People Involved
- LABRAK Yanis (1)
Affiliations
- LIA, NLP team, Avignon University, Avignon, France.
Model
XLM-Roberta : https://huggingface.co/xlm-roberta-base
Paper : Unsupervised Cross-lingual Representation Learning at Scale
Demo: How to use in HuggingFace Transformers Pipeline
Requires transformers: pip install transformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification, TextClassificationPipeline
model_name = 'qanastek/51-languages-classifier'
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
classifier = TextClassificationPipeline(model=model, tokenizer=tokenizer)
res = classifier("פרק הבא בפודקאסט בבקשה")
print(res)
Outputs:
[{'label': 'he-IL', 'score': 0.9998375177383423}]
Training data
MASSIVE is a parallel dataset of > 1M utterances across 51 languages with annotations for the Natural Language Understanding tasks of intent prediction and slot annotation. Utterances span 60 intents and include 55 slot types. MASSIVE was created by localizing the SLURP dataset, composed of general Intelligent Voice Assistant single-shot interactions.
Languages
Thee model is capable of distinguish 51 languages :
Afrikaans - South Africa (af-ZA)Amharic - Ethiopia (am-ET)Arabic - Saudi Arabia (ar-SA)Azeri - Azerbaijan (az-AZ)Bengali - Bangladesh (bn-BD)Chinese - China (zh-CN)Chinese - Taiwan (zh-TW)Danish - Denmark (da-DK)German - Germany (de-DE)Greek - Greece (el-GR)English - United States (en-US)Spanish - Spain (es-ES)Farsi - Iran (fa-IR)Finnish - Finland (fi-FI)French - France (fr-FR)Hebrew - Israel (he-IL)Hungarian - Hungary (hu-HU)Armenian - Armenia (hy-AM)Indonesian - Indonesia (id-ID)Icelandic - Iceland (is-IS)Italian - Italy (it-IT)Japanese - Japan (ja-JP)Javanese - Indonesia (jv-ID)Georgian - Georgia (ka-GE)Khmer - Cambodia (km-KH)Korean - Korea (ko-KR)Latvian - Latvia (lv-LV)Mongolian - Mongolia (mn-MN)Malay - Malaysia (ms-MY)Burmese - Myanmar (my-MM)Norwegian - Norway (nb-NO)Dutch - Netherlands (nl-NL)Polish - Poland (pl-PL)Portuguese - Portugal (pt-PT)Romanian - Romania (ro-RO)Russian - Russia (ru-RU)Slovanian - Slovania (sl-SL)Albanian - Albania (sq-AL)Swedish - Sweden (sv-SE)Swahili - Kenya (sw-KE)Hindi - India (hi-IN)Kannada - India (kn-IN)Malayalam - India (ml-IN)Tamil - India (ta-IN)Telugu - India (te-IN)Thai - Thailand (th-TH)Tagalog - Philippines (tl-PH)Turkish - Turkey (tr-TR)Urdu - Pakistan (ur-PK)Vietnamese - Vietnam (vi-VN)Welsh - United Kingdom (cy-GB)
Evaluation results
precision recall f1-score support
af-ZA 0.9821 0.9805 0.9813 2974
am-ET 1.0000 1.0000 1.0000 2974
ar-SA 0.9809 0.9822 0.9815 2974
az-AZ 0.9946 0.9845 0.9895 2974
bn-BD 0.9997 0.9990 0.9993 2974
cy-GB 0.9970 0.9929 0.9949 2974
da-DK 0.9575 0.9617 0.9596 2974
de-DE 0.9906 0.9909 0.9908 2974
el-GR 0.9997 0.9973 0.9985 2974
en-US 0.9712 0.9866 0.9788 2974
es-ES 0.9825 0.9842 0.9834 2974
fa-IR 0.9940 0.9973 0.9956 2974
fi-FI 0.9943 0.9946 0.9945 2974
fr-FR 0.9963 0.9923 0.9943 2974
he-IL 1.0000 0.9997 0.9998 2974
hi-IN 1.0000 0.9980 0.9990 2974
hu-HU 0.9983 0.9950 0.9966 2974
hy-AM 1.0000 0.9993 0.9997 2974
id-ID 0.9319 0.9291 0.9305 2974
is-IS 0.9966 0.9943 0.9955 2974
it-IT 0.9698 0.9926 0.9811 2974
ja-JP 0.9987 0.9963 0.9975 2974
jv-ID 0.9628 0.9744 0.9686 2974
ka-GE 0.9993 0.9997 0.9995 2974
km-KH 0.9867 0.9963 0.9915 2974
kn-IN 1.0000 0.9993 0.9997 2974
ko-KR 0.9917 0.9997 0.9956 2974
lv-LV 0.9990 0.9950 0.9970 2974
ml-IN 0.9997 0.9997 0.9997 2974
mn-MN 0.9987 0.9966 0.9976 2974
ms-MY 0.9359 0.9418 0.9388 2974
my-MM 1.0000 0.9993 0.9997 2974
nb-NO 0.9600 0.9533 0.9566 2974
nl-NL 0.9850 0.9748 0.9799 2974
pl-PL 0.9946 0.9923 0.9934 2974
pt-PT 0.9885 0.9798 0.9841 2974
ro-RO 0.9919 0.9916 0.9918 2974
ru-RU 0.9976 0.9983 0.9980 2974
sl-SL 0.9956 0.9939 0.9948 2974
sq-AL 0.9936 0.9896 0.9916 2974
sv-SE 0.9902 0.9842 0.9872 2974
sw-KE 0.9867 0.9953 0.9910 2974
ta-IN 1.0000 1.0000 1.0000 2974
te-IN 1.0000 0.9997 0.9998 2974
th-TH 1.0000 0.9983 0.9992 2974
tl-PH 0.9929 0.9899 0.9914 2974
tr-TR 0.9869 0.9872 0.9871 2974
ur-PK 0.9983 0.9929 0.9956 2974
vi-VN 0.9993 0.9973 0.9983 2974
zh-CN 0.9812 0.9832 0.9822 2974
zh-TW 0.9832 0.9815 0.9823 2974
accuracy 0.9889 151674
macro avg 0.9889 0.9889 0.9889 151674
weighted avg 0.9889 0.9889 0.9889 151674
Keywords : language identification ; language identification ; multilingual ; classification