
This is a [COMET](https://github.com/Unbabel/COMET) evaluation model: It receives a triplet with (source sentence, translation, reference translation) and returns a score that reflects the quality of the translation compared to both source and reference. 

This model was developed using [UniTE](https://aclanthology.org/2022.acl-long.558/) architecture but using the same data and hyperparameters from [Unbabel/wmt22-comet-da](https://huggingface.co/Unbabel/wmt22-comet-da). 

We build this model for our paper: [The Inside Story](https://arxiv.org/pdf/2305.11806.pdf) (Rei et al., ACL 2023)

# Paper

- [The Inside Story: Towards Better Understanding of Machine Translation Neural Evaluation Metrics](https://arxiv.org/pdf/2305.11806.pdf) (Rei et al., ACL 2023)
- [UniTE: Unified Translation Evaluation](https://aclanthology.org/2022.acl-long.558/) (Wan et al., ACL 2022)
- [COMET-22: Unbabel-IST 2022 Submission for the Metrics Shared Task](https://aclanthology.org/2022.wmt-1.52) (Rei et al., WMT 2022)

# License

cc-by-nc-sa-4.0

# 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/wmt22-comet-da
```

Or using Python:

```python
from comet import download_model, load_from_checkpoint

model_path = download_model("Unbabel/wmt22-unite-da")
model = load_from_checkpoint(model_path)
data = [
    {
        "src": "Dem Feuer konnte Einhalt geboten werden",
        "mt": "The fire could be stopped",
        "ref": "They were able to control the fire."
    },
    {
        "src": "Schulen und Kindergärten wurden eröffnet.",
        "mt": "Schools and kindergartens were open",
        "ref": "Schools and kindergartens opened"
    }
]
model_output = model.predict(data, batch_size=8, gpus=1)
print (model_output)
```

# 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": "cc-by-nc-sa-4.0", "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 arxiv:2305.11806 license:cc-by-nc-sa-4.0 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 #arxiv-2305.11806 #license-cc-by-nc-sa-4.0 #region-us 


This is a COMET evaluation model: It receives a triplet with (source sentence, translation, reference translation) and returns a score that reflects the quality of the translation compared to both source and reference. 

This model was developed using UniTE architecture but using the same data and hyperparameters from Unbabel/wmt22-comet-da. 

We build this model for our paper: The Inside Story (Rei et al., ACL 2023)

# Paper

- The Inside Story: Towards Better Understanding of Machine Translation Neural Evaluation Metrics (Rei et al., ACL 2023)
- UniTE: Unified Translation Evaluation (Wan et al., ACL 2022)
- COMET-22: Unbabel-IST 2022 Submission for the Metrics Shared Task (Rei et al., WMT 2022)

# License

cc-by-nc-sa-4.0

# Usage (unbabel-comet)

Using this model requires unbabel-comet 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

- The Inside Story: Towards Better Understanding of Machine Translation Neural Evaluation Metrics (Rei et al., ACL 2023)
- UniTE: Unified Translation Evaluation (Wan et al., ACL 2022)
- COMET-22: Unbabel-IST 2022 Submission for the Metrics Shared Task (Rei et al., WMT 2022) # License

cc-by-nc-sa-4.0 # Usage (unbabel-comet)

Using this model requires unbabel-comet 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 #arxiv-2305.11806 #license-cc-by-nc-sa-4.0 #region-us 
 # Paper

- The Inside Story: Towards Better Understanding of Machine Translation Neural Evaluation Metrics (Rei et al., ACL 2023)
- UniTE: Unified Translation Evaluation (Wan et al., ACL 2022)
- COMET-22: Unbabel-IST 2022 Submission for the Metrics Shared Task (Rei et al., WMT 2022) # License

cc-by-nc-sa-4.0 # Usage (unbabel-comet)

Using this model requires unbabel-comet 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!