well text goes with a given video. See the [model hub](https://huggingface.co/models?search=microsoft/xclip) to look for fine-tuned versions on a task that interests you. ### How to use For code examples, we refer to the [documentation](https://huggingface.co/transformers/main/model_doc/xclip.html#). ## Training data This model was trained on [HMDB-51](https://serre-lab.clps.brown.edu/resource/hmdb-a-large-human-motion-database/). ### Preprocessing The exact details of preprocessing during training can be found [here](https://github.com/microsoft/VideoX/blob/40f6d177e0a057a50ac69ac1de6b5938fd268601/X-CLIP/datasets/build.py#L247). The exact details of preprocessing during validation can be found [here](https://github.com/microsoft/VideoX/blob/40f6d177e0a057a50ac69ac1de6b5938fd268601/X-CLIP/datasets/build.py#L285). During validation, one resizes the shorter edge of each frame, after which center cropping is performed to a fixed-size resolution (like 224x224). Next, frames are normalized across the RGB channels with the ImageNet mean and standard deviation. ## Evaluation results This model achieves a top-1 accuracy of 53.0%.

c78f56c7cbd357af76c7855b4177f332

facebook/wmt19-en-ru

facebook

fsmt

transformers

translation

apache-2.0

['en', 'ru']

['wmt19']

['translation', 'wmt19', 'facebook']

# FSMT ## Model description This is a ported version of [fairseq wmt19 transformer](https://github.com/pytorch/fairseq/blob/master/examples/wmt19/README.md) for en-ru. For more details, please see, [Facebook FAIR's WMT19 News Translation Task Submission](https://arxiv.org/abs/1907.06616). The abbreviation FSMT stands for FairSeqMachineTranslation All four models are available: * [wmt19-en-ru](https://huggingface.co/facebook/wmt19-en-ru) * [wmt19-ru-en](https://huggingface.co/facebook/wmt19-ru-en) * [wmt19-en-de](https://huggingface.co/facebook/wmt19-en-de) * [wmt19-de-en](https://huggingface.co/facebook/wmt19-de-en) ## Intended uses & limitations #### How to use ```python from transformers import FSMTForConditionalGeneration, FSMTTokenizer mname = "facebook/wmt19-en-ru" tokenizer = FSMTTokenizer.from_pretrained(mname) model = FSMTForConditionalGeneration.from_pretrained(mname) input = "Machine learning is great, isn't it?" input_ids = tokenizer.encode(input, return_tensors="pt") outputs = model.generate(input_ids) decoded = tokenizer.decode(outputs[0], skip_special_tokens=True) print(decoded) # Машинное обучение - это здорово, не так ли? ``` #### Limitations and bias - The original (and this ported model) doesn't seem to handle well inputs with repeated sub-phrases, [content gets truncated](https://discuss.huggingface.co/t/issues-with-translating-inputs-containing-repeated-phrases/981) ## Training data Pretrained weights were left identical to the original model released by fairseq. For more details, please, see the [paper](https://arxiv.org/abs/1907.06616). ## Eval results pair | fairseq | transformers -------|---------|---------- en-ru | [36.4](http://matrix.statmt.org/matrix/output/1914?run_id=6724) | 33.47 The score is slightly below the score reported by `fairseq`, since `transformers`` currently doesn't support: - model ensemble, therefore the best performing checkpoint was ported (``model4.pt``). - re-ranking The score was calculated using this code: ```bash git clone https://github.com/huggingface/transformers cd transformers export PAIR=en-ru export DATA_DIR=data/$PAIR export SAVE_DIR=data/$PAIR export BS=8 export NUM_BEAMS=15 mkdir -p $DATA_DIR sacrebleu -t wmt19 -l $PAIR --echo src > $DATA_DIR/val.source sacrebleu -t wmt19 -l $PAIR --echo ref > $DATA_DIR/val.target echo $PAIR PYTHONPATH="src:examples/seq2seq" python examples/seq2seq/run_eval.py facebook/wmt19-$PAIR $DATA_DIR/val.source $SAVE_DIR/test_translations.txt --reference_path $DATA_DIR/val.target --score_path $SAVE_DIR/test_bleu.json --bs $BS --task translation --num_beams $NUM_BEAMS ``` note: fairseq reports using a beam of 50, so you should get a slightly higher score if re-run with `--num_beams 50`. ## Data Sources - [training, etc.](http://www.statmt.org/wmt19/) - [test set](http://matrix.statmt.org/test_sets/newstest2019.tgz?1556572561) ### BibTeX entry and citation info ```bibtex @inproceedings{..., year={2020}, title={Facebook FAIR's WMT19 News Translation Task Submission}, author={Ng, Nathan and Yee, Kyra and Baevski, Alexei and Ott, Myle and Auli, Michael and Edunov, Sergey}, booktitle={Proc. of WMT}, } ``` ## TODO - port model ensemble (fairseq uses 4 model checkpoints)

09fd5ca751e6c96921792d1b942ec023

PeterBanning71/t5-small-finetuned-xsum-finetuned-bioMedv3

PeterBanning71

t5

transformers

summarization

apache-2.0

['summarization', 'generated_from_trainer']

<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # t5-small-finetuned-xsum-finetuned-bioMedv3 This model is a fine-tuned version of [PeterBanning71/t5-small-finetuned-xsum](https://huggingface.co/PeterBanning71/t5-small-finetuned-xsum) on the None dataset. It achieves the following results on the evaluation set: - Loss: 6.1056 - Rouge1: 4.8565 - Rouge2: 0.4435 - Rougel: 3.9735 - Rougelsum: 4.415 - Gen Len: 19.0 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 24 - eval_batch_size: 24 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 8 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:| | No log | 1.0 | 1 | 8.4025 | 4.8565 | 0.4435 | 3.9735 | 4.415 | 19.0 | | No log | 2.0 | 2 | 8.4025 | 4.8565 | 0.4435 | 3.9735 | 4.415 | 19.0 | | No log | 3.0 | 3 | 7.7250 | 4.8565 | 0.4435 | 3.9735 | 4.415 | 19.0 | | No log | 4.0 | 4 | 7.1617 | 4.8565 | 0.4435 | 3.9735 | 4.415 | 19.0 | | No log | 5.0 | 5 | 6.7113 | 4.8565 | 0.4435 | 3.9735 | 4.415 | 19.0 | | No log | 6.0 | 6 | 6.3646 | 4.8565 | 0.4435 | 3.9735 | 4.415 | 19.0 | | No log | 7.0 | 7 | 6.1056 | 4.8565 | 0.4435 | 3.9735 | 4.415 | 19.0 | | No log | 8.0 | 8 | 6.1056 | 4.8565 | 0.4435 | 3.9735 | 4.415 | 19.0 | ### Framework versions - Transformers 4.26.0 - Pytorch 1.13.1+cu116 - Datasets 2.9.0 - Tokenizers 0.13.2

fd41830000499dbb6d5db2af04fc04e4

yip-i/xls-r-53-copy

yip-i

wav2vec2

transformers

apache-2.0

['multilingual']

['common_voice']

['speech']

# Wav2Vec2-XLSR-53 [Facebook's XLSR-Wav2Vec2](https://ai.facebook.com/blog/wav2vec-20-learning-the-structure-of-speech-from-raw-audio/) The base model pretrained on 16kHz sampled speech audio. When using the model make sure that your speech input is also sampled at 16Khz. Note that this model should be fine-tuned on a downstream task, like Automatic Speech Recognition. Check out [this blog](https://huggingface.co/blog/fine-tune-wav2vec2-english) for more information. [Paper](https://arxiv.org/abs/2006.13979) Authors: Alexis Conneau, Alexei Baevski, Ronan Collobert, Abdelrahman Mohamed, Michael Auli **Abstract** 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 model can be found under https://github.com/pytorch/fairseq/tree/master/examples/wav2vec#wav2vec-20. # Usage See [this notebook](https://colab.research.google.com/github/patrickvonplaten/notebooks/blob/master/Fine_Tune_XLSR_Wav2Vec2_on_Turkish_ASR_with_%F0%9F%A4%97_Transformers.ipynb) for more information on how to fine-tune the model. 

fb0df48764b64890ae5c043865e65d6e

google/t5-11b-ssm-wq

google

t5

transformers

text2text-generation

apache-2.0

['en']

['c4', 'wikipedia', 'web_questions']

[]

[Google's T5](https://ai.googleblog.com/2020/02/exploring-transfer-learning-with-t5.html) for **Closed Book Question Answering**. The model was pre-trained using T5's denoising objective on [C4](https://huggingface.co/datasets/c4), subsequently additionally pre-trained using [REALM](https://arxiv.org/pdf/2002.08909.pdf)'s salient span masking objective on [Wikipedia](https://huggingface.co/datasets/wikipedia), and finally fine-tuned on [Web Questions (WQ)](https://huggingface.co/datasets/web_questions). **Note**: The model was fine-tuned on 100% of the train splits of [Web Questions (WQ)](https://huggingface.co/datasets/web_questions) for 10k steps. Other community Checkpoints: [here](https://huggingface.co/models?search=ssm) Paper: [How Much Knowledge Can You Pack Into the Parameters of a Language Model?](https://arxiv.org/abs/1910.10683.pdf) Authors: *Adam Roberts, Colin Raffel, Noam Shazeer* ## Results on Web Questions - Test Set |Id | link | Exact Match | |---|---|---| |**T5-11b**|**https://huggingface.co/google/t5-11b-ssm-wq**|**44.7**| |T5-xxl|https://huggingface.co/google/t5-xxl-ssm-wq|43.5| ## Usage The model can be used as follows for **closed book question answering**: ```python from transformers import AutoModelForSeq2SeqLM, AutoTokenizer t5_qa_model = AutoModelForSeq2SeqLM.from_pretrained("google/t5-11b-ssm-wq") t5_tok = AutoTokenizer.from_pretrained("google/t5-11b-ssm-wq") input_ids = t5_tok("When was Franklin D. Roosevelt born?", return_tensors="pt").input_ids gen_output = t5_qa_model.generate(input_ids)[0] print(t5_tok.decode(gen_output, skip_special_tokens=True)) ``` ## Abstract It has recently been observed that neural language models trained on unstructured text can implicitly store and retrieve knowledge using natural language queries. In this short paper, we measure the practical utility of this approach by fine-tuning pre-trained models to answer questions without access to any external context or knowledge. We show that this approach scales with model size and performs competitively with open-domain systems that explicitly retrieve answers from an external knowledge source when answering questions. To facilitate reproducibility and future work, we release our code and trained models at https://goo.gle/t5-cbqa. 

421f2b02195337d45d10a6dd9600d571

josetapia/hygpt2-clm

josetapia

gpt2

transformers

text-generation

mit

['generated_from_trainer']

<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # hygpt2-clm This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on the None dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0005 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - gradient_accumulation_steps: 8 - total_train_batch_size: 256 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 1000 - num_epochs: 4000 ### Training results ### Framework versions - Transformers 4.17.0 - Pytorch 1.10.2 - Datasets 1.18.4 - Tokenizers 0.11.6

4e288d13e1a2a45f6aa2104c6a908f1d

terzimert/bert-finetuned-ner-v2.2

terzimert

bert

transformers

token-classification

apache-2.0

['caner']

['generated_from_trainer']

<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # bert-finetuned-ner-v2.2 This model is a fine-tuned version of [bert-base-multilingual-cased](https://huggingface.co/bert-base-multilingual-cased) on the caner dataset. It achieves the following results on the evaluation set: - Loss: 0.3595 - Precision: 0.8823 - Recall: 0.8497 - F1: 0.8657 - Accuracy: 0.9427 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | 0.2726 | 1.0 | 3228 | 0.4504 | 0.7390 | 0.7287 | 0.7338 | 0.9107 | | 0.2057 | 2.0 | 6456 | 0.3679 | 0.8633 | 0.8446 | 0.8538 | 0.9385 | | 0.1481 | 3.0 | 9684 | 0.3595 | 0.8823 | 0.8497 | 0.8657 | 0.9427 | ### Framework versions - Transformers 4.26.0 - Pytorch 1.13.1+cu116 - Datasets 2.9.0 - Tokenizers 0.13.2

c1a5f866053a6d759a96278f6c27ab14

openclimatefix/nowcasting_cnn_v4

openclimatefix

transformers

mit

['nowcasting', 'forecasting', 'timeseries', 'remote-sensing']

# Nowcasting CNN ## Model description 3d conv model, that takes in different data streams architecture is roughly 1. satellite image time series goes into many 3d convolution layers. 2. nwp time series goes into many 3d convolution layers. 3. Final convolutional layer goes to full connected layer. This is joined by other data inputs like - pv yield - time variables Then there ~4 fully connected layers which end up forecasting the pv yield / gsp into the future ## Intended uses & limitations Forecasting short term PV power for different regions and nationally in the UK ## How to use [More information needed] ## Limitations and bias [More information needed] ## Training data Training data is EUMETSAT RSS imagery over the UK, on-the-ground PV data, and NWP predictions. ## Training procedure [More information needed] ## Evaluation results [More information needed]

409a984bb15368014d80cc8164fc5303

Thant123/distilbert-base-uncased-finetuned-emotion

Thant123

distilbert

transformers

text-classification

apache-2.0

['emotion']

['generated_from_trainer']

<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # distilbert-base-uncased-finetuned-emotion This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emotion dataset. It achieves the following results on the evaluation set: - Loss: 0.2270 - Accuracy: 0.924 - F1: 0.9241 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.8204 | 1.0 | 250 | 0.3160 | 0.9035 | 0.9008 | | 0.253 | 2.0 | 500 | 0.2270 | 0.924 | 0.9241 | ### Framework versions - Transformers 4.17.0 - Pytorch 1.10.0+cu111 - Datasets 2.0.0 - Tokenizers 0.11.6

ffacf1d2dcc9b780be66d5ad7b68e5e2

philschmid/roberta-base-squad2-optimized

philschmid

generic

mit

['endpoints-template', 'optimum']

# Optimized and Quantized [deepset/roberta-base-squad2](https://huggingface.co/deepset/roberta-base-squad2) with a custom handler.py This repository implements a `custom` handler for `question-answering` for 🤗 Inference Endpoints for accelerated inference using [🤗 Optiumum](https://huggingface.co/docs/optimum/index). The code for the customized handler is in the [handler.py](https://huggingface.co/philschmid/roberta-base-squad2-optimized/blob/main/handler.py). Below is also describe how we converted & optimized the model, based on the [Accelerate Transformers with Hugging Face Optimum](https://huggingface.co/blog/optimum-inference) blog post. You can also check out the [notebook](https://huggingface.co/philschmid/roberta-base-squad2-optimized/blob/main/optimize_model.ipynb). ### expected Request payload ```json { "inputs": { "question": "As what is Philipp working?", "context": "Hello, my name is Philipp and I live in Nuremberg, Germany. Currently I am working as a Technical Lead at Hugging Face to democratize artificial intelligence through open source and open science. In the past I designed and implemented cloud-native machine learning architectures for fin-tech and insurance companies. I found my passion for cloud concepts and machine learning 5 years ago. Since then I never stopped learning. Currently, I am focusing myself in the area NLP and how to leverage models like BERT, Roberta, T5, ViT, and GPT2 to generate business value." } } ``` below is an example on how to run a request using Python and `requests`. ## Run Request ```python import json from typing import List import requests as r import base64 ENDPOINT_URL = "" HF_TOKEN = "" def predict(question:str=None,context:str=None): payload = {"inputs": {"question": question, "context": context}} response = r.post( ENDPOINT_URL, headers={"Authorization": f"Bearer {HF_TOKEN}"}, json=payload ) return response.json() prediction = predict( question="As what is Philipp working?", context="Hello, my name is Philipp and I live in Nuremberg, Germany. Currently I am working as a Technical Lead at Hugging Face to democratize artificial intelligence through open source and open science." ) ``` expected output ```python { 'score': 0.4749588668346405, 'start': 88, 'end': 102, 'answer': 'Technical Lead' } ``` # Convert & Optimize model with Optimum Steps: 1. [Convert model to ONNX](#1-convert-model-to-onnx) 2. [Optimize & quantize model with Optimum](#2-optimize--quantize-model-with-optimum) 3. [Create Custom Handler for Inference Endpoints](#3-create-custom-handler-for-inference-endpoints) 4. [Test Custom Handler Locally](#4-test-custom-handler-locally) 5. [Push to repository and create Inference Endpoint](#5-push-to-repository-and-create-inference-endpoint) Helpful links: * [Accelerate Transformers with Hugging Face Optimum](https://huggingface.co/blog/optimum-inference) * [Optimizing Transformers for GPUs with Optimum](https://www.philschmid.de/optimizing-transformers-with-optimum-gpu) * [Optimum Documentation](https://huggingface.co/docs/optimum/onnxruntime/modeling_ort) * [Create Custom Handler Endpoints](https://link-to-docs) ## Setup & Installation ```python %%writefile requirements.txt optimum[onnxruntime]==1.4.0 mkl-include mkl ``` ```python !pip install -r requirements.txt ``` ## 0. Base line Performance ```python from transformers import pipeline qa = pipeline("question-answering",model="deepset/roberta-base-squad2") ``` Okay, let's test the performance (latency) with sequence length of 128. ```python context="Hello, my name is Philipp and I live in Nuremberg, Germany. Currently I am working as a Technical Lead at Hugging Face to democratize artificial intelligence through open source and open science. In the past I designed and implemented cloud-native machine learning architectures for fin-tech and insurance companies. I found my passion for cloud concepts and machine learning 5 years ago. Since then I never stopped learning. Currently, I am focusing myself in the area NLP and how to leverage models like BERT, Roberta, T5, ViT, and GPT2 to generate business value." question="As what is Philipp working?" payload = {"inputs": {"question": question, "context": context}} ``` ```python from time import perf_counter import numpy as np def measure_latency(pipe,payload): latencies = [] # warm up for _ in range(10): _ = pipe(question=payload["inputs"]["question"], context=payload["inputs"]["context"]) # Timed run for _ in range(50): start_time = perf_counter() _ = pipe(question=payload["inputs"]["question"], context=payload["inputs"]["context"]) latency = perf_counter() - start_time latencies.append(latency) # Compute run statistics time_avg_ms = 1000 * np.mean(latencies) time_std_ms = 1000 * np.std(latencies) return f"Average latency (ms) - {time_avg_ms:.2f} +\- {time_std_ms:.2f}" print(f"Vanilla model {measure_latency(qa,payload)}") # Vanilla model Average latency (ms) - 64.15 +\- 2.44 ``` ## 1. Convert model to ONNX ```python from optimum.onnxruntime import ORTModelForQuestionAnswering from transformers import AutoTokenizer from pathlib import Path model_id="deepset/roberta-base-squad2" onnx_path = Path(".") # load vanilla transformers and convert to onnx model = ORTModelForQuestionAnswering.from_pretrained(model_id, from_transformers=True) tokenizer = AutoTokenizer.from_pretrained(model_id) # save onnx checkpoint and tokenizer model.save_pretrained(onnx_path) tokenizer.save_pretrained(onnx_path) ``` ## 2. Optimize & quantize model with Optimum ```python from optimum.onnxruntime import ORTOptimizer, ORTQuantizer from optimum.onnxruntime.configuration import OptimizationConfig, AutoQuantizationConfig # Create the optimizer optimizer = ORTOptimizer.from_pretrained(model) # Define the optimization strategy by creating the appropriate configuration optimization_config = OptimizationConfig(optimization_level=99) # enable all optimizations # Optimize the model optimizer.optimize(save_dir=onnx_path, optimization_config=optimization_config) ``` ```python # create ORTQuantizer and define quantization configuration dynamic_quantizer = ORTQuantizer.from_pretrained(onnx_path, file_name="model_optimized.onnx") dqconfig = AutoQuantizationConfig.avx512_vnni(is_static=False, per_channel=False) # apply the quantization configuration to the model model_quantized_path = dynamic_quantizer.quantize( save_dir=onnx_path, quantization_config=dqconfig, ) ``` ## 3. Create Custom Handler for Inference Endpoints ```python %%writefile handler.py from typing import Dict, List, Any from optimum.onnxruntime import ORTModelForQuestionAnswering from transformers import AutoTokenizer, pipeline class EndpointHandler(): def __init__(self, path=""): # load the optimized model self.model = ORTModelForQuestionAnswering.from_pretrained(path, file_name="model_optimized_quantized.onnx") self.tokenizer = AutoTokenizer.from_pretrained(path) # create pipeline self.pipeline = pipeline("question-answering", model=self.model, tokenizer=self.tokenizer) def __call__(self, data: Any) -> List[List[Dict[str, float]]]: """ Args: data (:obj:): includes the input data and the parameters for the inference. Return: A :obj:`list`:. The list contains the answer and scores of the inference inputs """ inputs = data.get("inputs", data) # run the model prediction = self.pipeline(**inputs) # return prediction return prediction ``` ## 4. Test Custom Handler Locally ```python from handler import EndpointHandler # init handler my_handler = EndpointHandler(path=".") # prepare sample payload context="Hello, my name is Philipp and I live in Nuremberg, Germany. Currently I am working as a Technical Lead at Hugging Face to democratize artificial intelligence through open source and open science. In the past I designed and implemented cloud-native machine learning architectures for fin-tech and insurance companies. I found my passion for cloud concepts and machine learning 5 years ago. Since then I never stopped learning. Currently, I am focusing myself in the area NLP and how to leverage models like BERT, Roberta, T5, ViT, and GPT2 to generate business value." question="As what is Philipp working?" payload = {"inputs": {"question": question, "context": context}} # test the handler my_handler(payload) ``` ```python from time import perf_counter import numpy as np def measure_latency(handler,payload): latencies = [] # warm up for _ in range(10): _ = handler(payload) # Timed run for _ in range(50): start_time = perf_counter() _ = handler(payload) latency = perf_counter() - start_time latencies.append(latency) # Compute run statistics time_avg_ms = 1000 * np.mean(latencies) time_std_ms = 1000 * np.std(latencies) return f"Average latency (ms) - {time_avg_ms:.2f} +\- {time_std_ms:.2f}" print(f"Optimized & Quantized model {measure_latency(my_handler,payload)}") # ``` `Optimized & Quantized model Average latency (ms) - 29.90 +\- 0.53` `Vanilla model Average latency (ms) - 64.15 +\- 2.44` ## 5. Push to repository and create Inference Endpoint ```python # add all our new files !git add * # commit our files !git commit -m "add custom handler" # push the files to the hub !git push ```

8561f0d74d18810e336a6fc8caf0ae6d

MaggieXM/distilbert-base-uncased-finetuned-squad

MaggieXM

distilbert

transformers

question-answering

apache-2.0

['squad']

['generated_from_trainer']

<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # distilbert-base-uncased-finetuned-squad This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the squad dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 0.01 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | No log | 0.01 | 56 | 4.8054 | ### Framework versions - Transformers 4.16.2 - Pytorch 1.10.0+cu111 - Datasets 1.18.3 - Tokenizers 0.11.0

b71ec1cf30fd6b9f371d478067525884

jonatasgrosman/exp_w2v2t_pt_vp-nl_s6

jonatasgrosman

wav2vec2

transformers

automatic-speech-recognition

apache-2.0

['pt']

['mozilla-foundation/common_voice_7_0']

['automatic-speech-recognition', 'pt']

# exp_w2v2t_pt_vp-nl_s6 Fine-tuned [facebook/wav2vec2-large-nl-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-nl-voxpopuli) for speech recognition using the train split of [Common Voice 7.0 (pt)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using this model, make sure that your speech input is sampled at 16kHz. This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.

7a000bd97bc0b74b5287e62948946ec7

hsohn3/cchs-bert-visit-uncased-wordlevel-block512-batch8-ep10

hsohn3

bert

transformers

fill-mask

apache-2.0

['generated_from_keras_callback']

<!-- This model card has been generated automatically according to the information Keras had access to. You should probably proofread and complete it, then remove this comment. --> # hsohn3/cchs-bert-visit-uncased-wordlevel-block512-batch8-ep10 This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 2.9857 - Epoch: 9 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'AdamWeightDecay', 'learning_rate': 2e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight_decay_rate': 0.01} - training_precision: float32 ### Training results | Train Loss | Epoch | |:----------:|:-----:| | 4.4277 | 0 | | 3.1148 | 1 | | 3.0454 | 2 | | 3.0227 | 3 | | 3.0048 | 4 | | 3.0080 | 5 | | 2.9920 | 6 | | 2.9963 | 7 | | 2.9892 | 8 | | 2.9857 | 9 | ### Framework versions - Transformers 4.20.1 - TensorFlow 2.8.2 - Datasets 2.3.2 - Tokenizers 0.12.1

d1d10ad0216333d9b17d1427aae2e8d4

shumail/wav2vec2-base-timit-demo-colab

shumail

wav2vec2

transformers

automatic-speech-recognition

apache-2.0

['generated_from_trainer']

<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # wav2vec2-base-timit-demo-colab This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.8686 - Wer: 0.6263 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 1000 - num_epochs: 30 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 5.0505 | 13.89 | 500 | 3.0760 | 1.0 | | 1.2748 | 27.78 | 1000 | 0.8686 | 0.6263 | ### Framework versions - Transformers 4.11.3 - Pytorch 1.11.0+cu113 - Datasets 1.18.3 - Tokenizers 0.10.3

21419281cc9c65d8413aab2df9d3ffbe

fveredas/xlm-roberta-base-finetuned-panx-de

fveredas

xlm-roberta

transformers

token-classification

mit

['xtreme']

['generated_from_trainer']

<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # xlm-roberta-base-finetuned-panx-de This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the xtreme dataset. It achieves the following results on the evaluation set: - Loss: 0.1365 - F1: 0.8649 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 24 - eval_batch_size: 24 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.2553 | 1.0 | 525 | 0.1575 | 0.8279 | | 0.1284 | 2.0 | 1050 | 0.1386 | 0.8463 | | 0.0813 | 3.0 | 1575 | 0.1365 | 0.8649 | ### Framework versions - Transformers 4.11.3 - Pytorch 1.12.1+cu113 - Datasets 1.16.1 - Tokenizers 0.10.3

91b7a03e208a0ae34eca0e47fccabdb1

kurianbenoy/music_genre_classification_baseline

kurianbenoy

fastai

mit

['fastai']

# Amazing! 🥳 Congratulations on hosting your fastai model on the Hugging Face Hub! # Some next steps 1. Fill out this model card with more information (see the template below and the [documentation here](https://huggingface.co/docs/hub/model-repos))! 2. Create a demo in Gradio or Streamlit using 🤗 Spaces ([documentation here](https://huggingface.co/docs/hub/spaces)). 3. Join the fastai community on the [Fastai Discord](https://discord.com/invite/YKrxeNn)! Greetings fellow fastlearner 🤝! Don't forget to delete this content from your model card. --- # Model card ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed

f36361bbf3a4111abdeda44875b284bc

mkhairil/distillbert-finetuned-indonlusmsa

mkhairil

distilbert

transformers

text-classification

apache-2.0

['indonlu']

['generated_from_trainer']

<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # distillbert-finetuned-indonlusmsa This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the indonlu dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Training results ### Framework versions - Transformers 4.26.0 - Pytorch 1.13.1+cu116 - Datasets 2.9.0 - Tokenizers 0.13.2

42b35bdf153d04de3bdfd39be5fd4cfc

Alred/bart-base-finetuned-summarization-cnn-ver2

Alred

bart

transformers

summarization

apache-2.0

['cnn_dailymail']

['summarization', 'generated_from_trainer']

<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # bart-base-finetuned-summarization-cnn-ver2 This model is a fine-tuned version of [facebook/bart-base](https://huggingface.co/facebook/bart-base) on the cnn_dailymail dataset. It achieves the following results on the evaluation set: - Loss: 2.1715 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 1 - eval_batch_size: 1 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 2.3329 | 1.0 | 5742 | 2.1715 | ### Framework versions - Transformers 4.24.0 - Pytorch 1.12.1+cu113 - Datasets 2.7.1 - Tokenizers 0.13.2

25269342aaf4d9a62e88d8b1b5ab5e8a

manandey/wav2vec2-large-xlsr-_irish

manandey

wav2vec2

transformers

automatic-speech-recognition

apache-2.0

['ga']

['common_voice']

['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week', 'hf-asr-leaderboard']

# Wav2Vec2-Large-XLSR-53-Irish Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) in Irish using the [Common Voice](https://huggingface.co/datasets/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", "ga-IE", split="test[:2%]"). processor = Wav2Vec2Processor.from_pretrained("manandey/wav2vec2-large-xlsr-_irish") model = Wav2Vec2ForCTC.from_pretrained("manandey/wav2vec2-large-xlsr-_irish") 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["speech"][:2], 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["sentence"][:2]) ``` ## Evaluation The model can be evaluated as follows on the {language} test data of Common Voice. ```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", "ga-IE", split="test") wer = load_metric("wer") processor = Wav2Vec2Processor.from_pretrained("manandey/wav2vec2-large-xlsr-_irish") model = Wav2Vec2ForCTC.from_pretrained("manandey/wav2vec2-large-xlsr-_irish") model.to("cuda") chars_to_ignore_regex = '[\,\?\.\!\-\;\:\"\“\%\‘\”\�\’\–\(\)]' 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**: 42.34% ## Training The Common Voice `train`, `validation` datasets were used for training.

5019d14cdddfee6804b9e3be5a44eb38

Helsinki-NLP/opus-mt-it-vi

Helsinki-NLP

marian

transformers

translation

apache-2.0

['it', 'vi']

['translation']

### ita-vie * source group: Italian * target group: Vietnamese * OPUS readme: [ita-vie](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/ita-vie/README.md) * model: transformer-align * source language(s): ita * target language(s): vie * model: transformer-align * pre-processing: normalization + SentencePiece (spm32k,spm32k) * download original weights: [opus-2020-06-17.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/ita-vie/opus-2020-06-17.zip) * test set translations: [opus-2020-06-17.test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/ita-vie/opus-2020-06-17.test.txt) * test set scores: [opus-2020-06-17.eval.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/ita-vie/opus-2020-06-17.eval.txt) ## Benchmarks | testset | BLEU | chr-F | |-----------------------|-------|-------| | Tatoeba-test.ita.vie | 36.2 | 0.535 | ### System Info: - hf_name: ita-vie - source_languages: ita - target_languages: vie - opus_readme_url: https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/ita-vie/README.md - original_repo: Tatoeba-Challenge - tags: ['translation'] - languages: ['it', 'vi'] - src_constituents: {'ita'} - tgt_constituents: {'vie', 'vie_Hani'} - src_multilingual: False - tgt_multilingual: False - prepro: normalization + SentencePiece (spm32k,spm32k) - url_model: https://object.pouta.csc.fi/Tatoeba-MT-models/ita-vie/opus-2020-06-17.zip - url_test_set: https://object.pouta.csc.fi/Tatoeba-MT-models/ita-vie/opus-2020-06-17.test.txt - src_alpha3: ita - tgt_alpha3: vie - short_pair: it-vi - chrF2_score: 0.535 - bleu: 36.2 - brevity_penalty: 1.0 - ref_len: 2144.0 - src_name: Italian - tgt_name: Vietnamese - train_date: 2020-06-17 - src_alpha2: it - tgt_alpha2: vi - prefer_old: False - long_pair: ita-vie - helsinki_git_sha: 480fcbe0ee1bf4774bcbe6226ad9f58e63f6c535 - transformers_git_sha: 2207e5d8cb224e954a7cba69fa4ac2309e9ff30b - port_machine: brutasse - port_time: 2020-08-21-14:41

449a6592d1e9c61ddf102c80ed93f5c6

coreml/coreml-stable-diffusion-v1-5

coreml

text-to-image

creativeml-openrail-m

['coreml', 'stable-diffusion', 'text-to-image']

# Core ML Converted Model This model was converted to Core ML for use on Apple Silicon devices by following Apple's instructions [here](https://github.com/apple/ml-stable-diffusion#-converting-models-to-core-ml).<br> Provide the model to an app such as [Mochi Diffusion](https://github.com/godly-devotion/MochiDiffusion) to generate images.<br> `split_einsum` version is compatible with all compute unit options including Neural Engine.<br> `original` version is only compatible with CPU & GPU option. # Stable Diffusion v1-5 Model Card Stable Diffusion is a latent text-to-image diffusion model capable of generating photo-realistic images given any text input. For more information about how Stable Diffusion functions, please have a look at [🤗's Stable Diffusion blog](https://huggingface.co/blog/stable_diffusion). The **Stable-Diffusion-v1-5** checkpoint was initialized with the weights of the [Stable-Diffusion-v1-2](https:/steps/huggingface.co/CompVis/stable-diffusion-v1-2) checkpoint and subsequently fine-tuned on 595k steps at resolution 512x512 on "laion-aesthetics v2 5+" and 10% dropping of the text-conditioning to improve [classifier-free guidance sampling](https://arxiv.org/abs/2207.12598). You can use this both with the [🧨Diffusers library](https://github.com/huggingface/diffusers) and the [RunwayML GitHub repository](https://github.com/runwayml/stable-diffusion). ### Diffusers ```py from diffusers import StableDiffusionPipeline import torch model_id = "runwayml/stable-diffusion-v1-5" pipe = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float16) pipe = pipe.to("cuda") prompt = "a photo of an astronaut riding a horse on mars" image = pipe(prompt).images[0] image.save("astronaut_rides_horse.png") ``` For more detailed instructions, use-cases and examples in JAX follow the instructions [here](https://github.com/huggingface/diffusers#text-to-image-generation-with-stable-diffusion) ### Original GitHub Repository 1. Download the weights - [v1-5-pruned-emaonly.ckpt](https://huggingface.co/runwayml/stable-diffusion-v1-5/resolve/main/v1-5-pruned-emaonly.ckpt) - 4.27GB, ema-only weight. uses less VRAM - suitable for inference - [v1-5-pruned.ckpt](https://huggingface.co/runwayml/stable-diffusion-v1-5/resolve/main/v1-5-pruned.ckpt) - 7.7GB, ema+non-ema weights. uses more VRAM - suitable for fine-tuning 2. Follow instructions [here](https://github.com/runwayml/stable-diffusion). ## Model Details - **Developed by:** Robin Rombach, Patrick Esser - **Model type:** Diffusion-based text-to-image generation model - **Language(s):** English - **License:** [The CreativeML OpenRAIL M license](https://huggingface.co/spaces/CompVis/stable-diffusion-license) is an [Open RAIL M license](https://www.licenses.ai/blog/2022/8/18/naming-convention-of-responsible-ai-licenses), adapted from the work that [BigScience](https://bigscience.huggingface.co/) and [the RAIL Initiative](https://www.licenses.ai/) are jointly carrying in the area of responsible AI licensing. See also [the article about the BLOOM Open RAIL license](https://bigscience.huggingface.co/blog/the-bigscience-rail-license) on which our license is based. - **Model Description:** This is a model that can be used to generate and modify images based on text prompts. It is a [Latent Diffusion Model](https://arxiv.org/abs/2112.10752) that uses a fixed, pretrained text encoder ([CLIP ViT-L/14](https://arxiv.org/abs/2103.00020)) as suggested in the [Imagen paper](https://arxiv.org/abs/2205.11487). - **Resources for more information:** [GitHub Repository](https://github.com/CompVis/stable-diffusion), [Paper](https://arxiv.org/abs/2112.10752). - **Cite as:** @InProceedings{Rombach_2022_CVPR, author = {Rombach, Robin and Blattmann, Andreas and Lorenz, Dominik and Esser, Patrick and Ommer, Bj\"orn}, title = {High-Resolution Image Synthesis With Latent Diffusion Models}, booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)}, month = {June}, year = {2022}, pages = {10684-10695} } # Uses ## Direct Use The model is intended for research purposes only. Possible research areas and tasks include - Safe deployment of models which have the potential to generate harmful content. - Probing and understanding the limitations and biases of generative models. - Generation of artworks and use in design and other artistic processes. - Applications in educational or creative tools. - Research on generative models. Excluded uses are described below. ### Misuse, Malicious Use, and Out-of-Scope Use _Note: This section is taken from the [DALLE-MINI model card](https://huggingface.co/dalle-mini/dalle-mini), but applies in the same way to Stable Diffusion v1_. The model should not be used to intentionally create or disseminate images that create hostile or alienating environments for people. This includes generating images that people would foreseeably find disturbing, distressing, or offensive; or content that propagates historical or current stereotypes. #### Out-of-Scope Use The model was not trained to be factual or true representations of people or events, and therefore using the model to generate such content is out-of-scope for the abilities of this model. #### Misuse and Malicious Use Using the model to generate content that is cruel to individuals is a misuse of this model. This includes, but is not limited to: - Generating demeaning, dehumanizing, or otherwise harmful representations of people or their environments, cultures, religions, etc. - Intentionally promoting or propagating discriminatory content or harmful stereotypes. - Impersonating individuals without their consent. - Sexual content without consent of the people who might see it. - Mis- and disinformation - Representations of egregious violence and gore - Sharing of copyrighted or licensed material in violation of its terms of use. - Sharing content that is an alteration of copyrighted or licensed material in violation of its terms of use. ## Limitations and Bias ### Limitations - The model does not achieve perfect photorealism - The model cannot render legible text - The model does not perform well on more difficult tasks which involve compositionality, such as rendering an image corresponding to “A red cube on top of a blue sphere” - Faces and people in general may not be generated properly. - The model was trained mainly with English captions and will not work as well in other languages. - The autoencoding part of the model is lossy - The model was trained on a large-scale dataset [LAION-5B](https://laion.ai/blog/laion-5b/) which contains adult material and is not fit for product use without additional safety mechanisms and considerations. - No additional measures were used to deduplicate the dataset. As a result, we observe some degree of memorization for images that are duplicated in the training data. The training data can be searched at [https://rom1504.github.io/clip-retrieval/](https://rom1504.github.io/clip-retrieval/) to possibly assist in the detection of memorized images. ### Bias While the capabilities of image generation models are impressive, they can also reinforce or exacerbate social biases. Stable Diffusion v1 was trained on subsets of [LAION-2B(en)](https://laion.ai/blog/laion-5b/), which consists of images that are primarily limited to English descriptions. Texts and images from communities and cultures that use other languages are likely to be insufficiently accounted for. This affects the overall output of the model, as white and western cultures are often set as the default. Further, the ability of the model to generate content with non-English prompts is significantly worse than with English-language prompts. ### Safety Module The intended use of this model is with the [Safety Checker](https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/stable_diffusion/safety_checker.py) in Diffusers. This checker works by checking model outputs against known hard-coded NSFW concepts. The concepts are intentionally hidden to reduce the likelihood of reverse-engineering this filter. Specifically, the checker compares the class probability of harmful concepts in the embedding space of the `CLIPTextModel` *after generation* of the images. The concepts are passed into the model with the generated image and compared to a hand-engineered weight for each NSFW concept. ## Training **Training Data** The model developers used the following dataset for training the model: - LAION-2B (en) and subsets thereof (see next section) **Training Procedure** Stable Diffusion v1-5 is a latent diffusion model which combines an autoencoder with a diffusion model that is trained in the latent space of the autoencoder. During training, - Images are encoded through an encoder, which turns images into latent representations. The autoencoder uses a relative downsampling factor of 8 and maps images of shape H x W x 3 to latents of shape H/f x W/f x 4 - Text prompts are encoded through a ViT-L/14 text-encoder. - The non-pooled output of the text encoder is fed into the UNet backbone of the latent diffusion model via cross-attention. - The loss is a reconstruction objective between the noise that was added to the latent and the prediction made by the UNet. Currently six Stable Diffusion checkpoints are provided, which were trained as follows. - [`stable-diffusion-v1-1`](https://huggingface.co/CompVis/stable-diffusion-v1-1): 237,000 steps at resolution `256x256` on [laion2B-en](https://huggingface.co/datasets/laion/laion2B-en). 194,000 steps at resolution `512x512` on [laion-high-resolution](https://huggingface.co/datasets/laion/laion-high-resolution) (170M examples from LAION-5B with resolution `>= 1024x1024`). - [`stable-diffusion-v1-2`](https://huggingface.co/CompVis/stable-diffusion-v1-2): Resumed from `stable-diffusion-v1-1`. 515,000 steps at resolution `512x512` on "laion-improved-aesthetics" (a subset of laion2B-en, filtered to images with an original size `>= 512x512`, estimated aesthetics score `> 5.0`, and an estimated watermark probability `< 0.5`. The watermark estimate is from the LAION-5B metadata, the aesthetics score is estimated using an [improved aesthetics estimator](https://github.com/christophschuhmann/improved-aesthetic-predictor)). - [`stable-diffusion-v1-3`](https://huggingface.co/CompVis/stable-diffusion-v1-3): Resumed from `stable-diffusion-v1-2` - 195,000 steps at resolution `512x512` on "laion-improved-aesthetics" and 10 % dropping of the text-conditioning to improve [classifier-free guidance sampling](https://arxiv.org/abs/2207.12598). - [`stable-diffusion-v1-4`](https://huggingface.co/CompVis/stable-diffusion-v1-4) Resumed from `stable-diffusion-v1-2` - 225,000 steps at resolution `512x512` on "laion-aesthetics v2 5+" and 10 % dropping of the text-conditioning to improve [classifier-free guidance sampling](https://arxiv.org/abs/2207.12598). - [`stable-diffusion-v1-5`](https://huggingface.co/runwayml/stable-diffusion-v1-5) Resumed from `stable-diffusion-v1-2` - 595,000 steps at resolution `512x512` on "laion-aesthetics v2 5+" and 10 % dropping of the text-conditioning to improve [classifier-free guidance sampling](https://arxiv.org/abs/2207.12598). - [`stable-diffusion-inpainting`](https://huggingface.co/runwayml/stable-diffusion-inpainting) Resumed from `stable-diffusion-v1-5` - then 440,000 steps of inpainting training at resolution 512x512 on “laion-aesthetics v2 5+” and 10% dropping of the text-conditioning. For inpainting, the UNet has 5 additional input channels (4 for the encoded masked-image and 1 for the mask itself) whose weights were zero-initialized after restoring the non-inpainting checkpoint. During training, we generate synthetic masks and in 25% mask everything. - **Hardware:** 32 x 8 x A100 GPUs - **Optimizer:** AdamW - **Gradient Accumulations**: 2 - **Batch:** 32 x 8 x 2 x 4 = 2048 - **Learning rate:** warmup to 0.0001 for 10,000 steps and then kept constant ## Evaluation Results Evaluations with different classifier-free guidance scales (1.5, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0) and 50 PNDM/PLMS sampling steps show the relative improvements of the checkpoints:  Evaluated using 50 PLMS steps and 10000 random prompts from the COCO2017 validation set, evaluated at 512x512 resolution. Not optimized for FID scores. ## Environmental Impact **Stable Diffusion v1** **Estimated Emissions** Based on that information, we estimate the following CO2 emissions using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). The hardware, runtime, cloud provider, and compute region were utilized to estimate the carbon impact. - **Hardware Type:** A100 PCIe 40GB - **Hours used:** 150000 - **Cloud Provider:** AWS - **Compute Region:** US-east - **Carbon Emitted (Power consumption x Time x Carbon produced based on location of power grid):** 11250 kg CO2 eq. ## Citation ```bibtex @InProceedings{Rombach_2022_CVPR, author = {Rombach, Robin and Blattmann, Andreas and Lorenz, Dominik and Esser, Patrick and Ommer, Bj\"orn}, title = {High-Resolution Image Synthesis With Latent Diffusion Models}, booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)}, month = {June}, year = {2022}, pages = {10684-10695} } ``` *This model card was written by: Robin Rombach and Patrick Esser and is based on the [DALL-E Mini model card](https://huggingface.co/dalle-mini/dalle-mini).*

ff6dc79182f70d5525127385a73ba0ee

jonatasgrosman/exp_w2v2t_pl_wavlm_s250

jonatasgrosman

wavlm

transformers

automatic-speech-recognition

apache-2.0

['pl']

['mozilla-foundation/common_voice_7_0']

['automatic-speech-recognition', 'pl']

# exp_w2v2t_pl_wavlm_s250 Fine-tuned [microsoft/wavlm-large](https://huggingface.co/microsoft/wavlm-large) for speech recognition using the train split of [Common Voice 7.0 (pl)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using this model, make sure that your speech input is sampled at 16kHz. This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.

2ae287b7aad1344a15917389c6575372

Manishkalra/finetuning-movie-sentiment-model-9000-samples

Manishkalra

distilbert

transformers

text-classification

apache-2.0

['imdb']

['generated_from_trainer']

<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # finetuning-movie-sentiment-model-9000-samples This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the imdb dataset. It achieves the following results on the evaluation set: - Loss: 0.4040 - Accuracy: 0.9178 - F1: 0.9155 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results ### Framework versions - Transformers 4.19.2 - Pytorch 1.11.0+cu113 - Datasets 2.2.2 - Tokenizers 0.12.1

7ef4c70555b92d4568f030df0ffc5331

gokuls/mobilebert_add_GLUE_Experiment_logit_kd_pretrain_rte

gokuls

mobilebert

transformers

text-classification

apache-2.0

['en']

['glue']

['generated_from_trainer']

<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # mobilebert_add_GLUE_Experiment_logit_kd_pretrain_rte This model is a fine-tuned version of [gokuls/mobilebert_add_pre-training-complete](https://huggingface.co/gokuls/mobilebert_add_pre-training-complete) on the GLUE RTE dataset. It achieves the following results on the evaluation set: - Loss: nan - Accuracy: 0.5271 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 128 - eval_batch_size: 128 - seed: 10 - distributed_type: multi-GPU - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 50 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.0 | 1.0 | 20 | nan | 0.5271 | | 0.0 | 2.0 | 40 | nan | 0.5271 | | 0.0 | 3.0 | 60 | nan | 0.5271 | | 0.0 | 4.0 | 80 | nan | 0.5271 | | 0.0 | 5.0 | 100 | nan | 0.5271 | | 0.0 | 6.0 | 120 | nan | 0.5271 | ### Framework versions - Transformers 4.26.0 - Pytorch 1.14.0a0+410ce96 - Datasets 2.9.0 - Tokenizers 0.13.2

d0cde83f8c26abb80591ae721ba50e2a

Sahara/finetuning-sentiment-model-3000-samples

Sahara

distilbert

transformers

text-classification

apache-2.0

['imdb']

['generated_from_trainer']

<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # finetuning-sentiment-model-3000-samples This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the imdb dataset. It achieves the following results on the evaluation set: - Loss: 0.3322 - Accuracy: 0.8533 - F1: 0.8562 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Training results ### Framework versions - Transformers 4.20.1 - Pytorch 1.11.0+cu113 - Datasets 2.3.2 - Tokenizers 0.12.1

900f912994c36c1ea4886ea41a8f8ee4

Nadav/camembert-base-squad-fr

Nadav

camembert

transformers

question-answering

mit

['generated_from_trainer']

<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # camembert-base-squad-fr This model is a fine-tuned version of [camembert-base](https://huggingface.co/camembert-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.5182 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 3e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.2 - num_epochs: 2 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 1.7504 | 1.0 | 3581 | 1.6470 | | 1.4776 | 2.0 | 7162 | 1.5182 | ### Framework versions - Transformers 4.24.0 - Pytorch 1.13.0+cu117 - Datasets 2.7.1 - Tokenizers 0.13.2

27076f2df421437249dcc32fb253bc30

jonatasgrosman/exp_w2v2r_fr_vp-100k_gender_male-10_female-0_s626

jonatasgrosman

wav2vec2

transformers

automatic-speech-recognition

apache-2.0

['fr']

['mozilla-foundation/common_voice_7_0']

['automatic-speech-recognition', 'fr']

# exp_w2v2r_fr_vp-100k_gender_male-10_female-0_s626 Fine-tuned [facebook/wav2vec2-large-100k-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-100k-voxpopuli) for speech recognition using the train split of [Common Voice 7.0 (fr)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using this model, make sure that your speech input is sampled at 16kHz. This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.

64dffcb720500b36cba63de43180e27a

karolill/nb-bert-finetuned-on-norec

karolill

bert

transformers

text-classification

mit

[]

# NB-BERT fine-tuned on NoReC ## Description This model is based on the pre-trained [NB-BERT-large model](https://huggingface.co/NbAiLab/nb-bert-large?text=P%C3%A5+biblioteket+kan+du+l%C3%A5ne+en+%5BMASK%5D.). It is a model for sentiment analysis. ## Data for fine-tuning This model was fine-tuned on 1000 exemples from the [NoReC train dataset](https://github.com/ltgoslo/norec) that belonged to the screen category. The training lasted 3 epochs with a learning rate of 5e-5. The code used to create this model (and some additional models) can be found on [Github](https://github.com/Karolill/NB-BERT-fine-tuned-on-english).

db8876a697ac2ee74b1e4f99bfbae95c

lmvasque/readability-es-benchmark-mbert-es-sentences-3class

lmvasque

bert

transformers

text-classification

cc-by-4.0

[]

## Readability benchmark (ES): mbert-es-sentences-3class This project is part of a series of models from the paper "A Benchmark for Neural Readability Assessment of Texts in Spanish". You can find more details about the project in our [GitHub](https://github.com/lmvasque/readability-es-benchmark). ## Models Our models were fine-tuned in multiple settings, including readability assessment in 2-class (simple/complex) and 3-class (basic/intermediate/advanced) for sentences and paragraph datasets. You can find more details in our [paper](https://drive.google.com/file/d/1KdwvqrjX8MWYRDGBKeHmiR1NCzDcVizo/view?usp=share_link). These are the available models you can use (current model page in bold): | Model | Granularity | # classes | |-----------------------------------------------------------------------------------------------------------|----------------|:---------:| | [BERTIN (ES)](https://huggingface.co/lmvasque/readability-es-benchmark-bertin-es-paragraphs-2class) | paragraphs | 2 | | [BERTIN (ES)](https://huggingface.co/lmvasque/readability-es-benchmark-bertin-es-paragraphs-3class) | paragraphs | 3 | | [mBERT (ES)](https://huggingface.co/lmvasque/readability-es-benchmark-mbert-es-paragraphs-2class) | paragraphs | 2 | | [mBERT (ES)](https://huggingface.co/lmvasque/readability-es-benchmark-mbert-es-paragraphs-3class) | paragraphs | 3 | | [mBERT (EN+ES)](https://huggingface.co/lmvasque/readability-es-benchmark-mbert-en-es-paragraphs-3class) | paragraphs | 3 | | [BERTIN (ES)](https://huggingface.co/lmvasque/readability-es-benchmark-bertin-es-sentences-2class) | sentences | 2 | | **[mBERT (ES)](https://huggingface.co/lmvasque/readability-es-benchmark-mbert-es-sentences-3class)** | **sentences** | **3** | | [mBERT (ES)](https://huggingface.co/lmvasque/readability-es-benchmark-mbert-es-sentences-2class) | sentences | 2 | | [mBERT (ES)](https://huggingface.co/lmvasque/readability-es-benchmark-mbert-es-sentences-3class) | sentences | 3 | | [mBERT (EN+ES)](https://huggingface.co/lmvasque/readability-es-benchmark-mbert-en-es-sentences-3class) | sentences | 3 | For the zero-shot setting, we used the original models [BERTIN](bertin-project/bertin-roberta-base-spanish) and [mBERT](https://huggingface.co/bert-base-multilingual-uncased) with no further training. ## Results These are our results for all the readability models in different settings. Please select your model based on the desired performance: | Granularity | Model | F1 Score (2-class) | Precision (2-class) | Recall (2-class) | F1 Score (3-class) | Precision (3-class) | Recall (3-class) | |-------------|---------------|:-------------------:|:---------------------:|:------------------:|:--------------------:|:---------------------:|:------------------:| | Paragraph | Baseline (TF-IDF+LR) | 0.829 | 0.832 | 0.827 | 0.556 | 0.563 | 0.550 | | Paragraph | BERTIN (Zero) | 0.308 | 0.222 | 0.500 | 0.227 | 0.284 | 0.338 | | Paragraph | BERTIN (ES) | 0.924 | 0.923 | 0.925 | 0.772 | 0.776 | 0.768 | | Paragraph | mBERT (Zero) | 0.308 | 0.222 | 0.500 | 0.253 | 0.312 | 0.368 | | Paragraph | mBERT (EN) | - | - | - | 0.505 | 0.560 | 0.552 | | Paragraph | mBERT (ES) | **0.933** | **0.932** | **0.936** | 0.776 | 0.777 | 0.778 | | Paragraph | mBERT (EN+ES) | - | - | - | **0.779** | **0.783** | **0.779** | | Sentence | Baseline (TF-IDF+LR) | 0.811 | 0.814 | 0.808 | 0.525 | 0.531 | 0.521 | | Sentence | BERTIN (Zero) | 0.367 | 0.290 | 0.500 | 0.188 | 0.232 | 0.335 | | Sentence | BERTIN (ES) | **0.900** | **0.900** | **0.900** | **0.699** | **0.701** | **0.698** | | Sentence | mBERT (Zero) | 0.367 | 0.290 | 0.500 | 0.278 | 0.329 | 0.351 | | Sentence | mBERT (EN) | - | - | - | 0.521 | 0.565 | 0.539 | | Sentence | mBERT (ES) | 0.893 | 0.891 | 0.896 | 0.688 | 0.686 | 0.691 | | Sentence | mBERT (EN+ES) | - | - | - | 0.679 | 0.676 | 0.682 | ## Citation If you use our results and scripts in your research, please cite our work: "[A Benchmark for Neural Readability Assessment of Texts in Spanish](https://drive.google.com/file/d/1KdwvqrjX8MWYRDGBKeHmiR1NCzDcVizo/view?usp=share_link)" (to be published) ``` @inproceedings{vasquez-rodriguez-etal-2022-benchmarking, title = "A Benchmark for Neural Readability Assessment of Texts in Spanish", author = "V{\'a}squez-Rodr{\'\i}guez, Laura and Cuenca-Jim{\'\e}nez, Pedro-Manuel and Morales-Esquivel, Sergio Esteban and Alva-Manchego, Fernando", booktitle = "Workshop on Text Simplification, Accessibility, and Readability (TSAR-2022), EMNLP 2022", month = dec, year = "2022", } ```

febbe796a5f094ef6ad3bf1db2d17a6a

AshishBalhara/distilbert-base-uncased-distilled-clinc

AshishBalhara

distilbert

transformers

text-classification

apache-2.0

['clinc_oos']

['generated_from_trainer']

<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # distilbert-base-uncased-distilled-clinc This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the clinc_oos dataset. It achieves the following results on the evaluation set: - Loss: 0.2699 - Accuracy: 0.9458 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 48 - eval_batch_size: 48 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 9 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 4.2203 | 1.0 | 318 | 3.1656 | 0.7532 | | 2.4201 | 2.0 | 636 | 1.5891 | 0.8558 | | 1.1961 | 3.0 | 954 | 0.8037 | 0.9152 | | 0.5996 | 4.0 | 1272 | 0.4888 | 0.9326 | | 0.3306 | 5.0 | 1590 | 0.3589 | 0.9439 | | 0.2079 | 6.0 | 1908 | 0.3070 | 0.9439 | | 0.1458 | 7.0 | 2226 | 0.2809 | 0.9458 | | 0.1155 | 8.0 | 2544 | 0.2740 | 0.9461 | | 0.1021 | 9.0 | 2862 | 0.2699 | 0.9458 | ### Framework versions - Transformers 4.11.3 - Pytorch 1.13.0+cu116 - Datasets 1.16.1 - Tokenizers 0.10.3

9073c9738e9938c44e81a35e81987bb6

kaejo98/bart-base-question-generation

kaejo98

bart

transformers

text2text-generation

apache-2.0

['generated_from_trainer']

<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # bart-base-question-generation This model is a fine-tuned version of [facebook/bart-base](https://huggingface.co/facebook/bart-base) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 4e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.15 - num_epochs: 5 - mixed_precision_training: Native AMP ### Framework versions - Transformers 4.23.1 - Pytorch 1.13.0 - Datasets 2.6.1 - Tokenizers 0.13.1

57de9dffeaa30d6dea822e0166a216b0

Gausstein26/wav2vec2-base-50k

Gausstein26

wav2vec2

transformers

automatic-speech-recognition

apache-2.0

['common_voice']

['generated_from_trainer']

<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # wav2vec2-base-50k This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the common_voice dataset. It achieves the following results on the evaluation set: - Loss: 3.5640 - Wer: 1.0 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 7.5e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 1000 - num_epochs: 5 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:---:| | 10.7005 | 0.48 | 300 | 5.3021 | 1.0 | | 3.9938 | 0.96 | 600 | 3.4997 | 1.0 | | 3.591 | 1.44 | 900 | 3.5641 | 1.0 | | 3.6168 | 1.92 | 1200 | 3.5641 | 1.0 | | 3.6252 | 2.4 | 1500 | 3.5641 | 1.0 | | 3.6137 | 2.88 | 1800 | 3.5641 | 1.0 | | 3.6124 | 3.36 | 2100 | 3.5641 | 1.0 | | 3.6171 | 3.84 | 2400 | 3.5641 | 1.0 | | 3.6436 | 4.32 | 2700 | 3.5641 | 1.0 | | 3.6189 | 4.8 | 3000 | 3.5640 | 1.0 | ### Framework versions - Transformers 4.18.0 - Pytorch 1.11.0 - Datasets 2.1.0 - Tokenizers 0.12.1

39af85ca3a18ca97b7a4395f05670bd2

microsoft/resnet-34

microsoft

resnet

transformers

image-classification

apache-2.0

['imagenet-1k']

['vision', 'image-classification']

# ResNet-34 v1.5 ResNet model pre-trained on ImageNet-1k at resolution 224x224. It was introduced in the paper [Deep Residual Learning for Image Recognition](https://arxiv.org/abs/1512.03385) by He et al. Disclaimer: The team releasing ResNet did not write a model card for this model so this model card has been written by the Hugging Face team. ## Model description ResNet (Residual Network) is a convolutional neural network that democratized the concepts of residual learning and skip connections. This enables to train much deeper models. This is ResNet v1.5, which differs from the original model: in the bottleneck blocks which require downsampling, v1 has stride = 2 in the first 1x1 convolution, whereas v1.5 has stride = 2 in the 3x3 convolution. This difference makes ResNet50 v1.5 slightly more accurate (\~0.5% top1) than v1, but comes with a small performance drawback (~5% imgs/sec) according to [Nvidia](https://catalog.ngc.nvidia.com/orgs/nvidia/resources/resnet_50_v1_5_for_pytorch).  ## Intended uses & limitations You can use the raw model for image classification. See the [model hub](https://huggingface.co/models?search=resnet) to look for fine-tuned versions on a task that interests you. ### How to use Here is how to use this model to classify an image of the COCO 2017 dataset into one of the 1,000 ImageNet classes: ```python from transformers import AutoFeatureExtractor, ResNetForImageClassification import torch from datasets import load_dataset dataset = load_dataset("huggingface/cats-image") image = dataset["test"]["image"][0] feature_extractor = AutoFeatureExtractor.from_pretrained("microsoft/resnet-34") model = ResNetForImageClassification.from_pretrained("microsoft/resnet-34") inputs = feature_extractor(image, return_tensors="pt") with torch.no_grad(): logits = model(**inputs).logits # model predicts one of the 1000 ImageNet classes predicted_label = logits.argmax(-1).item() print(model.config.id2label[predicted_label]) ``` For more code examples, we refer to the [documentation](https://huggingface.co/docs/transformers/main/en/model_doc/resnet). ### BibTeX entry and citation info ```bibtex @inproceedings{he2016deep, title={Deep residual learning for image recognition}, author={He, Kaiming and Zhang, Xiangyu and Ren, Shaoqing and Sun, Jian}, booktitle={Proceedings of the IEEE conference on computer vision and pattern recognition}, pages={770--778}, year={2016} } ```

3a6fe139d3e20966c9c19b9645d70dca

torchxrayvision/densenet121-res224-rsna

torchxrayvision

image-classification

apache-2.0

['nih-pc-chex-mimic_ch-google-openi-rsna']

['vision', 'image-classification']

# densenet121-res224-rsna A DenseNet is a type of convolutional neural network that utilises dense connections between layers, through Dense Blocks, where we connect all layers (with matching feature-map sizes) directly with each other. To preserve the feed-forward nature, each layer obtains additional inputs from all preceding layers and passes on its own feature-maps to all subsequent layers. ### How to use Here is how to use this model to classify an image of xray: Note: Each pretrained model has 18 outputs. The `all` model has every output trained. However, for the other weights some targets are not trained and will predict randomly becuase they do not exist in the training dataset. The only valid outputs are listed in the field `{dataset}.pathologies` on the dataset that corresponds to the weights. Benchmarks of the modes are here: [BENCHMARKS.md](https://github.com/mlmed/torchxrayvision/blob/master/BENCHMARKS.md) ```python import urllib.request import skimage import torch import torch.nn.functional as F import torchvision import torchvision.transforms import torchxrayvision as xrv model_name = "densenet121-res224-rsna" img_url = "https://huggingface.co/spaces/torchxrayvision/torchxrayvision-classifier/resolve/main/16747_3_1.jpg" img_path = "xray.jpg" urllib.request.urlretrieve(img_url, img_path) model = xrv.models.get_model(model_name, from_hf_hub=True) img = skimage.io.imread(img_path) img = xrv.datasets.normalize(img, 255) # Check that images are 2D arrays if len(img.shape) > 2: img = img[:, :, 0] if len(img.shape) < 2: print("error, dimension lower than 2 for image") # Add color channel img = img[None, :, :] transform = torchvision.transforms.Compose([xrv.datasets.XRayCenterCrop()]) img = transform(img) with torch.no_grad(): img = torch.from_numpy(img).unsqueeze(0) preds = model(img).cpu() output = { k: float(v) for k, v in zip(xrv.datasets.default_pathologies, preds[0].detach().numpy()) } print(output) ``` For more code examples, we refer to the [example scripts](https://github.com/kamalkraj/torchxrayvision/blob/master/scripts). ### Citation Primary TorchXRayVision paper: [https://arxiv.org/abs/2111.00595](https://arxiv.org/abs/2111.00595) ``` Joseph Paul Cohen, Joseph D. Viviano, Paul Bertin, Paul Morrison, Parsa Torabian, Matteo Guarrera, Matthew P Lungren, Akshay Chaudhari, Rupert Brooks, Mohammad Hashir, Hadrien Bertrand TorchXRayVision: A library of chest X-ray datasets and models. https://github.com/mlmed/torchxrayvision, 2020 @article{Cohen2020xrv, author = {Cohen, Joseph Paul and Viviano, Joseph D. and Bertin, Paul and Morrison, Paul and Torabian, Parsa and Guarrera, Matteo and Lungren, Matthew P and Chaudhari, Akshay and Brooks, Rupert and Hashir, Mohammad and Bertrand, Hadrien}, journal = {https://github.com/mlmed/torchxrayvision}, title = {{TorchXRayVision: A library of chest X-ray datasets and models}}, url = {https://github.com/mlmed/torchxrayvision}, year = {2020} arxivId = {2111.00595}, } ``` and this paper which initiated development of the library: [https://arxiv.org/abs/2002.02497](https://arxiv.org/abs/2002.02497) ``` Joseph Paul Cohen and Mohammad Hashir and Rupert Brooks and Hadrien Bertrand On the limits of cross-domain generalization in automated X-ray prediction. Medical Imaging with Deep Learning 2020 (Online: https://arxiv.org/abs/2002.02497) @inproceedings{cohen2020limits, title={On the limits of cross-domain generalization in automated X-ray prediction}, author={Cohen, Joseph Paul and Hashir, Mohammad and Brooks, Rupert and Bertrand, Hadrien}, booktitle={Medical Imaging with Deep Learning}, year={2020}, url={https://arxiv.org/abs/2002.02497} } ```

505e2b3064723731502eeedb68525169

luhua/chinese_pretrain_mrc_macbert_large

luhua

bert

transformers

question-answering

apache-2.0

['zh']

[]

## Chinese MRC macbert-large * 使用大量中文MRC数据训练的macbert-large模型，详情可查看：https://github.com/basketballandlearn/MRC_Competition_Dureader * 此库发布的再训练模型，在 阅读理解/分类 等任务上均有大幅提高<br/> （已有多位小伙伴在Dureader-2021等多个比赛中取得**top5**的成绩😁） | 模型/数据集 | Dureader-2021 | tencentmedical | | ------------------------------------------|--------------- | --------------- | | | F1-score | Accuracy | | | dev / A榜 | test-1 | | macbert-large (哈工大预训练语言模型) | 65.49 / 64.27 | 82.5 | | roberta-wwm-ext-large (哈工大预训练语言模型) | 65.49 / 64.27 | 82.5 | | macbert-large (ours) | 70.45 / **68.13**| **83.4** | | roberta-wwm-ext-large (ours) | 68.91 / 66.91 | 83.1 |

92981deba62ebeb06ea120c1d0cea854

sudheer997/distilbert-base-uncased-finetuned-emotion

sudheer997

distilbert

transformers

text-classification

apache-2.0

['emotion']

['generated_from_trainer']

<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # distilbert-base-uncased-finetuned-emotion This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emotion dataset. It achieves the following results on the evaluation set: - Loss: 0.2264 - Accuracy: 0.9275 - F1: 0.9275 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.8546 | 1.0 | 250 | 0.3415 | 0.902 | 0.8975 | | 0.2647 | 2.0 | 500 | 0.2264 | 0.9275 | 0.9275 | ### Framework versions - Transformers 4.24.0 - Pytorch 1.12.1+cu113 - Datasets 2.6.1 - Tokenizers 0.13.1

09e96c41e3c29e83426fd262ed70d129

Yehor/wav2vec2-xls-r-300m-uk-with-lm

Yehor

wav2vec2

transformers

automatic-speech-recognition

apache-2.0

['uk']

['mozilla-foundation/common_voice_7_0']

['automatic-speech-recognition', 'mozilla-foundation/common_voice_7_0', 'generated_from_trainer', 'uk']

# Ukrainian STT model (with Language Model) 🇺🇦 Join Ukrainian Speech Recognition Community - https://t.me/speech_recognition_uk ⭐ See other Ukrainian models - https://github.com/egorsmkv/speech-recognition-uk - Have a look on an updated 300m model: https://huggingface.co/Yehor/wav2vec2-xls-r-300m-uk-with-small-lm - Have a look on a better model with more parameters: https://huggingface.co/Yehor/wav2vec2-xls-r-1b-uk-with-lm This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the MOZILLA-FOUNDATION/COMMON_VOICE_7_0 - UK dataset. It achieves the following results on the evaluation set: - Loss: 0.3015 - Wer: 0.3377 - Cer: 0.0708 The above results present evaluation without the language model. ## Model description On 100 test example the model shows the following results: Without LM: - WER: 0.2647 - CER: 0.0469 With LM: - WER: 0.1568 - CER: 0.0289 ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0003 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 20 - total_train_batch_size: 160 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 100.0 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | Cer | |:-------------:|:-----:|:----:|:---------------:|:------:|:------:| | 3.0255 | 7.93 | 500 | 2.5514 | 0.9921 | 0.9047 | | 1.3809 | 15.86 | 1000 | 0.4065 | 0.5361 | 0.1201 | | 1.2355 | 23.8 | 1500 | 0.3474 | 0.4618 | 0.1033 | | 1.1956 | 31.74 | 2000 | 0.3617 | 0.4580 | 0.1005 | | 1.1416 | 39.67 | 2500 | 0.3182 | 0.4074 | 0.0891 | | 1.0996 | 47.61 | 3000 | 0.3166 | 0.3985 | 0.0875 | | 1.0427 | 55.55 | 3500 | 0.3116 | 0.3835 | 0.0828 | | 0.9961 | 63.49 | 4000 | 0.3137 | 0.3757 | 0.0807 | | 0.9575 | 71.42 | 4500 | 0.2992 | 0.3632 | 0.0771 | | 0.9154 | 79.36 | 5000 | 0.3015 | 0.3502 | 0.0740 | | 0.8994 | 87.3 | 5500 | 0.3004 | 0.3425 | 0.0723 | | 0.871 | 95.24 | 6000 | 0.3016 | 0.3394 | 0.0713 | ### Framework versions - Transformers 4.16.0.dev0 - Pytorch 1.10.1+cu102 - Datasets 1.18.1.dev0 - Tokenizers 0.11.0

ddc10d9e0cf5c2d5baf616b10f77be7e

it5/it5-efficient-small-el32-repubblica-to-ilgiornale

it5

t5

transformers

text2text-generation

apache-2.0

['it']

['gsarti/change_it']

['italian', 'sequence-to-sequence', 'efficient', 'newspaper', 'ilgiornale', 'repubblica', 'style-transfer']

# IT5 Cased Small Efficient EL32 for News Headline Style Transfer (Repubblica to Il Giornale) 🗞️➡️🗞️ 🇮🇹 *Shout-out to [Stefan Schweter](https://github.com/stefan-it) for contributing the pre-trained efficient model!* This repository contains the checkpoint for the [IT5 Cased Small Efficient EL32](https://huggingface.co/it5/it5-efficient-small-el32) model fine-tuned on news headline style transfer in the Repubblica to Il Giornale direction on the Italian CHANGE-IT dataset as part of the experiments of the paper [IT5: Large-scale Text-to-text Pretraining for Italian Language Understanding and Generation](https://arxiv.org/abs/2203.03759) by [Gabriele Sarti](https://gsarti.com) and [Malvina Nissim](https://malvinanissim.github.io). Efficient IT5 models differ from the standard ones by adopting a different vocabulary that enables cased text generation and an [optimized model architecture](https://arxiv.org/abs/2109.10686) to improve performances while reducing parameter count. The Small-EL32 replaces the original encoder from the T5 Small architecture with a 32-layer deep encoder, showing improved performances over the base model. A comprehensive overview of other released materials is provided in the [gsarti/it5](https://github.com/gsarti/it5) repository. Refer to the paper for additional details concerning the reported scores and the evaluation approach. ## Using the model The model is trained to generate a headline in the style of Il Giornale from the full body of an article written in the style of Repubblica. Model checkpoints are available for usage in Tensorflow, Pytorch and JAX. They can be used directly with pipelines as: ```python from transformers import pipelines r2g = pipeline("text2text-generation", model='it5/it5-efficient-small-el32-repubblica-to-ilgiornale') r2g("Arriva dal Partito nazionalista basco (Pnv) la conferma che i cinque deputati che siedono in parlamento voteranno la sfiducia al governo guidato da Mariano Rajoy. Pochi voti, ma significativi quelli della formazione politica di Aitor Esteban, che interverrà nel pomeriggio. Pur con dimensioni molto ridotte, il partito basco si è trovato a fare da ago della bilancia in aula. E il sostegno alla mozione presentata dai Socialisti potrebbe significare per il primo ministro non trovare quei 176 voti che gli servono per continuare a governare. \" Perché dovrei dimettermi io che per il momento ho la fiducia della Camera e quella che mi è stato data alle urne \", ha detto oggi Rajoy nel suo intervento in aula, mentre procedeva la discussione sulla mozione di sfiducia. Il voto dei baschi ora cambia le carte in tavola e fa crescere ulteriormente la pressione sul premier perché rassegni le sue dimissioni. La sfiducia al premier, o un'eventuale scelta di dimettersi, porterebbe alle estreme conseguenze lo scandalo per corruzione che ha investito il Partito popolare. Ma per ora sembra pensare a tutt'altro. \"Non ha intenzione di dimettersi - ha detto il segretario generale del Partito popolare , María Dolores de Cospedal - Non gioverebbe all'interesse generale o agli interessi del Pp\".") >>> [{"generated_text": "il nazionalista rajoy: 'voteremo la sfiducia'"}] ``` or loaded using autoclasses: ```python from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("it5/it5-efficient-small-el32-repubblica-to-ilgiornale") model = AutoModelForSeq2SeqLM.from_pretrained("it5/it5-efficient-small-el32-repubblica-to-ilgiornale") ``` If you use this model in your research, please cite our work as: ```bibtex @article{sarti-nissim-2022-it5, title={{IT5}: Large-scale Text-to-text Pretraining for Italian Language Understanding and Generation}, author={Sarti, Gabriele and Nissim, Malvina}, journal={ArXiv preprint 2203.03759}, url={https://arxiv.org/abs/2203.03759}, year={2022}, month={mar} } ``` ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0003 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10.0 ### Framework versions - Transformers 4.15.0 - Pytorch 1.10.0+cu102 - Datasets 1.17.0 - Tokenizers 0.10.3

70db3b7ae2d781068dff302cf9b67401

LysandreJik/testing

LysandreJik

distilbert

transformers

text-classification

apache-2.0

['en']

['glue']

['generated_from_trainer']

<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # testing This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the GLUE MRPC dataset. It achieves the following results on the evaluation set: - Loss: 0.6644 - Accuracy: 0.6814 - F1: 0.8105 - Combined Score: 0.7459 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 1 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 10 ### Training results ### Framework versions - Transformers 4.11.0.dev0 - Pytorch 1.9.0+cu111 - Datasets 1.11.0 - Tokenizers 0.10.3

e5aa2fe722ebd3f2e3beefe57cd8446a

SfinOe/stable-diffusion-v2-1

SfinOe

diffusers

text-to-image

openrail++

['stable-diffusion', 'text-to-image']

# Stable Diffusion v2-1 Model Card This model card focuses on the model associated with the Stable Diffusion v2-1 model, codebase available [here](https://github.com/Stability-AI/stablediffusion). This `stable-diffusion-2-1` model is fine-tuned from [stable-diffusion-2](https://huggingface.co/stabilityai/stable-diffusion-2) (`768-v-ema.ckpt`) with an additional 55k steps on the same dataset (with `punsafe=0.1`), and then fine-tuned for another 155k extra steps with `punsafe=0.98`. - Use it with the [`stablediffusion`](https://github.com/Stability-AI/stablediffusion) repository: download the `v2-1_768-ema-pruned.ckpt` [here](https://huggingface.co/stabilityai/stable-diffusion-2-1/blob/main/v2-1_768-ema-pruned.ckpt). - Use it with 🧨 [`diffusers`](#examples) ## Model Details - **Developed by:** Robin Rombach, Patrick Esser - **Model type:** Diffusion-based text-to-image generation model - **Language(s):** English - **License:** [CreativeML Open RAIL++-M License](https://huggingface.co/stabilityai/stable-diffusion-2/blob/main/LICENSE-MODEL) - **Model Description:** This is a model that can be used to generate and modify images based on text prompts. It is a [Latent Diffusion Model](https://arxiv.org/abs/2112.10752) that uses a fixed, pretrained text encoder ([OpenCLIP-ViT/H](https://github.com/mlfoundations/open_clip)). - **Resources for more information:** [GitHub Repository](https://github.com/Stability-AI/). - **Cite as:** @InProceedings{Rombach_2022_CVPR, author = {Rombach, Robin and Blattmann, Andreas and Lorenz, Dominik and Esser, Patrick and Ommer, Bj\"orn}, title = {High-Resolution Image Synthesis With Latent Diffusion Models}, booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)}, month = {June}, year = {2022}, pages = {10684-10695} } ## Examples Using the [🤗's Diffusers library](https://github.com/huggingface/diffusers) to run Stable Diffusion 2 in a simple and efficient manner. ```bash pip install diffusers transformers accelerate scipy safetensors ``` Running the pipeline (if you don't swap the scheduler it will run with the default DDIM, in this example we are swapping it to DPMSolverMultistepScheduler): ```python from diffusers import StableDiffusionPipeline, DPMSolverMultistepScheduler model_id = "stabilityai/stable-diffusion-2-1" # Use the DPMSolverMultistepScheduler (DPM-Solver++) scheduler here instead pipe = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float16) pipe.scheduler = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config) pipe = pipe.to("cuda") prompt = "a photo of an astronaut riding a horse on mars" image = pipe(prompt).images[0] image.save("astronaut_rides_horse.png") ``` **Notes**: - Despite not being a dependency, we highly recommend you to install [xformers](https://github.com/facebookresearch/xformers) for memory efficient attention (better performance) - If you have low GPU RAM available, make sure to add a `pipe.enable_attention_slicing()` after sending it to `cuda` for less VRAM usage (to the cost of speed) # Uses ## Direct Use The model is intended for research purposes only. Possible research areas and tasks include - Safe deployment of models which have the potential to generate harmful content. - Probing and understanding the limitations and biases of generative models. - Generation of artworks and use in design and other artistic processes. - Applications in educational or creative tools. - Research on generative models. Excluded uses are described below. ### Misuse, Malicious Use, and Out-of-Scope Use _Note: This section is originally taken from the [DALLE-MINI model card](https://huggingface.co/dalle-mini/dalle-mini), was used for Stable Diffusion v1, but applies in the same way to Stable Diffusion v2_. The model should not be used to intentionally create or disseminate images that create hostile or alienating environments for people. This includes generating images that people would foreseeably find disturbing, distressing, or offensive; or content that propagates historical or current stereotypes. #### Out-of-Scope Use The model was not trained to be factual or true representations of people or events, and therefore using the model to generate such content is out-of-scope for the abilities of this model. #### Misuse and Malicious Use Using the model to generate content that is cruel to individuals is a misuse of this model. This includes, but is not limited to: - Generating demeaning, dehumanizing, or otherwise harmful representations of people or their environments, cultures, religions, etc. - Intentionally promoting or propagating discriminatory content or harmful stereotypes. - Impersonating individuals without their consent. - Sexual content without consent of the people who might see it. - Mis- and disinformation - Representations of egregious violence and gore - Sharing of copyrighted or licensed material in violation of its terms of use. - Sharing content that is an alteration of copyrighted or licensed material in violation of its terms of use. ## Limitations and Bias ### Limitations - The model does not achieve perfect photorealism - The model cannot render legible text - The model does not perform well on more difficult tasks which involve compositionality, such as rendering an image corresponding to “A red cube on top of a blue sphere” - Faces and people in general may not be generated properly. - The model was trained mainly with English captions and will not work as well in other languages. - The autoencoding part of the model is lossy - The model was trained on a subset of the large-scale dataset [LAION-5B](https://laion.ai/blog/laion-5b/), which contains adult, violent and sexual content. To partially mitigate this, we have filtered the dataset using LAION's NFSW detector (see Training section). ### Bias While the capabilities of image generation models are impressive, they can also reinforce or exacerbate social biases. Stable Diffusion was primarily trained on subsets of [LAION-2B(en)](https://laion.ai/blog/laion-5b/), which consists of images that are limited to English descriptions. Texts and images from communities and cultures that use other languages are likely to be insufficiently accounted for. This affects the overall output of the model, as white and western cultures are often set as the default. Further, the ability of the model to generate content with non-English prompts is significantly worse than with English-language prompts. Stable Diffusion v2 mirrors and exacerbates biases to such a degree that viewer discretion must be advised irrespective of the input or its intent. ## Training **Training Data** The model developers used the following dataset for training the model: - LAION-5B and subsets (details below). The training data is further filtered using LAION's NSFW detector, with a "p_unsafe" score of 0.1 (conservative). For more details, please refer to LAION-5B's [NeurIPS 2022](https://openreview.net/forum?id=M3Y74vmsMcY) paper and reviewer discussions on the topic. **Training Procedure** Stable Diffusion v2 is a latent diffusion model which combines an autoencoder with a diffusion model that is trained in the latent space of the autoencoder. During training, - Images are encoded through an encoder, which turns images into latent representations. The autoencoder uses a relative downsampling factor of 8 and maps images of shape H x W x 3 to latents of shape H/f x W/f x 4 - Text prompts are encoded through the OpenCLIP-ViT/H text-encoder. - The output of the text encoder is fed into the UNet backbone of the latent diffusion model via cross-attention. - The loss is a reconstruction objective between the noise that was added to the latent and the prediction made by the UNet. We also use the so-called _v-objective_, see https://arxiv.org/abs/2202.00512. We currently provide the following checkpoints: - `512-base-ema.ckpt`: 550k steps at resolution `256x256` on a subset of [LAION-5B](https://laion.ai/blog/laion-5b/) filtered for explicit pornographic material, using the [LAION-NSFW classifier](https://github.com/LAION-AI/CLIP-based-NSFW-Detector) with `punsafe=0.1` and an [aesthetic score](https://github.com/christophschuhmann/improved-aesthetic-predictor) >= `4.5`. 850k steps at resolution `512x512` on the same dataset with resolution `>= 512x512`. - `768-v-ema.ckpt`: Resumed from `512-base-ema.ckpt` and trained for 150k steps using a [v-objective](https://arxiv.org/abs/2202.00512) on the same dataset. Resumed for another 140k steps on a `768x768` subset of our dataset. - `512-depth-ema.ckpt`: Resumed from `512-base-ema.ckpt` and finetuned for 200k steps. Added an extra input channel to process the (relative) depth prediction produced by [MiDaS](https://github.com/isl-org/MiDaS) (`dpt_hybrid`) which is used as an additional conditioning. The additional input channels of the U-Net which process this extra information were zero-initialized. - `512-inpainting-ema.ckpt`: Resumed from `512-base-ema.ckpt` and trained for another 200k steps. Follows the mask-generation strategy presented in [LAMA](https://github.com/saic-mdal/lama) which, in combination with the latent VAE representations of the masked image, are used as an additional conditioning. The additional input channels of the U-Net which process this extra information were zero-initialized. The same strategy was used to train the [1.5-inpainting checkpoint](https://huggingface.co/runwayml/stable-diffusion-inpainting). - `x4-upscaling-ema.ckpt`: Trained for 1.25M steps on a 10M subset of LAION containing images `>2048x2048`. The model was trained on crops of size `512x512` and is a text-guided [latent upscaling diffusion model](https://arxiv.org/abs/2112.10752). In addition to the textual input, it receives a `noise_level` as an input parameter, which can be used to add noise to the low-resolution input according to a [predefined diffusion schedule](configs/stable-diffusion/x4-upscaling.yaml). - **Hardware:** 32 x 8 x A100 GPUs - **Optimizer:** AdamW - **Gradient Accumulations**: 1 - **Batch:** 32 x 8 x 2 x 4 = 2048 - **Learning rate:** warmup to 0.0001 for 10,000 steps and then kept constant ## Evaluation Results Evaluations with different classifier-free guidance scales (1.5, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0) and 50 steps DDIM sampling steps show the relative improvements of the checkpoints:  Evaluated using 50 DDIM steps and 10000 random prompts from the COCO2017 validation set, evaluated at 512x512 resolution. Not optimized for FID scores. ## Environmental Impact **Stable Diffusion v1** **Estimated Emissions** Based on that information, we estimate the following CO2 emissions using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). The hardware, runtime, cloud provider, and compute region were utilized to estimate the carbon impact. - **Hardware Type:** A100 PCIe 40GB - **Hours used:** 200000 - **Cloud Provider:** AWS - **Compute Region:** US-east - **Carbon Emitted (Power consumption x Time x Carbon produced based on location of power grid):** 15000 kg CO2 eq. ## Citation @InProceedings{Rombach_2022_CVPR, author = {Rombach, Robin and Blattmann, Andreas and Lorenz, Dominik and Esser, Patrick and Ommer, Bj\"orn}, title = {High-Resolution Image Synthesis With Latent Diffusion Models}, booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)}, month = {June}, year = {2022}, pages = {10684-10695} } *This model card was written by: Robin Rombach, Patrick Esser and David Ha and is based on the [Stable Diffusion v1](https://github.com/CompVis/stable-diffusion/blob/main/Stable_Diffusion_v1_Model_Card.md) and [DALL-E Mini model card](https://huggingface.co/dalle-mini/dalle-mini).*

fe9e1cdc45333400917d375824dbe07a

hamidov02/wav2vec2-large-xls-hun-53h-colab

hamidov02

wav2vec2

transformers

automatic-speech-recognition

apache-2.0

['common_voice']

['generated_from_trainer']

<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # wav2vec2-large-xls-hun-53h-colab This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on the common_voice dataset. It achieves the following results on the evaluation set: - Loss: 0.6027 - Wer: 0.4618 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0003 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 23 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 13.4225 | 0.67 | 100 | 3.7750 | 1.0 | | 3.4121 | 1.34 | 200 | 3.3166 | 1.0 | | 3.2263 | 2.01 | 300 | 3.1403 | 1.0 | | 3.0038 | 2.68 | 400 | 2.2474 | 0.9990 | | 1.2243 | 3.35 | 500 | 0.8174 | 0.7666 | | 0.6368 | 4.03 | 600 | 0.6306 | 0.6633 | | 0.4426 | 4.7 | 700 | 0.6151 | 0.6648 | | 0.3821 | 5.37 | 800 | 0.5765 | 0.6138 | | 0.3337 | 6.04 | 900 | 0.5522 | 0.5785 | | 0.2832 | 6.71 | 1000 | 0.5822 | 0.5691 | | 0.2485 | 7.38 | 1100 | 0.5626 | 0.5449 | | 0.2335 | 8.05 | 1200 | 0.5866 | 0.5662 | | 0.2031 | 8.72 | 1300 | 0.5574 | 0.5420 | | 0.1925 | 9.39 | 1400 | 0.5572 | 0.5297 | | 0.1793 | 10.07 | 1500 | 0.5878 | 0.5185 | | 0.1652 | 10.74 | 1600 | 0.6173 | 0.5243 | | 0.1663 | 11.41 | 1700 | 0.5807 | 0.5133 | | 0.1544 | 12.08 | 1800 | 0.5979 | 0.5154 | | 0.148 | 12.75 | 1900 | 0.5545 | 0.4986 | | 0.138 | 13.42 | 2000 | 0.5798 | 0.4947 | | 0.1353 | 14.09 | 2100 | 0.5670 | 0.5028 | | 0.1283 | 14.76 | 2200 | 0.5862 | 0.4957 | | 0.1271 | 15.43 | 2300 | 0.6009 | 0.4961 | | 0.1108 | 16.11 | 2400 | 0.5873 | 0.4975 | | 0.1182 | 16.78 | 2500 | 0.6013 | 0.4893 | | 0.103 | 17.45 | 2600 | 0.6165 | 0.4898 | | 0.1084 | 18.12 | 2700 | 0.6186 | 0.4838 | | 0.1014 | 18.79 | 2800 | 0.6122 | 0.4767 | | 0.1009 | 19.46 | 2900 | 0.5981 | 0.4793 | | 0.1004 | 20.13 | 3000 | 0.6034 | 0.4770 | | 0.0922 | 20.8 | 3100 | 0.6127 | 0.4663 | | 0.09 | 21.47 | 3200 | 0.5967 | 0.4672 | | 0.0893 | 22.15 | 3300 | 0.6051 | 0.4611 | | 0.0817 | 22.82 | 3400 | 0.6027 | 0.4618 | ### Framework versions - Transformers 4.11.3 - Pytorch 1.10.0+cu113 - Datasets 1.18.3 - Tokenizers 0.10.3

af63d676d2f0b56d1f5c8d55d2bef6d9

Charalampos/whisper-new

Charalampos

whisper

transformers

automatic-speech-recognition

apache-2.0

['el']

['mozilla-foundation/common_voice_11_0']

['whisper-event', 'generated_from_trainer']

<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # Whisper Tiny Greek This model is a fine-tuned version of [openai/whisper-base](https://huggingface.co/openai/whisper-base) on the mozilla-foundation/common_voice_11_0 el dataset. It achieves the following results on the evaluation set: - Loss: 1.3444 - Wer: 231.8841 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - training_steps: 2 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:--------:| | No log | 1.5 | 2 | 1.3444 | 231.8841 | ### Framework versions - Transformers 4.26.0.dev0 - Pytorch 1.13.1+cu117 - Datasets 2.8.1.dev0 - Tokenizers 0.13.2

81ff0cb7249db2f1c03fa77c00aba031

PaddyP/distilbert-base-uncased-finetuned-emotion

PaddyP

distilbert

transformers

text-classification

apache-2.0

['generated_from_trainer']

<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # distilbert-base-uncased-finetuned-emotion This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.2302 - Accuracy: 0.922 - F1: 0.9218 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | No log | 1.0 | 250 | 0.3344 | 0.903 | 0.9004 | | No log | 2.0 | 500 | 0.2302 | 0.922 | 0.9218 | ### Framework versions - Transformers 4.15.0 - Pytorch 1.9.1 - Datasets 2.0.0 - Tokenizers 0.10.3

273a7d6292504c7de9d52f0f6e59d80c

sd-concepts-library/manga-style

sd-concepts-library

mit

[]

### Manga style on Stable Diffusion This is the `<manga>` concept taught to Stable Diffusion via Textual Inversion. You can load this concept into the [Stable Conceptualizer](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/stable_conceptualizer_inference.ipynb) notebook. You can also train your own concepts and load them into the concept libraries using [this notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_textual_inversion_training.ipynb). Here is the new concept you will be able to use as a `style`:        

b9cfe7378f4a2aebab4af1e914f23ef6

google/multiberts-seed_2-step_1000k

google

bert

transformers

apache-2.0

['en']

['multiberts', 'multiberts-seed_2', 'multiberts-seed_2-step_1000k']

# MultiBERTs, Intermediate Checkpoint - Seed 2, Step 1000k MultiBERTs is a collection of checkpoints and a statistical library to support robust research on BERT. We provide 25 BERT-base models trained with similar hyper-parameters as [the original BERT model](https://github.com/google-research/bert) but with different random seeds, which causes variations in the initial weights and order of training instances. The aim is to distinguish findings that apply to a specific artifact (i.e., a particular instance of the model) from those that apply to the more general procedure. We also provide 140 intermediate checkpoints captured during the course of pre-training (we saved 28 checkpoints for the first 5 runs). The models were originally released through [http://goo.gle/multiberts](http://goo.gle/multiberts). We describe them in our paper [The MultiBERTs: BERT Reproductions for Robustness Analysis](https://arxiv.org/abs/2106.16163). This is model #2, captured at step 1000k (max: 2000k, i.e., 2M steps). ## Model Description This model was captured during a reproduction of [BERT-base uncased](https://github.com/google-research/bert), for English: it is a Transformers model pretrained on a large corpus of English data, using the Masked Language Modelling (MLM) and the Next Sentence Prediction (NSP) objectives. The intended uses, limitations, training data and training procedure for the fully trained model are similar to [BERT-base uncased](https://github.com/google-research/bert). Two major differences with the original model: * We pre-trained the MultiBERTs models for 2 million steps using sequence length 512 (instead of 1 million steps using sequence length 128 then 512). * We used an alternative version of Wikipedia and Books Corpus, initially collected for [Turc et al., 2019](https://arxiv.org/abs/1908.08962). This is a best-effort reproduction, and so it is probable that differences with the original model have gone unnoticed. The performance of MultiBERTs on GLUE after full training is oftentimes comparable to that of original BERT, but we found significant differences on the dev set of SQuAD (MultiBERTs outperforms original BERT). See our [technical report](https://arxiv.org/abs/2106.16163) for more details. ### How to use Using code from [BERT-base uncased](https://huggingface.co/bert-base-uncased), here is an example based on Tensorflow: ``` from transformers import BertTokenizer, TFBertModel tokenizer = BertTokenizer.from_pretrained('google/multiberts-seed_2-step_1000k') model = TFBertModel.from_pretrained("google/multiberts-seed_2-step_1000k") text = "Replace me by any text you'd like." encoded_input = tokenizer(text, return_tensors='tf') output = model(encoded_input) ``` PyTorch version: ``` from transformers import BertTokenizer, BertModel tokenizer = BertTokenizer.from_pretrained('google/multiberts-seed_2-step_1000k') model = BertModel.from_pretrained("google/multiberts-seed_2-step_1000k") text = "Replace me by any text you'd like." encoded_input = tokenizer(text, return_tensors='pt') output = model(**encoded_input) ``` ## Citation info ```bibtex @article{sellam2021multiberts, title={The MultiBERTs: BERT Reproductions for Robustness Analysis}, author={Thibault Sellam and Steve Yadlowsky and Jason Wei and Naomi Saphra and Alexander D'Amour and Tal Linzen and Jasmijn Bastings and Iulia Turc and Jacob Eisenstein and Dipanjan Das and Ian Tenney and Ellie Pavlick}, journal={arXiv preprint arXiv:2106.16163}, year={2021} } ```

eda5f85611a7e6697f8395dc9df69d3f

milyiyo/multi-minilm-finetuned-amazon-review

milyiyo

bert

transformers

text-classification

mit

['amazon_reviews_multi']

['generated_from_trainer']

<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # multi-minilm-finetuned-amazon-review This model is a fine-tuned version of [microsoft/Multilingual-MiniLM-L12-H384](https://huggingface.co/microsoft/Multilingual-MiniLM-L12-H384) on the amazon_reviews_multi dataset. It achieves the following results on the evaluation set: - Loss: 1.2436 - Accuracy: 0.5422 - F1: 0.5435 - Precision: 0.5452 - Recall: 0.5422 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall | |:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|:---------:|:------:| | 1.0049 | 1.0 | 2500 | 1.0616 | 0.5352 | 0.5268 | 0.5347 | 0.5352 | | 0.9172 | 2.0 | 5000 | 1.0763 | 0.5432 | 0.5412 | 0.5444 | 0.5432 | | 0.8285 | 3.0 | 7500 | 1.1077 | 0.5408 | 0.5428 | 0.5494 | 0.5408 | | 0.7361 | 4.0 | 10000 | 1.1743 | 0.5342 | 0.5399 | 0.5531 | 0.5342 | | 0.6538 | 5.0 | 12500 | 1.2436 | 0.5422 | 0.5435 | 0.5452 | 0.5422 | ### Framework versions - Transformers 4.15.0 - Pytorch 1.10.0+cu111 - Datasets 1.17.0 - Tokenizers 0.10.3

bbea158b23d3bd1f242a66420d2e24b5

thomas0104/whisper_large_v2_zh_tw

thomas0104

whisper

transformers

automatic-speech-recognition

apache-2.0

['zh']

['mozilla-foundation/common_voice_11_0']

['whisper-event', 'generated_from_trainer']

<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # Whisper large-v2 zh-tw This model is a fine-tuned version of [openai/whisper-large-v2](https://huggingface.co/openai/whisper-large-v2) on the mozilla-foundation/common_voice_11_0 zh-TW dataset. It achieves the following results on the evaluation set: - Loss: 1.1603 - Wer: 40.3946 - Cer: 41.1041 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data Training: "mozilla-foundation/common_voice_11_0","zh-TW","train" evaluation: "mozilla-foundation/common_voice_11_0","zh-TW","test" ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - training_steps: 5000 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | Cer | |:-------------:|:-----:|:----:|:---------------:|:--------:|:--------:| | 2.87 | 0.2 | 1000 | 3.0804 | 192.9556 | 192.6466 | | 2.6143 | 0.4 | 2000 | 2.4951 | 96.5525 | 96.6443 | | 1.863 | 0.6 | 3000 | 2.0882 | 69.3188 | 69.6395 | | 1.1665 | 1.14 | 4000 | 1.4647 | 50.5666 | 51.5850 | | 0.6674 | 1.34 | 5000 | 1.1603 | 40.3946 | 41.1041 | ### Framework versions - Transformers 4.25.1 - Pytorch 1.13.1+cu117 - Datasets 2.8.0 - Tokenizers 0.13.2

98f37609420e3ef0611174e3c40e0038

Helsinki-NLP/opus-mt-tc-big-itc-eu

Helsinki-NLP

marian

transformers

translation

cc-by-4.0

['es', 'eu']

['translation', 'opus-mt-tc']

# opus-mt-tc-big-itc-eu ## Table of Contents - [Model Details](#model-details) - [Uses](#uses) - [Risks, Limitations and Biases](#risks-limitations-and-biases) - [How to Get Started With the Model](#how-to-get-started-with-the-model) - [Training](#training) - [Evaluation](#evaluation) - [Citation Information](#citation-information) - [Acknowledgements](#acknowledgements) ## Model Details Neural machine translation model for translating from Italic languages (itc) to Basque (eu). This model is part of the [OPUS-MT project](https://github.com/Helsinki-NLP/Opus-MT), an effort to make neural machine translation models widely available and accessible for many languages in the world. All models are originally trained using the amazing framework of [Marian NMT](https://marian-nmt.github.io/), an efficient NMT implementation written in pure C++. The models have been converted to pyTorch using the transformers library by huggingface. Training data is taken from [OPUS](https://opus.nlpl.eu/) and training pipelines use the procedures of [OPUS-MT-train](https://github.com/Helsinki-NLP/Opus-MT-train). **Model Description:** - **Developed by:** Language Technology Research Group at the University of Helsinki - **Model Type:** Translation (transformer-big) - **Release**: 2022-07-23 - **License:** CC-BY-4.0 - **Language(s):** - Source Language(s): fra ita spa - Target Language(s): eus - Language Pair(s): spa-eus - Valid Target Language Labels: - **Original Model**: [opusTCv20210807_transformer-big_2022-07-23.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/itc-eus/opusTCv20210807_transformer-big_2022-07-23.zip) - **Resources for more information:** - [OPUS-MT-train GitHub Repo](https://github.com/Helsinki-NLP/OPUS-MT-train) - More information about released models for this language pair: [OPUS-MT itc-eus README](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/itc-eus/README.md) - [More information about MarianNMT models in the transformers library](https://huggingface.co/docs/transformers/model_doc/marian) - [Tatoeba Translation Challenge](https://github.com/Helsinki-NLP/Tatoeba-Challenge/ ## Uses This model can be used for translation and text-to-text generation. ## Risks, Limitations and Biases **CONTENT WARNING: Readers should be aware that the model is trained on various public data sets that may contain content that is disturbing, offensive, and can propagate historical and current stereotypes.** Significant research has explored bias and fairness issues with language models (see, e.g., [Sheng et al. (2021)](https://aclanthology.org/2021.acl-long.330.pdf) and [Bender et al. (2021)](https://dl.acm.org/doi/pdf/10.1145/3442188.3445922)). ## How to Get Started With the Model A short example code: ```python from transformers import MarianMTModel, MarianTokenizer src_text = [ "Il est riche.", "¿Correcto?" ] model_name = "pytorch-models/opus-mt-tc-big-itc-eu" tokenizer = MarianTokenizer.from_pretrained(model_name) model = MarianMTModel.from_pretrained(model_name) translated = model.generate(**tokenizer(src_text, return_tensors="pt", padding=True)) for t in translated: print( tokenizer.decode(t, skip_special_tokens=True) ) # expected output: # Aberatsa da. # Zuzena? ``` You can also use OPUS-MT models with the transformers pipelines, for example: ```python from transformers import pipeline pipe = pipeline("translation", model="Helsinki-NLP/opus-mt-tc-big-itc-eu") print(pipe("Il est riche.")) # expected output: Aberatsa da. ``` ## Training - **Data**: opusTCv20210807 ([source](https://github.com/Helsinki-NLP/Tatoeba-Challenge)) - **Pre-processing**: SentencePiece (spm32k,spm32k) - **Model Type:** transformer-big - **Original MarianNMT Model**: [opusTCv20210807_transformer-big_2022-07-23.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/itc-eus/opusTCv20210807_transformer-big_2022-07-23.zip) - **Training Scripts**: [GitHub Repo](https://github.com/Helsinki-NLP/OPUS-MT-train) ## Evaluation * test set translations: [opusTCv20210807_transformer-big_2022-07-23.test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/itc-eus/opusTCv20210807_transformer-big_2022-07-23.test.txt) * test set scores: [opusTCv20210807_transformer-big_2022-07-23.eval.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/itc-eus/opusTCv20210807_transformer-big_2022-07-23.eval.txt) * benchmark results: [benchmark_results.txt](benchmark_results.txt) * benchmark output: [benchmark_translations.zip](benchmark_translations.zip) | langpair | testset | chr-F | BLEU | #sent | #words | |----------|---------|-------|-------|-------|--------| | spa-eus | tatoeba-test-v2021-08-07 | 0.60699 | 32.4 | 1850 | 10945 | ## Citation Information * Publications: [OPUS-MT – Building open translation services for the World](https://aclanthology.org/2020.eamt-1.61/) and [The Tatoeba Translation Challenge – Realistic Data Sets for Low Resource and Multilingual MT](https://aclanthology.org/2020.wmt-1.139/) (Please, cite if you use this model.) ``` @inproceedings{tiedemann-thottingal-2020-opus, title = "{OPUS}-{MT} {--} Building open translation services for the World", author = {Tiedemann, J{\"o}rg and Thottingal, Santhosh}, booktitle = "Proceedings of the 22nd Annual Conference of the European Association for Machine Translation", month = nov, year = "2020", address = "Lisboa, Portugal", publisher = "European Association for Machine Translation", url = "https://aclanthology.org/2020.eamt-1.61", pages = "479--480", } @inproceedings{tiedemann-2020-tatoeba, title = "The Tatoeba Translation Challenge {--} Realistic Data Sets for Low Resource and Multilingual {MT}", author = {Tiedemann, J{\"o}rg}, booktitle = "Proceedings of the Fifth Conference on Machine Translation", month = nov, year = "2020", address = "Online", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2020.wmt-1.139", pages = "1174--1182", } ``` ## Acknowledgements The work is supported by the [European Language Grid](https://www.european-language-grid.eu/) as [pilot project 2866](https://live.european-language-grid.eu/catalogue/#/resource/projects/2866), by the [FoTran project](https://www.helsinki.fi/en/researchgroups/natural-language-understanding-with-cross-lingual-grounding), funded by the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement No 771113), and the [MeMAD project](https://memad.eu/), funded by the European Union’s Horizon 2020 Research and Innovation Programme under grant agreement No 780069. We are also grateful for the generous computational resources and IT infrastructure provided by [CSC -- IT Center for Science](https://www.csc.fi/), Finland. ## Model conversion info * transformers version: 4.16.2 * OPUS-MT git hash: 8b9f0b0 * port time: Sat Aug 13 00:08:07 EEST 2022 * port machine: LM0-400-22516.local

1ed70a370ef245ed1976a60c852add9a

scasutt/wav2vec2-large-xlsr-53_toy_train_data_fast_10pct

scasutt

wav2vec2

transformers

automatic-speech-recognition

apache-2.0

['generated_from_trainer']

<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # wav2vec2-large-xlsr-53_toy_train_data_fast_10pct This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.6983 - Wer: 0.5026 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 1000 - num_epochs: 20 ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 3.3619 | 1.05 | 250 | 3.4334 | 1.0 | | 3.0818 | 2.1 | 500 | 3.4914 | 1.0 | | 2.3245 | 3.15 | 750 | 1.6483 | 0.9486 | | 1.0233 | 4.2 | 1000 | 0.8817 | 0.7400 | | 0.7522 | 5.25 | 1250 | 0.7374 | 0.6529 | | 0.5343 | 6.3 | 1500 | 0.6972 | 0.6068 | | 0.4452 | 7.35 | 1750 | 0.6757 | 0.5740 | | 0.4275 | 8.4 | 2000 | 0.6789 | 0.5551 | | 0.3688 | 9.45 | 2250 | 0.6468 | 0.5394 | | 0.3363 | 10.5 | 2500 | 0.6798 | 0.5358 | | 0.3036 | 11.55 | 2750 | 0.6439 | 0.5265 | | 0.3173 | 12.6 | 3000 | 0.6898 | 0.5196 | | 0.2985 | 13.65 | 3250 | 0.6791 | 0.5169 | | 0.288 | 14.7 | 3500 | 0.6442 | 0.5090 | | 0.2673 | 15.75 | 3750 | 0.6984 | 0.5119 | | 0.2575 | 16.81 | 4000 | 0.7146 | 0.5084 | | 0.239 | 17.86 | 4250 | 0.6847 | 0.5040 | | 0.2266 | 18.91 | 4500 | 0.6900 | 0.5028 | | 0.22 | 19.96 | 4750 | 0.6983 | 0.5026 | ### Framework versions - Transformers 4.17.0 - Pytorch 1.11.0+cu102 - Datasets 2.0.0 - Tokenizers 0.11.6

ad6cf658f624b957a20d1f76b1637d68

kyonimouto/hoyu-ai

kyonimouto

other

['ja']

['hoyu256']

['PyTorch']

Diffusion GANというコードを使ってつくりました https://github.com/Zhendong-Wang/Diffusion-GAN つかいかた 試してないので動かなかったらごめんなさい - 環境をととのえる - 最近のNVIDIA製GPUがついたパソコンにLinuxを入れることをおすすめします - PytorchをCUDAありでインストールしてください - https://pytorch.org/get-started/locally/ - conda install pytorch torchvision torchaudio pytorch-cuda=11.6 -c pytorch -c nvidia - Gitもインストールしてください - sudo apt install git - Diffusion-GANをgithubからローカルにcloneしてください - git clone https://github.com/Zhendong-Wang/Diffusion-GAN - diffusion-projected-ganというフォルダを開いてください - ここの"Files and versions"から"best_model.pkl"をクリックしてダウンロードし、diffusion-projected-ganの中に保存してください - 以下のコマンドで画像を生成します - python gen_images.py --outdir=out --seeds=0-10 --network=./best_model.pkl - パッケージがインストールされていないというエラーが出たら適宜インストールしてください - outフォルダに生成された画像が入っています 商用利用などはご遠慮ください

5820d86ecd93456d7e8e645e11ad9c1b

fathyshalab/all-roberta-large-v1-meta-1-16-5

fathyshalab

roberta

transformers

text-classification

apache-2.0

['generated_from_trainer']

<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # all-roberta-large-v1-meta-1-16-5 This model is a fine-tuned version of [sentence-transformers/all-roberta-large-v1](https://huggingface.co/sentence-transformers/all-roberta-large-v1) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.4797 - Accuracy: 0.28 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 48 - eval_batch_size: 48 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 2.7721 | 1.0 | 1 | 2.6529 | 0.1889 | | 2.2569 | 2.0 | 2 | 2.5866 | 0.2333 | | 1.9837 | 3.0 | 3 | 2.5340 | 0.2644 | | 1.6425 | 4.0 | 4 | 2.4980 | 0.2756 | | 1.4612 | 5.0 | 5 | 2.4797 | 0.28 | ### Framework versions - Transformers 4.20.0 - Pytorch 1.11.0+cu102 - Datasets 2.3.2 - Tokenizers 0.12.1

00bb1723ac003e09ff759f2718cdffd8

sd-dreambooth-library/quino

sd-dreambooth-library

diffusers

text-to-image

creativeml-openrail-m

['text-to-image']

### quino Dreambooth model trained by machinelearnear with [Hugging Face Dreambooth Training Space](https://huggingface.co/spaces/multimodalart/dreambooth-training) with the v1-5 base model You run your new concept via `diffusers` [Colab Notebook for Inference](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_dreambooth_inference.ipynb). Don't forget to use the concept prompts! Sample pictures of: artequino (use that on your prompt) 

f7376fefdb0e0fbc960890f4996a147f

T-Systems-onsite/cross-en-de-roberta-sentence-transformer

T-Systems-onsite

xlm-roberta

transformers

feature-extraction

mit

['de', 'en', 'multilingual']

['stsb_multi_mt']

['sentence_embedding', 'search', 'pytorch', 'xlm-roberta', 'roberta', 'xlm-r-distilroberta-base-paraphrase-v1', 'paraphrase']

# Cross English & German RoBERTa for Sentence Embeddings This model is intended to [compute sentence (text) embeddings](https://www.sbert.net/examples/applications/computing-embeddings/README.html) for English and German text. These embeddings can then be compared with [cosine-similarity](https://en.wikipedia.org/wiki/Cosine_similarity) to find sentences with a similar semantic meaning. For example this can be useful for [semantic textual similarity](https://www.sbert.net/docs/usage/semantic_textual_similarity.html), [semantic search](https://www.sbert.net/docs/usage/semantic_search.html), or [paraphrase mining](https://www.sbert.net/docs/usage/paraphrase_mining.html). To do this you have to use the [Sentence Transformers Python framework](https://github.com/UKPLab/sentence-transformers). The speciality of this model is that it also works cross-lingually. Regardless of the language, the sentences are translated into very similar vectors according to their semantics. This means that you can, for example, enter a search in German and find results according to the semantics in German and also in English. Using a xlm model and _multilingual finetuning with language-crossing_ we reach performance that even exceeds the best current dedicated English large model (see Evaluation section below). > Sentence-BERT (SBERT) is a modification of the pretrained BERT network that use siamese and triplet network structures to derive semantically meaningful sentence embeddings that can be compared using cosine-similarity. This reduces the effort for finding the most similar pair from 65hours with BERT / RoBERTa to about 5 seconds with SBERT, while maintaining the accuracy from BERT. Source: [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://arxiv.org/abs/1908.10084) This model is fine-tuned from [Philip May](https://may.la/) and open-sourced by [T-Systems-onsite](https://www.t-systems-onsite.de/). Special thanks to [Nils Reimers](https://www.nils-reimers.de/) for your awesome open-source work, the Sentence Transformers, the models and your help on GitHub. ## How to use To use this model install the `sentence-transformers` package (see here: <https://github.com/UKPLab/sentence-transformers>). ```python from sentence_transformers import SentenceTransformer model = SentenceTransformer('T-Systems-onsite/cross-en-de-roberta-sentence-transformer') ``` For details of usage and examples see here: - [Computing Sentence Embeddings](https://www.sbert.net/docs/usage/computing_sentence_embeddings.html) - [Semantic Textual Similarity](https://www.sbert.net/docs/usage/semantic_textual_similarity.html) - [Paraphrase Mining](https://www.sbert.net/docs/usage/paraphrase_mining.html) - [Semantic Search](https://www.sbert.net/docs/usage/semantic_search.html) - [Cross-Encoders](https://www.sbert.net/docs/usage/cross-encoder.html) - [Examples on GitHub](https://github.com/UKPLab/sentence-transformers/tree/master/examples) ## Training The base model is [xlm-roberta-base](https://huggingface.co/xlm-roberta-base). This model has been further trained by [Nils Reimers](https://www.nils-reimers.de/) on a large scale paraphrase dataset for 50+ languages. [Nils Reimers](https://www.nils-reimers.de/) about this [on GitHub](https://github.com/UKPLab/sentence-transformers/issues/509#issuecomment-712243280): >A paper is upcoming for the paraphrase models. > >These models were trained on various datasets with Millions of examples for paraphrases, mainly derived from Wikipedia edit logs, paraphrases mined from Wikipedia and SimpleWiki, paraphrases from news reports, AllNLI-entailment pairs with in-batch-negative loss etc. > >In internal tests, they perform much better than the NLI+STSb models as they have see more and broader type of training data. NLI+STSb has the issue that they are rather narrow in their domain and do not contain any domain specific words / sentences (like from chemistry, computer science, math etc.). The paraphrase models has seen plenty of sentences from various domains. > >More details with the setup, all the datasets, and a wider evaluation will follow soon. The resulting model called `xlm-r-distilroberta-base-paraphrase-v1` has been released here: <https://github.com/UKPLab/sentence-transformers/releases/tag/v0.3.8> Building on this cross language model we fine-tuned it for English and German language on the [STSbenchmark](http://ixa2.si.ehu.es/stswiki/index.php/STSbenchmark) dataset. For German language we used the dataset of our [German STSbenchmark dataset](https://github.com/t-systems-on-site-services-gmbh/german-STSbenchmark) which has been translated with [deepl.com](https://www.deepl.com/translator). Additionally to the German and English training samples we generated samples of English and German crossed. We call this _multilingual finetuning with language-crossing_. It doubled the traing-datasize and tests show that it further improves performance. We did an automatic hyperparameter search for 33 trials with [Optuna](https://github.com/optuna/optuna). Using 10-fold crossvalidation on the deepl.com test and dev dataset we found the following best hyperparameters: - batch_size = 8 - num_epochs = 2 - lr = 1.026343323298136e-05, - eps = 4.462251033010287e-06 - weight_decay = 0.04794438776350409 - warmup_steps_proportion = 0.1609010732760181 The final model was trained with these hyperparameters on the combination of the train and dev datasets from English, German and the crossings of them. The testset was left for testing. # Evaluation The evaluation has been done on English, German and both languages crossed with the STSbenchmark test data. The evaluation-code is available on [Colab](https://colab.research.google.com/drive/1gtGnKq_dYU_sDYqMohTYVMVpxMJjyH0M?usp=sharing). As the metric for evaluation we use the Spearman’s rank correlation between the cosine-similarity of the sentence embeddings and STSbenchmark labels. | Model Name | Spearman<br/>German | Spearman<br/>English | Spearman<br/>EN-DE & DE-EN<br/>(cross) | |---------------------------------------------------------------|-------------------|--------------------|------------------| | xlm-r-distilroberta-base-paraphrase-v1 | 0.8079 | 0.8350 | 0.7983 | | [xlm-r-100langs-bert-base-nli-stsb-mean-tokens](https://huggingface.co/sentence-transformers/xlm-r-100langs-bert-base-nli-stsb-mean-tokens) | 0.7877 | 0.8465 | 0.7908 | | xlm-r-bert-base-nli-stsb-mean-tokens | 0.7877 | 0.8465 | 0.7908 | | [roberta-large-nli-stsb-mean-tokens](https://huggingface.co/sentence-transformers/roberta-large-nli-stsb-mean-tokens) | 0.6371 | 0.8639 | 0.4109 | | [T-Systems-onsite/<br/>german-roberta-sentence-transformer-v2](https://huggingface.co/T-Systems-onsite/german-roberta-sentence-transformer-v2) | 0.8529 | 0.8634 | 0.8415 | | **T-Systems-onsite/<br/>cross-en-de-roberta-sentence-transformer** | **0.8550** | **0.8660** | **0.8525** | ## License Copyright (c) 2020 Philip May, T-Systems on site services GmbH Licensed under the MIT License (the "License"); you may not use this work except in compliance with the License. You may obtain a copy of the License by reviewing the file [LICENSE](https://huggingface.co/T-Systems-onsite/cross-en-de-roberta-sentence-transformer/blob/main/LICENSE) in the repository.

39507c8ae82169a34201c6131c064719

gokuls/distilbert_add_GLUE_Experiment_mrpc

gokuls

distilbert

transformers

text-classification

apache-2.0

['en']

['glue']

['generated_from_trainer']

<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # distilbert_add_GLUE_Experiment_mrpc This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the GLUE MRPC dataset. It achieves the following results on the evaluation set: - Loss: 0.6028 - Accuracy: 0.6961 - F1: 0.8171 - Combined Score: 0.7566 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 256 - eval_batch_size: 256 - seed: 10 - distributed_type: multi-GPU - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 50 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Combined Score | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:--------------:| | 0.6617 | 1.0 | 15 | 0.6507 | 0.6838 | 0.8122 | 0.7480 | | 0.6412 | 2.0 | 30 | 0.6290 | 0.6838 | 0.8122 | 0.7480 | | 0.6315 | 3.0 | 45 | 0.6252 | 0.6838 | 0.8122 | 0.7480 | | 0.6319 | 4.0 | 60 | 0.6236 | 0.6838 | 0.8122 | 0.7480 | | 0.6321 | 5.0 | 75 | 0.6225 | 0.6838 | 0.8122 | 0.7480 | | 0.616 | 6.0 | 90 | 0.6028 | 0.6961 | 0.8171 | 0.7566 | | 0.5469 | 7.0 | 105 | 0.6485 | 0.6446 | 0.7349 | 0.6898 | | 0.4436 | 8.0 | 120 | 0.7536 | 0.6838 | 0.7909 | 0.7374 | | 0.3794 | 9.0 | 135 | 0.7805 | 0.6961 | 0.7898 | 0.7430 | | 0.3158 | 10.0 | 150 | 0.8811 | 0.6838 | 0.7825 | 0.7331 | | 0.281 | 11.0 | 165 | 0.9246 | 0.6863 | 0.7881 | 0.7372 | ### Framework versions - Transformers 4.26.0 - Pytorch 1.14.0a0+410ce96 - Datasets 2.8.0 - Tokenizers 0.13.2

9a7f7f884ae4664ffba19c3a471bd26f

jonatasgrosman/exp_w2v2t_fr_xls-r_s250

jonatasgrosman

wav2vec2

transformers

automatic-speech-recognition

apache-2.0

['fr']

['mozilla-foundation/common_voice_7_0']

['automatic-speech-recognition', 'fr']

# exp_w2v2t_fr_xls-r_s250 Fine-tuned [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) for speech recognition using the train split of [Common Voice 7.0 (fr)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using this model, make sure that your speech input is sampled at 16kHz. This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.

fd90a5b962e7ad7a21c5907fb71b16bd

tbosse/bert-base-german-cased-noisy-pretrain-fine-tuned_v1.2

tbosse

bert

transformers

token-classification

mit

['generated_from_trainer']

<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # bert-base-german-cased-noisy-pretrain-fine-tuned_v1.2 This model is a fine-tuned version of [tbosse/bert-base-german-cased-finetuned-subj_preTrained_with_noisyData_v1.2](https://huggingface.co/tbosse/bert-base-german-cased-finetuned-subj_preTrained_with_noisyData_v1.2) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.2810 - Precision: 0.7874 - Recall: 0.7514 - F1: 0.7690 - Accuracy: 0.9147 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 7 ### Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | No log | 1.0 | 33 | 0.3078 | 0.7675 | 0.5943 | 0.6699 | 0.8842 | | No log | 2.0 | 66 | 0.2535 | 0.7729 | 0.7486 | 0.7605 | 0.9073 | | No log | 3.0 | 99 | 0.2417 | 0.7714 | 0.7714 | 0.7714 | 0.9119 | | No log | 4.0 | 132 | 0.2532 | 0.8031 | 0.7343 | 0.7672 | 0.9142 | | No log | 5.0 | 165 | 0.2675 | 0.7834 | 0.7543 | 0.7686 | 0.9142 | | No log | 6.0 | 198 | 0.2750 | 0.7870 | 0.76 | 0.7733 | 0.9159 | | No log | 7.0 | 231 | 0.2810 | 0.7874 | 0.7514 | 0.7690 | 0.9147 | ### Framework versions - Transformers 4.19.2 - Pytorch 1.11.0+cu113 - Datasets 2.2.2 - Tokenizers 0.12.1

12a2266c62d438271e4b1db92134a6a7

slplab/wav2vec2-large-xlsr-53-korean-nia13-asia-9634_001

slplab

wav2vec2

transformers

automatic-speech-recognition

apache-2.0

['generated_from_trainer']

<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # wav2vec2-large-xlsr-53-korean-samsung-60k This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on the NIA13 ASIA dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data Training Data - Data Name: NIA13 ASIA - Num. of Samples: 9,634 - Audio Length: 9H 42M Evaluation Data - Data Name: NIA13 ASIA - Num. of Samples: 3,707 - Audio Length: 3H 37M Test Data - Data Name: NIA13 ASIA (Same as the Evaluation Data) - Num. of Samples: 3,707 - Audio Length: 3H 37M ## Training procedure  ### Training hyperparameters The following hyperparameters were used during training:  ### Training results - Validation Phone Error Rate: 19.90% - Test Phone Error Rate: 19.90% ### Framework versions - Transformers 4.21.3 - Pytorch 1.12.1 - Datasets 2.4.0 - Tokenizers 0.12.1

aa6ec52905440e0969c9a8fafc6cf76e

ZinebSN/whisper-small-swedish-Test-3000

ZinebSN

whisper

transformers

automatic-speech-recognition

apache-2.0

['sv']

['mozilla-foundation/common_voice_11_0']

['hf-asr-leaderboard', 'generated_from_trainer']

<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # Whisper Small Swedish -3000 This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the Common Voice 11.0 dataset. It achieves the following results on the evaluation set: - Loss: 0.2974 - Wer: 19.6042 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - training_steps: 3000 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:-------:| | 0.1448 | 1.29 | 1000 | 0.2953 | 21.4245 | | 0.0188 | 2.59 | 2000 | 0.2879 | 20.0882 | | 0.0233 | 3.88 | 3000 | 0.2974 | 19.6042 | ### Framework versions - Transformers 4.26.0.dev0 - Pytorch 1.13.0+cu116 - Datasets 2.7.1 - Tokenizers 0.13.2

fa8176c8386386acd76dd2a3c9c6097c

gokuls/mobilebert_add_GLUE_Experiment_logit_kd_qqp_256

gokuls

mobilebert

transformers

text-classification

apache-2.0

['en']

['glue']

['generated_from_trainer']

<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # mobilebert_add_GLUE_Experiment_logit_kd_qqp_256 This model is a fine-tuned version of [google/mobilebert-uncased](https://huggingface.co/google/mobilebert-uncased) on the GLUE QQP dataset. It achieves the following results on the evaluation set: - Loss: 0.8027 - Accuracy: 0.7596 - F1: 0.6364 - Combined Score: 0.6980 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 128 - eval_batch_size: 128 - seed: 10 - distributed_type: multi-GPU - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 50 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Combined Score | |:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|:--------------:| | 1.2838 | 1.0 | 2843 | 1.2200 | 0.6318 | 0.0 | 0.3159 | | 1.0184 | 2.0 | 5686 | 0.8422 | 0.7473 | 0.5924 | 0.6698 | | 0.8633 | 3.0 | 8529 | 0.8232 | 0.7520 | 0.5963 | 0.6742 | | 0.834 | 4.0 | 11372 | 0.8193 | 0.7563 | 0.6271 | 0.6917 | | 0.812 | 5.0 | 14215 | 0.8027 | 0.7596 | 0.6364 | 0.6980 | | 0.7871 | 6.0 | 17058 | nan | 0.6318 | 0.0 | 0.3159 | | 0.0 | 7.0 | 19901 | nan | 0.6318 | 0.0 | 0.3159 | | 0.0 | 8.0 | 22744 | nan | 0.6318 | 0.0 | 0.3159 | | 0.0 | 9.0 | 25587 | nan | 0.6318 | 0.0 | 0.3159 | | 0.0 | 10.0 | 28430 | nan | 0.6318 | 0.0 | 0.3159 | ### Framework versions - Transformers 4.26.0 - Pytorch 1.14.0a0+410ce96 - Datasets 2.9.0 - Tokenizers 0.13.2

8466e0635a43675a0b34bc6de87930da

tkubotake/xlm-roberta-base-finetuned-panx-fr

tkubotake

xlm-roberta

transformers

token-classification

mit

['xtreme']

['generated_from_trainer']

<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # xlm-roberta-base-finetuned-panx-fr This model is a fine-tuned version of [tkubotake/xlm-roberta-base-finetuned-panx-de](https://huggingface.co/tkubotake/xlm-roberta-base-finetuned-panx-de) on the xtreme dataset. It achieves the following results on the evaluation set: - Loss: 0.4157 - F1: 0.8636 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 24 - eval_batch_size: 24 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.0847 | 1.0 | 191 | 0.4066 | 0.8524 | | 0.0574 | 2.0 | 382 | 0.4025 | 0.8570 | | 0.0333 | 3.0 | 573 | 0.4157 | 0.8636 | ### Framework versions - Transformers 4.24.0 - Pytorch 1.12.1+cu113 - Datasets 2.6.1 - Tokenizers 0.13.1

0ceb5eeffb629c282138058fc91c160c

andreduarte/distilbert-base-uncased-finetuned-cola

andreduarte

distilbert

transformers

text-classification

apache-2.0

['glue']

['generated_from_trainer']

<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # distilbert-base-uncased-finetuned-cola This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the glue dataset. It achieves the following results on the evaluation set: - Loss: 0.7818 - Matthews Correlation: 0.5492 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results | Training Loss | Epoch | Step | Validation Loss | Matthews Correlation | |:-------------:|:-----:|:----:|:---------------:|:--------------------:| | 0.5257 | 1.0 | 535 | 0.5238 | 0.4004 | | 0.3516 | 2.0 | 1070 | 0.5173 | 0.5206 | | 0.2402 | 3.0 | 1605 | 0.5623 | 0.5301 | | 0.1871 | 4.0 | 2140 | 0.7421 | 0.5387 | | 0.1386 | 5.0 | 2675 | 0.7818 | 0.5492 | ### Framework versions - Transformers 4.25.1 - Pytorch 1.12.1+cu113 - Datasets 2.7.1 - Tokenizers 0.13.2

816c3a137dbc59a8a3c0d2e72b4ccb58

google/multiberts-seed_3-step_20k

google

bert

transformers

apache-2.0

['en']

['multiberts', 'multiberts-seed_3', 'multiberts-seed_3-step_20k']

# MultiBERTs, Intermediate Checkpoint - Seed 3, Step 20k MultiBERTs is a collection of checkpoints and a statistical library to support robust research on BERT. We provide 25 BERT-base models trained with similar hyper-parameters as [the original BERT model](https://github.com/google-research/bert) but with different random seeds, which causes variations in the initial weights and order of training instances. The aim is to distinguish findings that apply to a specific artifact (i.e., a particular instance of the model) from those that apply to the more general procedure. We also provide 140 intermediate checkpoints captured during the course of pre-training (we saved 28 checkpoints for the first 5 runs). The models were originally released through [http://goo.gle/multiberts](http://goo.gle/multiberts). We describe them in our paper [The MultiBERTs: BERT Reproductions for Robustness Analysis](https://arxiv.org/abs/2106.16163). This is model #3, captured at step 20k (max: 2000k, i.e., 2M steps). ## Model Description This model was captured during a reproduction of [BERT-base uncased](https://github.com/google-research/bert), for English: it is a Transformers model pretrained on a large corpus of English data, using the Masked Language Modelling (MLM) and the Next Sentence Prediction (NSP) objectives. The intended uses, limitations, training data and training procedure for the fully trained model are similar to [BERT-base uncased](https://github.com/google-research/bert). Two major differences with the original model: * We pre-trained the MultiBERTs models for 2 million steps using sequence length 512 (instead of 1 million steps using sequence length 128 then 512). * We used an alternative version of Wikipedia and Books Corpus, initially collected for [Turc et al., 2019](https://arxiv.org/abs/1908.08962). This is a best-effort reproduction, and so it is probable that differences with the original model have gone unnoticed. The performance of MultiBERTs on GLUE after full training is oftentimes comparable to that of original BERT, but we found significant differences on the dev set of SQuAD (MultiBERTs outperforms original BERT). See our [technical report](https://arxiv.org/abs/2106.16163) for more details. ### How to use Using code from [BERT-base uncased](https://huggingface.co/bert-base-uncased), here is an example based on Tensorflow: ``` from transformers import BertTokenizer, TFBertModel tokenizer = BertTokenizer.from_pretrained('google/multiberts-seed_3-step_20k') model = TFBertModel.from_pretrained("google/multiberts-seed_3-step_20k") text = "Replace me by any text you'd like." encoded_input = tokenizer(text, return_tensors='tf') output = model(encoded_input) ``` PyTorch version: ``` from transformers import BertTokenizer, BertModel tokenizer = BertTokenizer.from_pretrained('google/multiberts-seed_3-step_20k') model = BertModel.from_pretrained("google/multiberts-seed_3-step_20k") text = "Replace me by any text you'd like." encoded_input = tokenizer(text, return_tensors='pt') output = model(**encoded_input) ``` ## Citation info ```bibtex @article{sellam2021multiberts, title={The MultiBERTs: BERT Reproductions for Robustness Analysis}, author={Thibault Sellam and Steve Yadlowsky and Jason Wei and Naomi Saphra and Alexander D'Amour and Tal Linzen and Jasmijn Bastings and Iulia Turc and Jacob Eisenstein and Dipanjan Das and Ian Tenney and Ellie Pavlick}, journal={arXiv preprint arXiv:2106.16163}, year={2021} } ```

5054951bf7630834ee3b7576a7d32102

Zekunli/flan-t5-large-extraction-cnndm_fs0.1-all

Zekunli

t5

transformers

text2text-generation

apache-2.0

['generated_from_trainer']

<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # flan-t5-large-extraction-cnndm_fs0.1-all This model is a fine-tuned version of [google/flan-t5-large](https://huggingface.co/google/flan-t5-large) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.6225 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 48 - seed: 1799 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 2.0798 | 0.11 | 200 | 1.7813 | | 1.8704 | 0.23 | 400 | 1.7363 | | 1.8398 | 0.34 | 600 | 1.7100 | | 1.8068 | 0.45 | 800 | 1.6951 | | 1.8013 | 0.56 | 1000 | 1.6851 | | 1.8008 | 0.68 | 1200 | 1.6769 | | 1.783 | 0.79 | 1400 | 1.6609 | | 1.7459 | 0.9 | 1600 | 1.6578 | | 1.7394 | 1.02 | 1800 | 1.6605 | | 1.7036 | 1.13 | 2000 | 1.6464 | | 1.705 | 1.24 | 2200 | 1.6442 | | 1.6903 | 1.36 | 2400 | 1.6505 | | 1.6864 | 1.47 | 2600 | 1.6394 | | 1.7005 | 1.58 | 2800 | 1.6349 | | 1.6858 | 1.69 | 3000 | 1.6380 | | 1.6722 | 1.81 | 3200 | 1.6343 | | 1.6512 | 1.92 | 3400 | 1.6319 | | 1.6717 | 2.03 | 3600 | 1.6336 | | 1.636 | 2.15 | 3800 | 1.6352 | | 1.643 | 2.26 | 4000 | 1.6225 | | 1.6308 | 2.37 | 4200 | 1.6227 | | 1.6115 | 2.48 | 4400 | 1.6278 | | 1.6342 | 2.6 | 4600 | 1.6249 | | 1.6301 | 2.71 | 4800 | 1.6320 | | 1.6164 | 2.82 | 5000 | 1.6302 | ### Framework versions - Transformers 4.18.0 - Pytorch 1.10.0+cu111 - Datasets 2.5.1 - Tokenizers 0.12.1

bbfcc5bbc997d13f85a7be0129ab2efa

praf-choub/bart-CaPE-xsum

praf-choub

bart

transformers

summarization

bsd-3-clause

['en']

['xsum']

['summarization']

Citation ``` @misc{https://doi.org/10.48550/arxiv.2110.07166, doi = {10.48550/ARXIV.2110.07166}, url = {https://arxiv.org/abs/2110.07166}, author = {Choubey, Prafulla Kumar and Fabbri, Alexander R. and Vig, Jesse and Wu, Chien-Sheng and Liu, Wenhao and Rajani, Nazneen Fatema}, keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences}, title = {CaPE: Contrastive Parameter Ensembling for Reducing Hallucination in Abstractive Summarization}, publisher = {arXiv}, year = {2021}, copyright = {Creative Commons Attribution 4.0 International} } ```

2468d26b38f2b16cbf690b197616995b

DrishtiSharma/wav2vec2-large-xls-r-300m-as-g1

DrishtiSharma

wav2vec2

transformers

automatic-speech-recognition

apache-2.0

['as']

['mozilla-foundation/common_voice_8_0']

['automatic-speech-recognition', 'mozilla-foundation/common_voice_8_0', 'generated_from_trainer', 'as', 'robust-speech-event', 'model_for_talk', 'hf-asr-leaderboard']

<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # wav2vec2-large-xls-r-300m-as-g1 This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the MOZILLA-FOUNDATION/COMMON_VOICE_8_0 - AS dataset. It achieves the following results on the evaluation set: - Loss: 1.3327 - Wer: 0.5744 ### Evaluation Commands 1. To evaluate on mozilla-foundation/common_voice_8_0 with test split python eval.py --model_id DrishtiSharma/wav2vec2-large-xls-r-300m-as-g1 --dataset mozilla-foundation/common_voice_8_0 --config as --split test --log_outputs 2. To evaluate on speech-recognition-community-v2/dev_data Assamese language isn't available in speech-recognition-community-v2/dev_data ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0003 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 1000 - num_epochs: 200 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:------:|:----:|:---------------:|:------:| | 14.1958 | 5.26 | 100 | 7.1919 | 1.0 | | 5.0035 | 10.51 | 200 | 3.9362 | 1.0 | | 3.6193 | 15.77 | 300 | 3.4451 | 1.0 | | 3.4852 | 21.05 | 400 | 3.3536 | 1.0 | | 2.8489 | 26.31 | 500 | 1.6451 | 0.9100 | | 0.9568 | 31.56 | 600 | 1.0514 | 0.7561 | | 0.4865 | 36.82 | 700 | 1.0434 | 0.7184 | | 0.322 | 42.1 | 800 | 1.0825 | 0.7210 | | 0.2383 | 47.36 | 900 | 1.1304 | 0.6897 | | 0.2136 | 52.62 | 1000 | 1.1150 | 0.6854 | | 0.179 | 57.87 | 1100 | 1.2453 | 0.6875 | | 0.1539 | 63.15 | 1200 | 1.2211 | 0.6704 | | 0.1303 | 68.41 | 1300 | 1.2859 | 0.6747 | | 0.1183 | 73.67 | 1400 | 1.2775 | 0.6721 | | 0.0994 | 78.92 | 1500 | 1.2321 | 0.6404 | | 0.0991 | 84.21 | 1600 | 1.2766 | 0.6524 | | 0.0887 | 89.46 | 1700 | 1.3026 | 0.6344 | | 0.0754 | 94.72 | 1800 | 1.3199 | 0.6704 | | 0.0693 | 99.97 | 1900 | 1.3044 | 0.6361 | | 0.0568 | 105.26 | 2000 | 1.3541 | 0.6254 | | 0.0536 | 110.51 | 2100 | 1.3320 | 0.6249 | | 0.0529 | 115.77 | 2200 | 1.3370 | 0.6271 | | 0.048 | 121.05 | 2300 | 1.2757 | 0.6031 | | 0.0419 | 126.31 | 2400 | 1.2661 | 0.6172 | | 0.0349 | 131.56 | 2500 | 1.2897 | 0.6048 | | 0.0309 | 136.82 | 2600 | 1.2688 | 0.5962 | | 0.0278 | 142.1 | 2700 | 1.2885 | 0.5954 | | 0.0254 | 147.36 | 2800 | 1.2988 | 0.5915 | | 0.0223 | 152.62 | 2900 | 1.3153 | 0.5941 | | 0.0216 | 157.87 | 3000 | 1.2936 | 0.5937 | | 0.0186 | 163.15 | 3100 | 1.2906 | 0.5877 | | 0.0156 | 168.41 | 3200 | 1.3476 | 0.5962 | | 0.0158 | 173.67 | 3300 | 1.3363 | 0.5847 | | 0.0142 | 178.92 | 3400 | 1.3367 | 0.5847 | | 0.0153 | 184.21 | 3500 | 1.3105 | 0.5757 | | 0.0119 | 189.46 | 3600 | 1.3255 | 0.5705 | | 0.0115 | 194.72 | 3700 | 1.3340 | 0.5787 | | 0.0103 | 199.97 | 3800 | 1.3327 | 0.5744 | ### Framework versions - Transformers 4.16.2 - Pytorch 1.10.0+cu111 - Datasets 1.18.3 - Tokenizers 0.11.0

06836b810af39f2736d85422bdc5412c

adiharush/tu-nlpweb-w22-g18-e6

adiharush

distilbert

transformers

question-answering

apache-2.0

['generated_from_trainer']

<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # result This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 3e-05 - train_batch_size: 12 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2.0 ### Training results ### Framework versions - Transformers 4.27.0.dev0 - Pytorch 1.13.1 - Datasets 2.9.0 - Tokenizers 0.13.2

c4d6bc071fd5b40401576730a87dbdee

PaddlePaddle/uie-medium

PaddlePaddle

ernie

paddlenlp

apache-2.0

['zh']

[]

[](https://github.com/PaddlePaddle/PaddleNLP) # PaddlePaddle/uie-medium Information extraction suffers from its varying targets, heterogeneous structures, and demand-specific schemas. The unified text-to-structure generation framework, namely UIE, can universally model different IE tasks, adaptively generate targeted structures, and collaboratively learn general IE abilities from different knowledge sources. Specifically, UIE uniformly encodes different extraction structures via a structured extraction language, adaptively generates target extractions via a schema-based prompt mechanism - structural schema instructor, and captures the common IE abilities via a large-scale pre-trained text-to-structure model. Experiments show that UIE achieved the state-of-the-art performance on 4 IE tasks, 13 datasets, and on all supervised, low-resource, and few-shot settings for a wide range of entity, relation, event and sentiment extraction tasks and their unification. These results verified the effectiveness, universality, and transferability of UIE. UIE Paper: https://arxiv.org/abs/2203.12277 PaddleNLP released UIE model series for Information Extraction of texts and multi-modal documents which use the ERNIE 3.0 models as the pre-trained language models and were finetuned on a large amount of information extraction data.  ## Available Models | Model Name | Usage Scenarios | Supporting Tasks | | :----------------------------------------------------------: | :--------------------------------------------------------- | :--------------------------------------------------- | | `uie-base`<br />`uie-medium`<br />`uie-mini`<br />`uie-micro`<br />`uie-nano` | For **plain text** The **extractive** model of the scene supports **Chinese** | Supports entity, relation, event, opinion extraction | | `uie-base-en` | An **extractive** model for **plain text** scenarios, supports **English** | Supports entity, relation, event, opinion extraction | | `uie-m-base`<br />`uie-m-large` | An **extractive** model for **plain text** scenarios, supporting **Chinese and English** | Supports entity, relation, event, opinion extraction | | <b>`uie-x-base`</b> | An **extractive** model for **plain text** and **document** scenarios, supports **Chinese and English** | Supports entity, relation, event, opinion extraction on both plain text and documents/pictures/tables | ## Performance on Text Dataset We conducted experiments on the in-house test sets of the three different domains of Internet, medical care, and finance: <table> <tr><th row_span='2'><th colspan='2'>finance<th colspan='2'>healthcare<th colspan='2'>internet <tr><td><th>0-shot<th>5-shot<th>0-shot<th>5-shot<th>0-shot<th>5-shot <tr><td>uie-base (12L768H)<td>46.43<td>70.92<td><b>71.83</b><td>85.72<td>78.33<td>81.86 <tr><td>uie-medium (6L768H)<td>41.11<td>64.53<td>65.40<td>75.72<td>78.32<td>79.68 <tr><td>uie-mini (6L384H)<td>37.04<td>64.65<td>60.50<td>78.36<td>72.09<td>76.38 <tr><td>uie-micro (4L384H)<td>37.53<td>62.11<td>57.04<td>75.92<td>66.00<td>70.22 <tr><td>uie-nano (4L312H)<td>38.94<td>66.83<td>48.29<td>76.74<td>62.86<td>72.35 <tr><td>uie-m-large (24L1024H)<td><b>49.35</b><td><b>74.55</b><td>70.50<td><b>92.66</b ><td>78.49<td><b>83.02</b> <tr><td>uie-m-base (12L768H)<td>38.46<td>74.31<td>63.37<td>87.32<td>76.27<td>80.13 <tr><td>🧾🎓<b>uie-x-base (12L768H)</b><td>48.84<td>73.87<td>65.60<td>88.81<td><b>79.36</b> <td>81.65 </table> 0-shot means that no training data is directly used for prediction through paddlenlp.Taskflow, and 5-shot means that each category contains 5 pieces of labeled data for model fine-tuning. Experiments show that UIE can further improve the performance with a small amount of data (few-shot). > Detailed Info: https://github.com/PaddlePaddle/PaddleNLP/blob/develop/applications/information_extraction/README_en.md

23016a311de271c35d1cf8e0e7c41f1a

Salesforce/blip2-flan-t5-xl-coco

Salesforce

blip-2

transformers

image-to-text

mit

['en']

['vision', 'image-to-text', 'image-captioning', 'visual-question-answering']

# BLIP-2, Flan T5-xl, fine-tuned on COCO BLIP-2 model, leveraging [Flan T5-xl](https://huggingface.co/google/flan-t5-xl) (a large language model). It was introduced in the paper [BLIP-2: Bootstrapping Language-Image Pre-training with Frozen Image Encoders and Large Language Models](https://arxiv.org/abs/2301.12597) by Li et al. and first released in [this repository](https://github.com/salesforce/LAVIS/tree/main/projects/blip2). Disclaimer: The team releasing BLIP-2 did not write a model card for this model so this model card has been written by the Hugging Face team. ## Model description BLIP-2 consists of 3 models: a CLIP-like image encoder, a Querying Transformer (Q-Former) and a large language model. The authors initialize the weights of the image encoder and large language model from pre-trained checkpoints and keep them frozen while training the Querying Transformer, which is a BERT-like Transformer encoder that maps a set of "query tokens" to query embeddings, which bridge the gap between the embedding space of the image encoder and the large language model. The goal for the model is simply to predict the next text token, giving the query embeddings and the previous text. <img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/model_doc/blip2_architecture.jpg" alt="drawing" width="600"/> This allows the model to be used for tasks like: - image captioning - visual question answering (VQA) - chat-like conversations by feeding the image and the previous conversation as prompt to the model ## Intended uses & limitations You can use the raw model for conditional text generation given an image and optional text. See the [model hub](https://huggingface.co/models?search=Salesforce/blip) to look for fine-tuned versions on a task that interests you. ### How to use For code examples, we refer to the [documentation](https://huggingface.co/docs/transformers/main/en/model_doc/blip-2#transformers.Blip2ForConditionalGeneration.forward.example).

16fd192d2c19228210efc52bbf85be93

TestZee/t5-small-finetuned-xum-test

TestZee

t5

transformers

text2text-generation

apache-2.0

['generated_from_keras_callback']

<!-- This model card has been generated automatically according to the information Keras had access to. You should probably proofread and complete it, then remove this comment. --> # TestZee/t5-small-finetuned-xum-test This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 2.9733 - Validation Loss: 2.6463 - Epoch: 0 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'AdamWeightDecay', 'learning_rate': 2e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight_decay_rate': 0.01} - training_precision: float32 ### Training results | Train Loss | Validation Loss | Epoch | |:----------:|:---------------:|:-----:| | 2.9733 | 2.6463 | 0 | ### Framework versions - Transformers 4.20.1 - TensorFlow 2.8.2 - Datasets 2.3.2 - Tokenizers 0.12.1

bc3616ed76b830d500dcfc4b1075ec34

sd-concepts-library/sherhook-painting-v2

sd-concepts-library