We recommend the two following models to evaluate your translations: Thus, results for language pairs containing uncovered languages are unreliable! COMET Models: predict( data, batch_size = 8, gpus = 1) Languages Covered:Īll the above mentioned models are build on top of XLM-R which cover the following languages:Īfrikaans, 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. "ref": "Schools and kindergartens opened"
"mt": "Schools and kindergartens were open", "src": "Schulen und Kindergärten wurden eröffnet.", "ref": "They were able to control the fire." "src": "Dem Feuer konnte Einhalt geboten werden",
Model = load_from_checkpoint( model_path) From comet import download_model, load_from_checkpoint model_path = download_model( "wmt20-comet-da")
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