transformers pytorch t5 text2text-generation en dataset:tals/vitaminc dataset:SetFit/mnli dataset:snli dataset:fever dataset:paws dataset:scitail arxiv:2204.04991 arxiv:1508.05326 arxiv:1904.01130 arxiv:2103.08541 license:apache-2.0 autotrain_compatible text-generation-inference endpoints_compatible region:us
---
datasets:
- tals/vitaminc
- SetFit/mnli
- snli
- fever
- paws
- scitail
language:
- en
license: apache-2.0
---
This is an NLI model based on T5-XXL that predicts a binary label ('1' - Entailment, '0' - No entailment).

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:
- SNLI ([Bowman et al., 2015](https://arxiv.org/abs/1508.05326))
- MNLI ([Williams et al., 2018](https://aclanthology.org/N18-1101.pdf))
- Fever ([Thorne et al., 2018](https://aclanthology.org/N18-1074.pdf))
- Scitail ([Khot et al., 2018](http://ai2-website.s3.amazonaws.com/publications/scitail-aaai-2018_cameraready.pdf))
- PAWS ([Zhang et al. 2019](https://arxiv.org/abs/1904.01130))
- VitaminC ([Schuster et al., 2021](https://arxiv.org/pdf/2103.08541.pdf))

The input format for the model is: "premise: PREMISE_TEXT hypothesis: HYPOTHESIS_TEXT".

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.

```
@inproceedings{honovich-etal-2022-true-evaluating,
    title = "{TRUE}: Re-evaluating Factual Consistency Evaluation",
    author = "Honovich, Or  and
      Aharoni, Roee  and
      Herzig, Jonathan  and
      Taitelbaum, Hagai  and
      Kukliansy, Doron  and
      Cohen, Vered  and
      Scialom, Thomas  and
      Szpektor, Idan  and
      Hassidim, Avinatan  and
      Matias, Yossi",
    booktitle = "Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies",
    month = jul,
    year = "2022",
    address = "Seattle, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2022.naacl-main.287",
    doi = "10.18653/v1/2022.naacl-main.287",
    pages = "3905--3920",
}
```