---
license: cc-by-4.0
---
This is model based on mT5-XXL that predicts a binary label for a given article and summary for Q4 (attribution), as defined in the [SEAHORSE paper](https://arxiv.org/abs/2305.13194)  (Clark et al., 2023).

It is trained similarly to the [TRUE paper (Honovich et al, 2022)](https://arxiv.org/pdf/2204.04991.pdf) on human ratings from the SEAHORSE dataset in 6 languages:
- German
- English
- Spanish
- Russian
- Turkish
- Vietnamese

The input format for the model is: "premise: ARTICLE hypothesis: SUMMARY", where ARTICLE is the document being summarized and SUMMARY is the candidate summary.

There is also a smaller (mT5-L) version of this model, as well as metrics trained for each of the other 5 dimensions described in the original paper.

The full citation for the SEAHORSE paper is:
```
@misc{clark2023seahorse,
      title={SEAHORSE: A Multilingual, Multifaceted Dataset for Summarization Evaluation}, 
      author={Elizabeth Clark and Shruti Rijhwani and Sebastian Gehrmann and Joshua Maynez and Roee Aharoni and Vitaly Nikolaev and Thibault Sellam and Aditya Siddhant and Dipanjan Das and Ankur P. Parikh},
      year={2023},
      eprint={2305.13194},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}
```

Contact: seahorse-authors@google.com