This is a source-only COMET model used for efficient evaluation subset selection. Specifically this model predicts difficulty times discriminability distilled from an IRT model from up to WMT2022 (inclusive). The higher the scores, the better it is for evaluation because models will likely fail to translate the segment. It is not compatible with the original Unbabel's COMET and to run it you have to install github.com/zouharvi/PreCOMET:

You can then use it in Python:

The primary use of this model is from the subset2evaluate package:

Random selection gives us only one cluster and system-level Spearman correlation of 0.71 when we have a budget for only 100 segments. However, by using this model:

we get more clusters and higher correlation. You can expect a bigger effect on a larger scale, as described in the paper.

This work is described in How to Select Datapoints for Efficient Human Evaluation of NLG Models?. Cite as:

@misc{zouhar2025selectdatapointsefficienthuman,
    title={How to Select Datapoints for Efficient Human Evaluation of NLG Models?}, 
    author={Vilém Zouhar and Peng Cui and Mrinmaya Sachan},
    year={2025},
    eprint={2501.18251},
    archivePrefix={arXiv},
    primaryClass={cs.CL},
    url={https://arxiv.org/abs/2501.18251}, 
}