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Unbabel/wmt20-comet-da
wmt20-comet-da is a translation model from Unbabel. Use it when you need text moved from one language to another. The card lists the license as apache-2.0.
This is a COMET evaluation model: It receives a triplet with (source sentence, translation, reference translation) and returns a score that reflects the quality of the translation compared to both source and reference.
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Updated Jan 3, 2025
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From the Hugging Face model README
This is a COMET evaluation model: It receives a triplet with (source sentence, translation, reference translation) and returns a score that reflects the quality of the translation compared to both source and reference.
NOTE: This model was recently replaced by an improved version wmt22-comet-da
Unbabel’s Participation in the WMT20 Metrics Shared Task (Rei et al., WMT 2020)
Apache-2.0
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-inputs}.txt -t {translation-outputs}.txt -r {references}.txt --model Unbabel/wmt22-comet-da
Or using Python:
from comet import download_model, load_from_checkpoint
model_path = download_model("Unbabel/wmt20-comet-da")
model = load_from_checkpoint(model_path)
data = [
{
"src": "Dem Feuer konnte Einhalt geboten werden",
"mt": "The fire could be stopped",
"ref": "They were able to control the fire."
},
{
"src": "Schulen und Kindergärten wurden eröffnet.",
"mt": "Schools and kindergartens were open",
"ref": "Schools and kindergartens opened"
}
]
model_output = model.predict(data, batch_size=8, gpus=1)
print (model_output)
Our model is intented to be used for MT evaluation.
Given a a triplet with (source sentence, translation, reference translation) outputs a single score. This score is unbounded but typically falls between -1 and 1 where 1 reflects a perfect translation.
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!