File size: 1,934 Bytes
08423b4 31e6f12 3bb628d 31e6f12 3bb628d aa6cfe1 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 |
---
license: apache-2.0
datasets:
- tals/vitaminc
- SetFit/mnli
- snli
- fever
- paws
- scitail
language:
- en
---
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",
}
``` |