--- language: - en tags: - text-classification - zero-shot-classification license: mit metrics: - accuracy datasets: - multi_nli - anli - fever - lingnli - alisawuffles/WANLI #pipeline_tag: #- text-classification widget: - text: "I first thought that I really liked the movie, but upon second thought it was actually disappointing. [SEP] The movie was not good." model-index: # info: https://github.com/huggingface/hub-docs/blame/main/modelcard.md - name: DeBERTa-v3-large-mnli-fever-anli-ling-wanli results: - task: type: text-classification # Required. Example: automatic-speech-recognition name: Natural Language Inference # Optional. Example: Speech Recognition dataset: type: multi_nli # Required. Example: common_voice. Use dataset id from https://hf.co/datasets name: MultiNLI-matched # Required. A pretty name for the dataset. Example: Common Voice (French) split: validation_matched # Optional. Example: test metrics: - type: accuracy # Required. Example: wer. Use metric id from https://hf.co/metrics value: 0,912 # Required. Example: 20.90 #name: # Optional. Example: Test WER verified: false # Optional. If true, indicates that evaluation was generated by Hugging Face (vs. self-reported). - task: type: text-classification # Required. Example: automatic-speech-recognition name: Natural Language Inference # Optional. Example: Speech Recognition dataset: type: multi_nli # Required. Example: common_voice. Use dataset id from https://hf.co/datasets name: MultiNLI-mismatched # Required. A pretty name for the dataset. Example: Common Voice (French) split: validation_mismatched # Optional. Example: test metrics: - type: accuracy # Required. Example: wer. Use metric id from https://hf.co/metrics value: 0,908 # Required. Example: 20.90 #name: # Optional. Example: Test WER verified: false # Optional. If true, indicates that evaluation was generated by Hugging Face (vs. self-reported). - task: type: text-classification # Required. Example: automatic-speech-recognition name: Natural Language Inference # Optional. Example: Speech Recognition dataset: type: anli # Required. Example: common_voice. Use dataset id from https://hf.co/datasets name: ANLI-all # Required. A pretty name for the dataset. Example: Common Voice (French) split: test_r1+test_r2+test_r3 # Optional. Example: test metrics: - type: accuracy # Required. Example: wer. Use metric id from https://hf.co/metrics value: 0,702 # Required. Example: 20.90 #name: # Optional. Example: Test WER verified: false # Optional. If true, indicates that evaluation was generated by Hugging Face (vs. self-reported). - task: type: text-classification # Required. Example: automatic-speech-recognition name: Natural Language Inference # Optional. Example: Speech Recognition dataset: type: anli # Required. Example: common_voice. Use dataset id from https://hf.co/datasets name: ANLI-r3 # Required. A pretty name for the dataset. Example: Common Voice (French) split: test_r3 # Optional. Example: test metrics: - type: accuracy # Required. Example: wer. Use metric id from https://hf.co/metrics value: 0,64 # Required. Example: 20.90 #name: # Optional. Example: Test WER verified: false # Optional. If true, indicates that evaluation was generated by Hugging Face (vs. self-reported). - task: type: text-classification # Required. Example: automatic-speech-recognition name: Natural Language Inference # Optional. Example: Speech Recognition dataset: type: alisawuffles/WANLI # Required. Example: common_voice. Use dataset id from https://hf.co/datasets name: WANLI # Required. A pretty name for the dataset. Example: Common Voice (French) split: test # Optional. Example: test metrics: - type: accuracy # Required. Example: wer. Use metric id from https://hf.co/metrics value: 0,77 # Required. Example: 20.90 #name: # Optional. Example: Test WER verified: false # Optional. If true, indicates that evaluation was generated by Hugging Face (vs. self-reported). - task: type: text-classification # Required. Example: automatic-speech-recognition name: Natural Language Inference # Optional. Example: Speech Recognition dataset: type: lingnli # Required. Example: common_voice. Use dataset id from https://hf.co/datasets name: LingNLI # Required. A pretty name for the dataset. Example: Common Voice (French) split: test # Optional. Example: test metrics: - type: accuracy # Required. Example: wer. Use metric id from https://hf.co/metrics value: 0,87 # Required. Example: 20.90 #name: # Optional. Example: Test WER verified: false # Optional. If true, indicates that evaluation was generated by Hugging Face (vs. self-reported). --- # 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). The foundation model is [DeBERTa-v3-large from Microsoft](https://huggingface.co/microsoft/deberta-v3-large). Released on 06.12.21, DeBERTa-v3-large is currently the best large-sized foundation model for text classification. It combines several recent innovations compared to classical Masked Language Models like BERT, RoBERTa etc., see the [paper](https://arxiv.org/abs/2111.09543) ## Intended uses & limitations #### How to use the model ```python from transformers import AutoTokenizer, AutoModelForSequenceClassification import torch 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) ``` ### 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. ### 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). ``` 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 ) ``` ### 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. |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| ## 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. ### BibTeX entry and citation info If you want to cite this model, please cite my [preprint on low-resource text classification](https://osf.io/74b8k/) and the original DeBERTa-v3 paper. ### 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/) ### 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.