
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 (>=2.0.0) to be installed:

```bash
pip install --upgrade pip  # ensures that pip is current 
pip install "unbabel-comet>=2.0.0"
```

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 three scores that reflect the translation quality according to different inputs: 
- source score: [`mt`, `src`]
- reference score: [`mt`, `ref`]
- unified score: [`mt`, `src`, `ref`]

# 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!

{"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", "pipeline_tag": "translation"}
translation 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 has_space region:us
TAGS
#translation #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 #has_space #region-us 


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

# Paper

- UniTE: Unified Translation Evaluation (Wan et al., ACL 2022)

# Original Code

- UniTE

# License

Apache 2.0

# Usage (unbabel-comet)

Using this model requires unbabel-comet (>=2.0.0) to be installed:



Then you can use it through comet CLI:



Or using Python:



# Intended uses

Our model is intented to be used for MT evaluation. 

Given a a triplet with (source sentence, translation, reference translation) outputs three scores that reflect the translation quality according to different inputs: 
- source score: ['mt', 'src']
- reference score: ['mt', 'ref']
- unified score: ['mt', 'src', 'ref']

# 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!

# Paper

- UniTE: Unified Translation Evaluation (Wan et al., ACL 2022) # Original Code

- UniTE # License

Apache 2.0 # Usage (unbabel-comet)

Using this model requires unbabel-comet (>=2.0.0) to be installed:



Then you can use it through comet CLI:



Or using Python: # Intended uses

Our model is intented to be used for MT evaluation. 

Given a a triplet with (source sentence, translation, reference translation) outputs three scores that reflect the translation quality according to different inputs: 
- source score: ['mt', 'src']
- reference score: ['mt', 'ref']
- unified score: ['mt', 'src', 'ref'] # 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!
TAGS
#translation #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 #has_space #region-us 
 # Paper

- UniTE: Unified Translation Evaluation (Wan et al., ACL 2022) # Original Code

- UniTE # License

Apache 2.0 # Usage (unbabel-comet)

Using this model requires unbabel-comet (>=2.0.0) to be installed:



Then you can use it through comet CLI:



Or using Python: # Intended uses

Our model is intented to be used for MT evaluation. 

Given a a triplet with (source sentence, translation, reference translation) outputs three scores that reflect the translation quality according to different inputs: 
- source score: ['mt', 'src']
- reference score: ['mt', 'ref']
- unified score: ['mt', 'src', 'ref'] # 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!