Update README.md

README.md

@@ -1,251 +1,165 @@
 ---
-language: en
+language:
+- en
+license: mit
 tags:
-- exbert
-license: apache-2.0
+- text-classification
+- zero-shot-classification
 datasets:
-- bookcorpus
-- wikipedia
+- multi_nli
+- facebook/anli
+- fever
+- lingnli
+- alisawuffles/WANLI
+metrics:
+- accuracy
+pipeline_tag: zero-shot-classification
+model-index:
+- name: DeBERTa-v3-large-mnli-fever-anli-ling-wanli
+  results:
+  - task:
+      type: text-classification
+      name: Natural Language Inference
+    dataset:
+      name: MultiNLI-matched
+      type: multi_nli
+      split: validation_matched
+    metrics:
+    - type: accuracy
+      value: 0,912
+      verified: false
+  - task:
+      type: text-classification
+      name: Natural Language Inference
+    dataset:
+      name: MultiNLI-mismatched
+      type: multi_nli
+      split: validation_mismatched
+    metrics:
+    - type: accuracy
+      value: 0,908
+      verified: false
+  - task:
+      type: text-classification
+      name: Natural Language Inference
+    dataset:
+      name: ANLI-all
+      type: anli
+      split: test_r1+test_r2+test_r3
+    metrics:
+    - type: accuracy
+      value: 0,702
+      verified: false
+  - task:
+      type: text-classification
+      name: Natural Language Inference
+    dataset:
+      name: ANLI-r3
+      type: anli
+      split: test_r3
+    metrics:
+    - type: accuracy
+      value: 0,64
+      verified: false
+  - task:
+      type: text-classification
+      name: Natural Language Inference
+    dataset:
+      name: WANLI
+      type: alisawuffles/WANLI
+      split: test
+    metrics:
+    - type: accuracy
+      value: 0,77
+      verified: false
+  - task:
+      type: text-classification
+      name: Natural Language Inference
+    dataset:
+      name: LingNLI
+      type: lingnli
+      split: test
+    metrics:
+    - type: accuracy
+      value: 0,87
+      verified: false
 ---
 
-# BERT base model (uncased)
-
-Pretrained model on English language using a masked language modeling (MLM) objective. It was introduced in
-[this paper](https://arxiv.org/abs/1810.04805) and first released in
-[this repository](https://github.com/google-research/bert). This model is uncased: it does not make a difference
-between english and English.
-
-Disclaimer: The team releasing BERT did not write a model card for this model so this model card has been written by
-the Hugging Face team.
-
+# DeBERTa-v3-large-mnli-fever-anli-ling-wanli
 ## Model description
+This model was fine-tuned on the [MultiNLI](https://huggingface.co/datasets/multi_nli), [Fever-NLI](https://github.com/easonnie/combine-FEVER-NSMN/blob/master/other_resources/nli_fever.md), Adversarial-NLI ([ANLI](https://huggingface.co/datasets/anli)), [LingNLI](https://arxiv.org/pdf/2104.07179.pdf) and [WANLI](https://huggingface.co/datasets/alisawuffles/WANLI) datasets, which comprise 885 242 NLI hypothesis-premise pairs. This model is the best performing NLI model on the Hugging Face Hub as of 06.06.22 and can be used for zero-shot classification. It significantly outperforms all other large models on the [ANLI benchmark](https://github.com/facebookresearch/anli).
 
-BERT is a transformers model pretrained on a large corpus of English data in a self-supervised fashion. This means it
-was pretrained on the raw texts only, with no humans labeling them in any way (which is why it can use lots of
-publicly available data) with an automatic process to generate inputs and labels from those texts. More precisely, it
-was pretrained with two objectives:
-
-- Masked language modeling (MLM): taking a sentence, the model randomly masks 15% of the words in the input then run
-  the entire masked sentence through the model and has to predict the masked words. This is different from traditional
-  recurrent neural networks (RNNs) that usually see the words one after the other, or from autoregressive models like
-  GPT which internally masks the future tokens. It allows the model to learn a bidirectional representation of the
-  sentence.
-- Next sentence prediction (NSP): the models concatenates two masked sentences as inputs during pretraining. Sometimes
-  they correspond to sentences that were next to each other in the original text, sometimes not. The model then has to
-  predict if the two sentences were following each other or not.
-
-This way, the model learns an inner representation of the English language that can then be used to extract features
-useful for downstream tasks: if you have a dataset of labeled sentences, for instance, you can train a standard
-classifier using the features produced by the BERT model as inputs.
-
-## Model variations
-
-BERT has originally been released in base and large variations, for cased and uncased input text. The uncased models also strips out an accent markers.  
-Chinese and multilingual uncased and cased versions followed shortly after.  
-Modified preprocessing with whole word masking has replaced subpiece masking in a following work, with the release of two models.  
-Other 24 smaller models are released afterward.  
-
-The detailed release history can be found on the [google-research/bert readme](https://github.com/google-research/bert/blob/master/README.md) on github.
-
-| Model | #params | Language |
-|------------------------|--------------------------------|-------|
-| [`bert-base-uncased`](https://huggingface.co/bert-base-uncased) | 110M   | English |
-| [`bert-large-uncased`](https://huggingface.co/bert-large-uncased)              | 340M    | English | sub 
-| [`bert-base-cased`](https://huggingface.co/bert-base-cased)        | 110M    | English |
-| [`bert-large-cased`](https://huggingface.co/bert-large-cased) | 340M    |  English |
-| [`bert-base-chinese`](https://huggingface.co/bert-base-chinese) | 110M    | Chinese |
-| [`bert-base-multilingual-cased`](https://huggingface.co/bert-base-multilingual-cased) | 110M | Multiple |
-| [`bert-large-uncased-whole-word-masking`](https://huggingface.co/bert-large-uncased-whole-word-masking) | 340M | English |
-| [`bert-large-cased-whole-word-masking`](https://huggingface.co/bert-large-cased-whole-word-masking) | 340M | English |
-
-## Intended uses & limitations
-
-You can use the raw model for either masked language modeling or next sentence prediction, but it's mostly intended to
-be fine-tuned on a downstream task. See the [model hub](https://huggingface.co/models?filter=bert) to look for
-fine-tuned versions of a task that interests you.
-
-Note that this model is primarily aimed at being fine-tuned on tasks that use the whole sentence (potentially masked)
-to make decisions, such as sequence classification, token classification or question answering. For tasks such as text
-generation you should look at model like GPT2.
-
-### How to use
+The foundation model is [DeBERTa-v3-large from Microsoft](https://huggingface.co/microsoft/deberta-v3-large). DeBERTa-v3 combines several recent innovations compared to classical Masked Language Models like BERT, RoBERTa etc., see the [paper](https://arxiv.org/abs/2111.09543)
 
-You can use this model directly with a pipeline for masked language modeling:
 
+### How to use the model
+#### Simple zero-shot classification pipeline
 ```python
->>> from transformers import pipeline
->>> unmasker = pipeline('fill-mask', model='bert-base-uncased')
->>> unmasker("Hello I'm a [MASK] model.")
-
-[{'sequence': "[CLS] hello i'm a fashion model. [SEP]",
-  'score': 0.1073106899857521,
-  'token': 4827,
-  'token_str': 'fashion'},
- {'sequence': "[CLS] hello i'm a role model. [SEP]",
-  'score': 0.08774490654468536,
-  'token': 2535,
-  'token_str': 'role'},
- {'sequence': "[CLS] hello i'm a new model. [SEP]",
-  'score': 0.05338378623127937,
-  'token': 2047,
-  'token_str': 'new'},
- {'sequence': "[CLS] hello i'm a super model. [SEP]",
-  'score': 0.04667217284440994,
-  'token': 3565,
-  'token_str': 'super'},
- {'sequence': "[CLS] hello i'm a fine model. [SEP]",
-  'score': 0.027095865458250046,
-  'token': 2986,
-  'token_str': 'fine'}]
+from transformers import pipeline
+classifier = pipeline("zero-shot-classification", model="MoritzLaurer/DeBERTa-v3-large-mnli-fever-anli-ling-wanli")
+sequence_to_classify = "Angela Merkel is a politician in Germany and leader of the CDU"
+candidate_labels = ["politics", "economy", "entertainment", "environment"]
+output = classifier(sequence_to_classify, candidate_labels, multi_label=False)
+print(output)
 ```
-
-Here is how to use this model to get the features of a given text in PyTorch:
-
+#### NLI use-case
 ```python
-from transformers import BertTokenizer, BertModel
-tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
-model = BertModel.from_pretrained("bert-base-uncased")
-text = "Replace me by any text you'd like."
-encoded_input = tokenizer(text, return_tensors='pt')
-output = model(**encoded_input)
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+import torch
+device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu")
+
+model_name = "MoritzLaurer/DeBERTa-v3-large-mnli-fever-anli-ling-wanli"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForSequenceClassification.from_pretrained(model_name)
+
+premise = "I first thought that I liked the movie, but upon second thought it was actually disappointing."
+hypothesis = "The movie was not good."
+
+input = tokenizer(premise, hypothesis, truncation=True, return_tensors="pt")
+output = model(input["input_ids"].to(device))  # device = "cuda:0" or "cpu"
+prediction = torch.softmax(output["logits"][0], -1).tolist()
+label_names = ["entailment", "neutral", "contradiction"]
+prediction = {name: round(float(pred) * 100, 1) for pred, name in zip(prediction, label_names)}
+print(prediction)
 ```
 
-and in TensorFlow:
+### Training data
+DeBERTa-v3-large-mnli-fever-anli-ling-wanli was trained on the [MultiNLI](https://huggingface.co/datasets/multi_nli), [Fever-NLI](https://github.com/easonnie/combine-FEVER-NSMN/blob/master/other_resources/nli_fever.md), Adversarial-NLI ([ANLI](https://huggingface.co/datasets/anli)), [LingNLI](https://arxiv.org/pdf/2104.07179.pdf) and [WANLI](https://huggingface.co/datasets/alisawuffles/WANLI) datasets, which comprise 885 242 NLI hypothesis-premise pairs. Note that [SNLI](https://huggingface.co/datasets/snli) was explicitly excluded due to quality issues with the dataset. More data does not necessarily make for better NLI models. 
 
-```python
-from transformers import BertTokenizer, TFBertModel
-tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
-model = TFBertModel.from_pretrained("bert-base-uncased")
-text = "Replace me by any text you'd like."
-encoded_input = tokenizer(text, return_tensors='tf')
-output = model(encoded_input)
-```
+### Training procedure
+DeBERTa-v3-large-mnli-fever-anli-ling-wanli was trained using the Hugging Face trainer with the following hyperparameters. Note that longer training with more epochs hurt performance in my tests (overfitting).
 
-### Limitations and bias
 
-Even if the training data used for this model could be characterized as fairly neutral, this model can have biased
-predictions:
-
-```python
->>> from transformers import pipeline
->>> unmasker = pipeline('fill-mask', model='bert-base-uncased')
->>> unmasker("The man worked as a [MASK].")
-
-[{'sequence': '[CLS] the man worked as a carpenter. [SEP]',
-  'score': 0.09747550636529922,
-  'token': 10533,
-  'token_str': 'carpenter'},
- {'sequence': '[CLS] the man worked as a waiter. [SEP]',
-  'score': 0.0523831807076931,
-  'token': 15610,
-  'token_str': 'waiter'},
- {'sequence': '[CLS] the man worked as a barber. [SEP]',
-  'score': 0.04962705448269844,
-  'token': 13362,
-  'token_str': 'barber'},
- {'sequence': '[CLS] the man worked as a mechanic. [SEP]',
-  'score': 0.03788609802722931,
-  'token': 15893,
-  'token_str': 'mechanic'},
- {'sequence': '[CLS] the man worked as a salesman. [SEP]',
-  'score': 0.037680890411138535,
-  'token': 18968,
-  'token_str': 'salesman'}]
-
->>> unmasker("The woman worked as a [MASK].")
-
-[{'sequence': '[CLS] the woman worked as a nurse. [SEP]',
-  'score': 0.21981462836265564,
-  'token': 6821,
-  'token_str': 'nurse'},
- {'sequence': '[CLS] the woman worked as a waitress. [SEP]',
-  'score': 0.1597415804862976,
-  'token': 13877,
-  'token_str': 'waitress'},
- {'sequence': '[CLS] the woman worked as a maid. [SEP]',
-  'score': 0.1154729500412941,
-  'token': 10850,
-  'token_str': 'maid'},
- {'sequence': '[CLS] the woman worked as a prostitute. [SEP]',
-  'score': 0.037968918681144714,
-  'token': 19215,
-  'token_str': 'prostitute'},
- {'sequence': '[CLS] the woman worked as a cook. [SEP]',
-  'score': 0.03042375110089779,
-  'token': 5660,
-  'token_str': 'cook'}]
+```
+training_args = TrainingArguments(
+    num_train_epochs=4,              # total number of training epochs
+    learning_rate=5e-06,
+    per_device_train_batch_size=16,   # batch size per device during training
+    gradient_accumulation_steps=2,    # doubles the effective batch_size to 32, while decreasing memory requirements
+    per_device_eval_batch_size=64,    # batch size for evaluation
+    warmup_ratio=0.06,                # number of warmup steps for learning rate scheduler
+    weight_decay=0.01,               # strength of weight decay
+    fp16=True                        # mixed precision training
+)
 ```
 
-This bias will also affect all fine-tuned versions of this model.
-
-## Training data
-
-The BERT model was pretrained on [BookCorpus](https://yknzhu.wixsite.com/mbweb), a dataset consisting of 11,038
-unpublished books and [English Wikipedia](https://en.wikipedia.org/wiki/English_Wikipedia) (excluding lists, tables and
-headers).
-
-## Training procedure
+### Eval results
+The model was evaluated using the test sets for MultiNLI, ANLI, LingNLI, WANLI and the dev set for Fever-NLI. The metric used is accuracy.
+The model achieves state-of-the-art performance on each dataset. Surprisingly, it outperforms the previous [state-of-the-art on ANLI](https://github.com/facebookresearch/anli) (ALBERT-XXL) by 8,3%. I assume that this is because ANLI was created to fool masked language models like RoBERTa (or ALBERT), while DeBERTa-v3 uses a better pre-training objective (RTD), disentangled attention and I fine-tuned it on higher quality NLI data. 
 
-### Preprocessing
+|Datasets|mnli_test_m|mnli_test_mm|anli_test|anli_test_r3|ling_test|wanli_test|
+| :---: | :---: | :---: | :---: | :---: | :---: | :---: |
+|Accuracy|0.912|0.908|0.702|0.64|0.87|0.77|
+|Speed (text/sec, A100 GPU)|696.0|697.0|488.0|425.0|828.0|980.0|
 
-The texts are lowercased and tokenized using WordPiece and a vocabulary size of 30,000. The inputs of the model are
-then of the form:
+## Limitations and bias
+Please consult the original DeBERTa-v3 paper and literature on different NLI datasets for more information on the training data and potential biases. The model will reproduce statistical patterns in the training data. 
 
-```
-[CLS] Sentence A [SEP] Sentence B [SEP]
-```
+## Citation
+If you use this model, please cite: Laurer, Moritz, Wouter van Atteveldt, Andreu Salleras Casas, and Kasper Welbers. 2022. ‘Less Annotating, More Classifying – Addressing the Data Scarcity Issue of Supervised Machine Learning with Deep Transfer Learning and BERT - NLI’. Preprint, June. Open Science Framework. https://osf.io/74b8k.
 
-With probability 0.5, sentence A and sentence B correspond to two consecutive sentences in the original corpus, and in
-the other cases, it's another random sentence in the corpus. Note that what is considered a sentence here is a
-consecutive span of text usually longer than a single sentence. The only constrain is that the result with the two
-"sentences" has a combined length of less than 512 tokens.
-
-The details of the masking procedure for each sentence are the following:
-- 15% of the tokens are masked.
-- In 80% of the cases, the masked tokens are replaced by `[MASK]`.
-- In 10% of the cases, the masked tokens are replaced by a random token (different) from the one they replace.
-- In the 10% remaining cases, the masked tokens are left as is.
-
-### Pretraining
-
-The model was trained on 4 cloud TPUs in Pod configuration (16 TPU chips total) for one million steps with a batch size
-of 256. The sequence length was limited to 128 tokens for 90% of the steps and 512 for the remaining 10%. The optimizer
-used is Adam with a learning rate of 1e-4, \\(\beta_{1} = 0.9\\) and \\(\beta_{2} = 0.999\\), a weight decay of 0.01,
-learning rate warmup for 10,000 steps and linear decay of the learning rate after.
-
-## Evaluation results
-
-When fine-tuned on downstream tasks, this model achieves the following results:
-
-Glue test results:
-
-| Task | MNLI-(m/mm) | QQP  | QNLI | SST-2 | CoLA | STS-B | MRPC | RTE  | Average |
-|:----:|:-----------:|:----:|:----:|:-----:|:----:|:-----:|:----:|:----:|:-------:|
-|      | 84.6/83.4   | 71.2 | 90.5 | 93.5  | 52.1 | 85.8  | 88.9 | 66.4 | 79.6    |
-
-
-### BibTeX entry and citation info
-
-```bibtex
-@article{DBLP:journals/corr/abs-1810-04805,
-  author    = {Jacob Devlin and
-               Ming{-}Wei Chang and
-               Kenton Lee and
-               Kristina Toutanova},
-  title     = {{BERT:} Pre-training of Deep Bidirectional Transformers for Language
-               Understanding},
-  journal   = {CoRR},
-  volume    = {abs/1810.04805},
-  year      = {2018},
-  url       = {http://arxiv.org/abs/1810.04805},
-  archivePrefix = {arXiv},
-  eprint    = {1810.04805},
-  timestamp = {Tue, 30 Oct 2018 20:39:56 +0100},
-  biburl    = {https://dblp.org/rec/journals/corr/abs-1810-04805.bib},
-  bibsource = {dblp computer science bibliography, https://dblp.org}
-}
-```
+### Ideas for cooperation or questions?
+If you have questions or ideas for cooperation, contact me at m{dot}laurer{at}vu{dot}nl or [LinkedIn](https://www.linkedin.com/in/moritz-laurer/)
 
-<a href="https://huggingface.co/exbert/?model=bert-base-uncased">
-	<img width="300px" src="https://cdn-media.huggingface.co/exbert/button.png">
-</a>
+### Debugging and issues
+Note that DeBERTa-v3 was released on 06.12.21 and older versions of HF Transformers seem to have issues running the model (e.g. resulting in an issue with the tokenizer). Using Transformers>=4.13 might solve some issues.