Acknowledge license to accept the repository

This repository is publicly accessible, but you have to accept the conditions to access its files and content.

Log in or Sign Up to review the conditions and access this model content.

Baseline for subtask 2 (word-level) from WMT24 Quality Estimation shared task. This baseline follows the approach described in Unbabel-IST submission from 2022: CometKiwi (Rei et al., WMT 2022)

License:

cc-by-nc-sa-4.0

Usage (unbabel-comet)

Using this model requires installing an older fork of unbabel-comet:

git clone https://github.com/ricardorei/wmt22-comet-legacy
poetry install

Make sure you acknowledge its License and Log in into Hugging face hub before using:

huggingface-cli login
# or using an environment variable
huggingface-cli login --token $HUGGINGFACE_TOKEN

Running the model:

from comet import download_model, load_from_checkpoint

model_path = download_model("Unbabel/wmt22-cometkiwi-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 and word-level OK/BAD tags.

The model is a baseline for a shared task and it should only be used for research purposes.

Languages Covered:

This model builds on top of InfoXLM 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!

Downloads last month

-

Downloads are not tracked for this model. How to track
Inference API
Unable to determine this model’s pipeline type. Check the docs .