Unbabel/wmt23-cometkiwi-da-xl

This model is similar to the Unbabel/wmt22-cometkiwi-da but using XLM-R XL. Thus, this model has 3.5 billion parameters and requires a minimum of 15GB of GPU memory.

Paper

Scaling up CometKiwi: Unbabel-IST 2023 Submission for the Quality Estimation Shared Task

License:

cc-by-nc-sa-4.0

Usage (unbabel-comet)

Best used with unbabel-comet (>=2.1.0) to be installed:

pip install --upgrade pip  # ensures that pip is current 
pip install "unbabel-comet>=2.1.0"

Then you can use it through comet CLI:

comet-score -s {source-input}.txt -t {translation-output}.txt --model Unbabel/wmt23-cometkiwi-da-xl

Or using Python:

from comet import download_model, load_from_checkpoint

model_path = download_model("Unbabel/wmt23-cometkiwi-da-xl")
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 between 0 and 1 where 1 represents a perfect translation and 0 a random translation.

Languages Covered:

This model builds on top of XLM-R XL 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!