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--- |
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license: apache-2.0 |
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datasets: |
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- tals/vitaminc |
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- SetFit/mnli |
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- snli |
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- fever |
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- paws |
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- scitail |
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language: |
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- en |
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--- |
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This is an NLI model based on T5-XXL that predicts a binary label ('1' - Entailment, '0' - No entailment). |
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It is trained similarly to the NLI model described in the [TRUE paper (Honovich et al, 2022)](https://arxiv.org/pdf/2204.04991.pdf), but using the following datasets instead of ANLI: |
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- SNLI ([Bowman et al., 2015](https://arxiv.org/abs/1508.05326)) |
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- MNLI ([Williams et al., 2018](https://aclanthology.org/N18-1101.pdf)) |
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- Fever ([Thorne et al., 2018](https://aclanthology.org/N18-1074.pdf)) |
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- Scitail ([Khot et al., 2018](http://ai2-website.s3.amazonaws.com/publications/scitail-aaai-2018_cameraready.pdf)) |
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- PAWS ([Zhang et al. 2019](https://arxiv.org/abs/1904.01130)) |
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- VitaminC ([Schuster et al., 2021](https://arxiv.org/pdf/2103.08541.pdf)) |
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The input format for the model is: "premise: PREMISE_TEXT hypothesis: HYPOTHESIS_TEXT". |
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If you use this model for a research publication, please cite the TRUE paper (using the bibtex entry below) and the dataset papers mentioned above. |
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``` |
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@inproceedings{honovich-etal-2022-true-evaluating, |
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title = "{TRUE}: Re-evaluating Factual Consistency Evaluation", |
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author = "Honovich, Or and |
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Aharoni, Roee and |
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Herzig, Jonathan and |
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Taitelbaum, Hagai and |
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Kukliansy, Doron and |
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Cohen, Vered and |
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Scialom, Thomas and |
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Szpektor, Idan and |
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Hassidim, Avinatan and |
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Matias, Yossi", |
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booktitle = "Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies", |
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month = jul, |
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year = "2022", |
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address = "Seattle, United States", |
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publisher = "Association for Computational Linguistics", |
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url = "https://aclanthology.org/2022.naacl-main.287", |
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doi = "10.18653/v1/2022.naacl-main.287", |
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pages = "3905--3920", |
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} |
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``` |
