metadata

pipeline_tag: translation
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
tags:
  - arXiv:2010.15535
  - PyTorch

This is a COMET quality estimation model: It receives a source sentence and the respective translation and returns a score that reflects the quality of the translation.

NOTE: This model was recently replaced by an improved version wmt22-cometkiwi-da

Paper

Unbabel’s Participation in the WMT20 Metrics Shared Task (Rei et al., WMT 2020)

License

Apache-2.0

Usage (unbabel-comet)

Using this model requires unbabel-comet to be installed:

pip install --upgrade pip  # ensures that pip is current 
pip install unbabel-comet

Then you can use it through comet CLI:

comet-score -s {source-input}.txt -t {translation-output}.txt --model Unbabel/wmt20-comet-qe-da

Or using Python:

from comet import download_model, load_from_checkpoint

model_path = download_model("Unbabel/wmt20-comet-qe-da")
model = load_from_checkpoint(model_path)
data = [
    {
        "src": "The output signal provides constant sync so the display never glitches.",
        "mt": "Das Ausgangssignal bietet eine konstante Synchronisation, so dass die Anzeige nie stört."
    },
    {
        "src": "Kroužek ilustrace je určen všem milovníkům umění ve věku od 10 do 15 let.",
        "mt": "Кільце ілюстрації призначене для всіх любителів мистецтва у віці від 10 до 15 років."
    },
    {
        "src": "Mandela then became South Africa's first black president after his African National Congress party won the 1994 election.",
        "mt": "その後、1994年の選挙でアフリカ国民会議派が勝利し、南アフリカ初の黒人大統領となった。"
    }
]
model_output = model.predict(data, batch_size=8, gpus=1)
print (model_output)

Intended uses

Our model is intented to be used for reference-free MT evaluation.

Given a source text and its translation, outputs a single score that reflects the quality of the translation. The returned score is unbounded and noisy. It works well for ranking engines and translations over the same source but there is no clear interpretation for the resulting score.

Languages Covered:

This model builds on top of XLM-R which cover the following languages:

Afrikaans, Albanian, Amharic, Arabic, Armenian, Assamese, Azerbaijani, Basque, Belarusian, Bengali, Bengali Romanized, Bosnian, Breton, Bulgarian, Burmese, Burmese, Catalan, Chinese (Simplified), Chinese (Traditional), Croatian, Czech, Danish, Dutch, English, Esperanto, Estonian, Filipino, Finnish, French, Galician, Georgian, German, Greek, Gujarati, Hausa, Hebrew, Hindi, Hindi Romanized, Hungarian, Icelandic, Indonesian, Irish, Italian, Japanese, Javanese, Kannada, Kazakh, Khmer, Korean, Kurdish (Kurmanji), Kyrgyz, Lao, Latin, Latvian, Lithuanian, Macedonian, Malagasy, Malay, Malayalam, Marathi, Mongolian, Nepali, Norwegian, Oriya, Oromo, Pashto, Persian, Polish, Portuguese, Punjabi, Romanian, Russian, Sanskri, Scottish, Gaelic, Serbian, Sindhi, Sinhala, Slovak, Slovenian, Somali, Spanish, Sundanese, Swahili, Swedish, Tamil, Tamil Romanized, Telugu, Telugu Romanized, Thai, Turkish, Ukrainian, Urdu, Urdu Romanized, Uyghur, Uzbek, Vietnamese, Welsh, Western, Frisian, Xhosa, Yiddish.

Thus, results for language pairs containing uncovered languages are unreliable!