This is a
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
License:
cc-by-nc-sa-4.0
Usage for Inference Endpoint
1import json
2import requests
3
4API_URL = ""
5API_TOKEN="MY_API_KEY"
6headers = {
7 "Authorization": f"Bearer {API_TOKEN}",
8 "Content-Type": "application/json",
9}
10
11def query(url, headers, payload):
12 data = json.dumps(payload)
13 response = requests.request("POST", url, headers=headers, data=data)
14 return json.loads(response.content.decode("utf-8"))
15
16payload = {
17 "inputs": {
18 "batch_size": 8,
19 "workers": None,
20 "data": [
21 {
22 "src": "Youll be picking fruit and generally helping us do all the usual farm work",
23 "mt": "당신은 과일을 따기도 하고 대체로 우리가 하는 일상적인 농장 일을 돕게 될 겁니다",
24 },{
25 "src": "Youll be picking fruit and generally helping us do all the usual farm work",
26 "mt": "당신은 과일을 따기도 하고 대체로 우리가 하는 일상적인 농장 일을 돕게 될 겁니다",
27 },{
28 "src": "Youll be picking fruit and generally helping us do all the usual farm work",
29 "mt": "당신은 과일을 따기도 하고 대체로 우리가 하는 일상적인 농장 일을 돕게 될 겁니다",
30 },{
31 "src": "Youll be picking fruit and generally helping us do all the usual farm work",
32 "mt": "당신은 과일을 따기도 하고 대체로 우리가 하는 일상적인 농장 일을 돕게 될 겁니다",
33 },{
34 "src": "Youll be picking fruit and generally helping us do all the usual farm work",
35 "mt": "당신은 과일을 따기도 하고 대체로 우리가 하는 일상적인 농장 일을 돕게 될 겁니다",
36 },{
37 "src": "Youll be picking fruit and generally helping us do all the usual farm work",
38 "mt": "당신은 과일을 따기도 하고 대체로 우리가 하는 일상적인 농장 일을 돕게 될 겁니다",
39 },{
40 "src": "Youll be picking fruit and generally helping us do all the usual farm work",
41 "mt": "당신은 과일을 따기도 하고 대체로 우리가 하는 일상적인 농장 일을 돕게 될 겁니다",
42 },
43 ]
44 }
45}
46
47scores = 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!
