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--- |
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license: apache-2.0 |
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datasets: |
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- multi_nli |
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language: |
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- en |
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pipeline_tag: text-classification |
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--- |
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# DeBERTa-v3 (large) fine-tuned to Multi-NLI (MNLI) |
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This model is for Textual Entailment (aka NLI), i.e., predict whether `textA` is supported by `textB`. More specifically, it's a 2-way classification where the relationship between `textA` and `textB` can be **entail, neutral, contradict**. |
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- Input: (`textA`, `textB`) |
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- Output: prob(entail), prob(contradict) |
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Note that during training, all 3 labels (entail, neural, contradict) were used. But for this model, the neural output head has been removed. |
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## Model Details |
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- Base model: [deberta-v3-large](https://huggingface.co/microsoft/deberta-v3-large) |
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- Training data: [MNLI](https://huggingface.co/datasets/multi_nli) |
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- Training details: num_epochs = 3, batch_size = 16, `textA=hypothesis`, `textB=premise` |
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## Example |
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```python |
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from transformers import AutoTokenizer, AutoModelForSequenceClassification |
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tokenizer = AutoTokenizer.from_pretrained("potsawee/deberta-v3-large-mnli") |
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model = AutoModelForSequenceClassification.from_pretrained("potsawee/deberta-v3-large-mnli") |
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textA = "Kyle Walker has a personal issue" |
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textB = "Kyle Walker will remain Manchester City captain following reports about his private life, says boss Pep Guardiola." |
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inputs = tokenizer.batch_encode_plus( |
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batch_text_or_text_pairs=[(textA, textB)], |
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add_special_tokens=True, return_tensors="pt", |
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) |
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logits = model(**inputs).logits # neutral is already removed |
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probs = torch.softmax(logits, dim=-1)[0] |
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# probs = [0.7080, 0.2920], meaning that prob(entail) = 0.708, prob(contradict) = 0.292 |
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``` |
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## Citation |
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```bibtex |
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@article{manakul2023selfcheckgpt, |
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title={Selfcheckgpt: Zero-resource black-box hallucination detection for generative large language models}, |
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author={Manakul, Potsawee and Liusie, Adian and Gales, Mark JF}, |
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journal={arXiv preprint arXiv:2303.08896}, |
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year={2023} |
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} |
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``` |
