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:cc-by-nc-sa-4.0 endpoints_compatible region:us
---
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
- false
- 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
extra_gated_heading: Acknowledge license to accept the repository
extra_gated_button_content: Acknowledge license
---

This is a [COMET](https://github.com/Unbabel/COMET) quality estimation model by Unbabel: It receives a source sentence and the respective translation and returns a score that reflects the quality of the translation.

# Paper

[CometKiwi: IST-Unbabel 2022 Submission for the Quality Estimation Shared Task](https://aclanthology.org/2022.wmt-1.60) (Rei et al., WMT 2022)

# License:

cc-by-nc-sa-4.0

# Usage for Inference Endpoint

```python
import json
import requests

API_URL = ""
API_TOKEN="MY_API_KEY"
headers = {
    "Authorization": f"Bearer {API_TOKEN}",
    "Content-Type": "application/json",
}

def query(url, headers, payload):
    data = json.dumps(payload)
    response = requests.request("POST", url, headers=headers, data=data)
    return json.loads(response.content.decode("utf-8"))

payload = {
    "inputs": {
        "batch_size": 8,
        "workers": None,
        "data": [
            {
                "src": "Youll be picking fruit and generally helping us do all the usual farm work",
                "mt": "당신은 과일을 따기도 하고 대체로 우리가 하는 일상적인 농장 일을 돕게 될 겁니다",
            },{
                "src": "Youll be picking fruit and generally helping us do all the usual farm work",
                "mt": "당신은 과일을 따기도 하고 대체로 우리가 하는 일상적인 농장 일을 돕게 될 겁니다",
            },{
                "src": "Youll be picking fruit and generally helping us do all the usual farm work",
                "mt": "당신은 과일을 따기도 하고 대체로 우리가 하는 일상적인 농장 일을 돕게 될 겁니다",
            },{
                "src": "Youll be picking fruit and generally helping us do all the usual farm work",
                "mt": "당신은 과일을 따기도 하고 대체로 우리가 하는 일상적인 농장 일을 돕게 될 겁니다",
            },{
                "src": "Youll be picking fruit and generally helping us do all the usual farm work",
                "mt": "당신은 과일을 따기도 하고 대체로 우리가 하는 일상적인 농장 일을 돕게 될 겁니다",
            },{
                "src": "Youll be picking fruit and generally helping us do all the usual farm work",
                "mt": "당신은 과일을 따기도 하고 대체로 우리가 하는 일상적인 농장 일을 돕게 될 겁니다",
            },{
                "src": "Youll be picking fruit and generally helping us do all the usual farm work",
                "mt": "당신은 과일을 따기도 하고 대체로 우리가 하는 일상적인 농장 일을 돕게 될 겁니다",
            },
        ]
    }
}

scores = query(API_URL, headers, payload)
```

# Intended uses

Unbabel's model is intented to be used for **reference-free MT evaluation**. 

Given a source text and its translation, outputs a single score between 0 and 1 where 1 represents a perfect translation. 

# 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!