This model is based on COMET-poly, which is a fork but not compatible with original Unbabel's COMET.
To run the model, you need to first install this version of COMET either with:
This model scores the translation mt given its source. It is a baseline model that other COMET-poly models are compared to.
python
1import comet_poly
2model = comet_poly.load_from_checkpoint(comet_poly.download_model("zouharvi/COMET-poly-base-wmt25"))3data =[4{5"src":"Iceberg lettuce got its name in the 1920s when it was shipped packed in ice to stay fresh.",6"mt":"Eisbergsalat erhielt seinen Namen in den 1920er-Jahren, als er in Eis verpackt verschickt wurde, um frisch zu bleiben.",7},8{9"src":"Goats have rectangular pupils, which give them a wide field of vision—up to 320 degrees!",10"mt":"Kozy mají obdélníkové zornice, což jim umožňuje vidět skoro všude kolem sebe, aniž by musely otáčet hlavou.",11},12{13"src":"This helps them spot predators from almost all directions without moving their heads.",14"mt":"Điều này giúp chúng phát hiện kẻ săn mồi từ gần như mọi hướng mà không cần quay đầu.",15}16]17print("scores", model.predict(data, batch_size=8, gpus=1).scores)
@inproceedings{zufle-etal-2025-comet,
title = "{COMET}-poly: Machine Translation Metric Grounded in Other Candidates",
author = {Z{\"u}fle, Maike and
Zouhar, Vil{\'e}m and
Dinh, Tu Anh and
Maia Polo, Felipe and
Niehues, Jan and
Sachan, Mrinmaya},
editor = "Haddow, Barry and
Kocmi, Tom and
Koehn, Philipp and
Monz, Christof",
booktitle = "Proceedings of the Tenth Conference on Machine Translation",
month = nov,
year = "2025",
address = "Suzhou, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.wmt-1.63/",
doi = "10.18653/v1/2025.wmt-1.63",
pages = "887--904",
ISBN = "979-8-89176-341-8",
abstract = "Automated metrics for machine translation attempt to replicate human judgment. Unlike humans, who often assess a translation in the context of multiple alternatives, these metrics typically consider only the source sentence and a single translation. This discrepancy in the evaluation setup may negatively impact the performance of automated metrics. We propose two automated metrics that incorporate additional information beyond the single translation. COMET-polycand uses alternative translations of the same source sentence to compare and contrast with the translation at hand, thereby providing a more informed assessment of its quality. COMET-polyic, inspired by retrieval-based in-context learning, takes in translations of similar source texts along with their human-labeled quality scores to guide the evaluation. We find that including a single additional translation in COMET-polycand improves the segment-level metric performance (0.079 to 0.118 Kendall{'}s tau-b correlation), with further gains when more translations are added. Incorporating retrieved examples in COMET-polyic yields similar improvements (0.079 to 0.116 Kendall{'}s tau-b correlation). We release our models publicly."
}
