---
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-base-wmt25

This model is based on [COMET-poly](https://github.com/zouharvi/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:
```bash
pip install "git+https://github.com/zouharvi/COMET-poly#egg=comet-poly&subdirectory=comet_poly"
```
or in editable mode:
```bash
git clone https://github.com/zouharvi/COMET-poly.git
cd COMET-poly
pip3 install -e comet_poly
```

This model scores the translation `mt` given its source. It is a baseline model that other COMET-poly models are compared to.
```python
import comet_poly
model = comet_poly.load_from_checkpoint(comet_poly.download_model("zouharvi/COMET-poly-base-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.",
    },
    {
        "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.",
    }
]
print("scores", model.predict(data, batch_size=8, gpus=1).scores)
```
Outputs:
```
scores [94.98790740966797, 77.56731414794922, 90.77655029296875]
```

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 [COMET-poly: Machine Translation Metric Grounded in Other Candidates](https://aclanthology.org/2025.wmt-1.63/) which can be cited as:
```
@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."
}
```