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--- |
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language: |
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- en |
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license: mit |
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tags: |
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- text-classification |
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- zero-shot-classification |
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datasets: |
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- multi_nli |
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- facebook/anli |
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- fever |
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- lingnli |
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metrics: |
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- accuracy |
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pipeline_tag: zero-shot-classification |
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--- |
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# DeBERTa-v3-xsmall-mnli-fever-anli-ling-binary |
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## Model description |
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This model was trained on 782 357 hypothesis-premise pairs from 4 NLI datasets: [MultiNLI](https://huggingface.co/datasets/multi_nli), [Fever-NLI](https://github.com/easonnie/combine-FEVER-NSMN/blob/master/other_resources/nli_fever.md), [LingNLI](https://arxiv.org/abs/2104.07179) and [ANLI](https://github.com/facebookresearch/anli). |
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Note that the model was trained on binary NLI to predict either "entailment" or "not-entailment". This is specifically designed for zero-shot classification, where the difference between "neutral" and "contradiction" is irrelevant. |
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The base model is [DeBERTa-v3-xsmall from Microsoft](https://huggingface.co/microsoft/deberta-v3-xsmall). The v3 variant of DeBERTa substantially outperforms previous versions of the model by including a different pre-training objective, see the [DeBERTa-V3 paper](https://arxiv.org/abs/2111.09543). |
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For highest performance (but less speed), I recommend using https://huggingface.co/MoritzLaurer/DeBERTa-v3-large-mnli-fever-anli-ling-wanli. |
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## Intended uses & limitations |
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#### How to use the model |
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```python |
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from transformers import AutoTokenizer, AutoModelForSequenceClassification |
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import torch |
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device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu") |
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model_name = "MoritzLaurer/DeBERTa-v3-xsmall-mnli-fever-anli-ling-binary" |
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tokenizer = AutoTokenizer.from_pretrained(model_name) |
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model = AutoModelForSequenceClassification.from_pretrained(model_name) |
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premise = "I first thought that I liked the movie, but upon second thought it was actually disappointing." |
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hypothesis = "The movie was good." |
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input = tokenizer(premise, hypothesis, truncation=True, return_tensors="pt") |
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output = model(input["input_ids"].to(device)) # device = "cuda:0" or "cpu" |
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prediction = torch.softmax(output["logits"][0], -1).tolist() |
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label_names = ["entailment", "not_entailment"] |
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prediction = {name: round(float(pred) * 100, 1) for pred, name in zip(prediction, label_names)} |
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print(prediction) |
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``` |
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### Training data |
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This model was trained on 782 357 hypothesis-premise pairs from 4 NLI datasets: [MultiNLI](https://huggingface.co/datasets/multi_nli), [Fever-NLI](https://github.com/easonnie/combine-FEVER-NSMN/blob/master/other_resources/nli_fever.md), [LingNLI](https://arxiv.org/abs/2104.07179) and [ANLI](https://github.com/facebookresearch/anli). |
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### Training procedure |
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DeBERTa-v3-xsmall-mnli-fever-anli-ling-binary was trained using the Hugging Face trainer with the following hyperparameters. |
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``` |
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training_args = TrainingArguments( |
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num_train_epochs=5, # total number of training epochs |
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learning_rate=2e-05, |
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per_device_train_batch_size=32, # batch size per device during training |
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per_device_eval_batch_size=32, # batch size for evaluation |
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warmup_ratio=0.1, # number of warmup steps for learning rate scheduler |
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weight_decay=0.06, # strength of weight decay |
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fp16=True # mixed precision training |
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) |
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``` |
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### Eval results |
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The model was evaluated using the binary test sets for MultiNLI, ANLI, LingNLI and the binary dev set for Fever-NLI (two classes instead of three). The metric used is accuracy. |
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dataset | mnli-m-2c | mnli-mm-2c | fever-nli-2c | anli-all-2c | anli-r3-2c | lingnli-2c |
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--------|---------|----------|---------|----------|----------|------ |
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accuracy | 0.925 | 0.922 | 0.892 | 0.676 | 0.665 | 0.888 |
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speed (text/sec, CPU, 128 batch) | 6.0 | 6.3 | 3.0 | 5.8 | 5.0 | 7.6 |
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speed (text/sec, GPU Tesla P100, 128 batch) | 473 | 487 | 230 | 390 | 340 | 586 |
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## Limitations and bias |
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Please consult the original DeBERTa paper and literature on different NLI datasets for potential biases. |
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## Citation |
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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. |
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### Ideas for cooperation or questions? |
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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/) |
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### Debugging and issues |
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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. |
