README.md

metadata

pipeline_tag: translation
library_name: comet
language:
  - multilingual
  - af
  - am
  - ar
  - as
  - az
  - be
  - bg
  - bn
  - br
  - bs
  - ca
  - cs
  - cy
  - da
  - de
  - el
  - en
  - eo
  - es
  - et
  - eu
  - fa
  - fi
  - fr
  - fy
  - ga
  - gd
  - gl
  - gu
  - ha
  - he
  - hi
  - hr
  - hu
  - hy
  - id
  - is
  - it
  - ja
  - jv
  - ka
  - kk
  - km
  - kn
  - ko
  - ku
  - ky
  - la
  - lo
  - lt
  - lv
  - mg
  - mk
  - ml
  - mn
  - mr
  - ms
  - my
  - ne
  - nl
  - 'no'
  - om
  - or
  - pa
  - pl
  - ps
  - pt
  - ro
  - ru
  - sa
  - sd
  - si
  - sk
  - sl
  - so
  - sq
  - sr
  - su
  - sv
  - sw
  - ta
  - te
  - th
  - tl
  - tr
  - ug
  - uk
  - ur
  - uz
  - vi
  - xh
  - yi
  - zh
license: apache-2.0
base_model:
  - FacebookAI/xlm-roberta-large

COMET-poly-ic1-wmt25

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:

pip install "git+https://github.com/zouharvi/COMET-poly#egg=comet-poly&subdirectory=comet_poly"

or in editable mode:

git clone https://github.com/zouharvi/COMET-poly.git
cd COMET-poly
pip3 install -e comet_poly

This model scores the translation mt but takes additional in-context example: source src2, translation mt2, and score score2, which makes it a better quality estimator:

import comet_poly
model = comet_poly.load_from_checkpoint(comet_poly.download_model("zouharvi/COMET-poly-ic1-wmt25"))
data = [
    {
        "src": "Iceberg lettuce got its name in the 1920s when it was shipped packed in ice to stay fresh.",
        "mt": "Eisbergsalat erhielt seinen Namen in den 1920er-Jahren, als er in Eis verpackt verschickt wurde, um frisch zu bleiben.",
        "src2": "Lettuce is mostly water, which helps keep it crisp when chilled.",
        "mt2": "Kopfsalat besteht größtenteils aus Wasser, was ihm hilft, beim Kühlen knackig zu bleiben.",
        "score2": 94.5
    },
    {
        "src": "Goats have rectangular pupils, which give them a wide field of vision—up to 320 degrees!",
        "mt": "Kozy mají obdélníkové zornice, což jim umožňuje vidět skoro všude kolem sebe, aniž by musely otáčet hlavou.",
        "src2": "Sheep, like goats, also have rectangular pupils for better peripheral vision.",
        "mt2": "Вівці, як і кози, також мають прямокутні зіниці для кращого периферичного зору.",
        "score2": 96.0
    },
    {
        "src": "This helps them spot predators from almost all directions without moving their heads.",
        "mt": "Điều này giúp chúng phát hiện kẻ săn mồi từ gần như mọi hướng mà không cần quay đầu.",
        "src2": "Many prey animals have evolved to detect threats with minimal movement.",
        "mt2": "Nhiều động vật thịt có tiến hóa để xem mối nguy bằng nhỏ đi lại.",
        "score2": 42.3
    }
]
print("scores", model.predict(data, batch_size=8, gpus=1).scores)

Outputs:

scores [98.09857940673828, 85.52458953857422, 83.38972473144531]

You can use a readily-available training data to do the on-the-fly retrieval. Specifically, this model has been trained with retrieval based on src:

import datasets
import comet_poly.retrieval
data = [
    {
        "src": "Iceberg lettuce got its name in the 1920s when it was shipped packed in ice to stay fresh.",
        "mt": "Eisbergsalat erhielt seinen Namen in den 1920er-Jahren, als er in Eis verpackt verschickt wurde, um frisch zu bleiben.",
    },
    {
        "src": "Goats have rectangular pupils, which give them a wide field of vision—up to 320 degrees!",
        "mt": "Kozy mají obdélníkové zornice, což jim umožňuje vidět skoro všude kolem sebe, aniž by musely otáčet hlavou.",
    },
    {
        "src": "This helps them spot predators from almost all directions without moving their heads.",
        "mt": "Điều này giúp chúng phát hiện kẻ săn mồi từ gần như mọi hướng mà không cần quay đầu.",
    }
]

data_kb = list(datasets.load_dataset("zouharvi/wmt-human-all", split="train"))
data_retrieved = comet_poly.retrieval.retrieve_from_kb(
    data=data,
    data_kb=data_kb,
    k=1, # this model takes one in-context example
    prevent_hardmatch=False,
    key="src",
)
# add the retrieved data
for line, lines_retrieved in zip(data, data_retrieved):
    for i in range(len(lines_retrieved)):
        line[f"src{i+2}"] = lines_retrieved[i]["src"]
        line[f"mt{i+2}"] = lines_retrieved[i]["mt"]
        line[f"score{i+2}"] = lines_retrieved[i]["score"]

print("scores", model.predict(data, batch_size=8, gpus=1).scores)

The training data is WMT up to 2024 (inclusive) with DA/ESA/MQM merged on a single scale. This model is based on the work TODO which can be cited as:

@misc{zuefle2025comet,
    title={COMET-poly: Machine Translation Metric Grounded in Other Candidates},
    author={Maike Züfle, Vilém Zouhar, Tu Anh Dinh, Felipe Polo, Jan Niehues, Mrinmaya Sachan},
    year={2025},
}