| | --- |
| | language: |
| | - en |
| | license: mit |
| | tags: |
| | - text-classification |
| | - zero-shot-classification |
| | datasets: |
| | - 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 |
| | --- |
| | |
| | # 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). 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) |
| |
|
| |
|
| | ### How to use the model |
| | #### Simple zero-shot classification pipeline |
| | ```python |
| | 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) |
| | ``` |
| | #### NLI use-case |
| | ```python |
| | 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) |
| | ``` |
| |
|
| | ### 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. |
| | |
| | ## 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. |
| | |
| | ### 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. |
| | |
