| 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 | |
| This model was developed by the NLP2CT Lab at the University of Macau and Alibaba Group, and all credits should be attributed to these groups. Since it was developed using the COMET codebase, we adapted the code to run these models within COMET." | |
| This is equivalent to [UniTE-MUP-large] from [modelscope](https://www.modelscope.cn/models/damo/nlp_unite_mup_translation_evaluation_multilingual_large/summary) | |
| # Paper | |
| - [UniTE: Unified Translation Evaluation](https://aclanthology.org/2022.acl-long.558/) (Wan et al., ACL 2022) | |
| # Original Code | |
| - [UniTE](https://github.com/NLP2CT/UniTE) | |
| # License | |
| Apache 2.0 | |
| # Usage (unbabel-comet) | |
| Using this model requires unbabel-comet to be installed: | |
| ```bash | |
| pip install --upgrade pip # ensures that pip is current | |
| pip install unbabel-comet | |
| ``` | |
| Then you can use it through comet CLI: | |
| ```bash | |
| comet-score -s {source-inputs}.txt -t {translation-outputs}.txt -r {references}.txt --model Unbabel/unite-mup | |
| ``` | |
| Or using Python: | |
| ```python | |
| from comet import download_model, load_from_checkpoint | |
| model_path = download_model("Unbabel/unite-mup") | |
| model = load_from_checkpoint(model_path) | |
| data = [ | |
| { | |
| "src": "这是个句子。", | |
| "mt": "This is a sentence.", | |
| "ref": "It is a sentence." | |
| }, | |
| { | |
| "src": "这是另一个句子。", | |
| "mt": "This is another sentence.", | |
| "ref": "It is another sentence." | |
| } | |
| ] | |
| model_output = model.predict(data, batch_size=8, gpus=1) | |
| # Expected SRC score: | |
| # [0.3474583327770233, 0.4492775797843933] | |
| print (model_output.metadata.src_scores) | |
| # Expected REF score: | |
| # [0.9252626895904541, 0.899452269077301] | |
| print (model_output.metadata.ref_scores) | |
| # Expected UNIFIED score: | |
| # [0.8758717179298401, 0.8294666409492493] | |
| print (model_output.metadata.unified_scores) | |
| ``` | |
| # Intended uses | |
| Our model is intented to be used for **MT evaluation**. | |
| Given a a triplet with (source sentence, translation, reference translation) outputs a single score between 0 and 1 where 1 represents a perfect translation. | |
| # 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! | |