fasttext-langdetect

This library is a wrapper for the language detection model trained on fasttext by Facebook. For more information, please visit: https://fasttext.cc/docs/en/language-identification.html. It's a fork of zafercavdar/fasttext-langdetect and makes the code async while also fixing some bugs.

Supported languages

af als am an ar arz as ast av az azb ba bar bcl be bg bh bn bo bpy br bs bxr ca cbk ce cebckb co cs cv cy da de diq dsb dty dv el eml en eo es et eu fa fi fr frr fy ga gd gl gn gom gu gv he hi hif hr hsb ht hu hy ia id ie ilo io is it ja jbo jv ka kk km kn ko krc ku kv kw ky la lb lez li lmo lo lrc lt lv mai mg mhr min mk ml mn mr mrj ms mt mwl my myv mzn nah nap nds ne new nl nn no oc or os pa pam pfl pl pms pnb ps pt qu rm ro ru rue sa sah sc scn sco sd sh si sk sl so sq sr su sv sw ta te tg th tk tl tr tt tyv ug uk ur uz vec vep vi vls vo wa war wuu xal xmf yi yo yue zh

Install

pip install fasttext-langdetect

Usage

detect method expects UTF-8 data. low_memory option enables getting predictions with the compressed version of the fasttext model by sacrificing the accuracy a bit.

from ftlangdetect import detect

result = detect(text="Bugün hava çok güzel", low_memory=False)
print(result)
> {'lang': 'tr', 'score': 1.00}

result = detect(text="Bugün hava çok güzel", low_memory=True)
print(result)
> {'lang': 'tr', 'score': 0.9982126951217651}

Benchmark

We benchmarked the fasttext model against cld2, langid, and langdetect on Wili-2018 dataset.

	fasttext	langid	langdetect	cld2
Average time (ms)	0,158273381	1,726618705	12,44604317	0,028776978
139 langs - not weighted	76,8	61,6	37,6	80,8
139 langs - pop weighted	95,5	93,1	86,6	92,7
44 langs - not weighted	93,3	89,2	81,6	91,5
44 langs - pop weighted	96,6	94,8	89,4	93,4

• pop weighted means recall for each language is multipled by its number of speakers.
• 139 languages = all languages with ISO 639-1 2-letter code
• 44 languages = top 44 languages spoken in the world

Recall per language

lang	cld2	fasttext	langdetect	langid
Afrikaans	0,94	0,918	0,992	0,966
Albanian	0,958	0,966	0,964	0,954
Amharic	0,976	0,982	0	0,982
Arabic	0,994	0,998	0,998	0,996
Aragonese	0	0,43	0	0,788
Armenian	0,966	0,972	0	0,968
Assamese	0,946	0,956	0	0,14
Avar	0	0,626	0	0
Aymara	0,596	0	0	0
Azerbaijani	0,97	0,988	0	0,984
Bashkir	0,97	0,97	0	0
Basque	0,978	0,99	0	0,962
Belarusian	0,94	0,97	0	0,964
Bengali	0,898	0,922	0,904	0,942
Bhojpuri	0,716	0,15	0	0
Bokmål	0,852	0,966	0,976	0,95
Bosnian	0,422	0,108	0	0,054
Breton	0,946	0,974	0	0,976
Bulgarian	0,892	0,964	0,964	0,942
Burmese	0,998	0,998	0	0
Catalan	0,882	0,95	0,93	0,928
Central Khmer	0,876	0,878	0	0,876
Chechen	0	0,99	0	0
Chuvash	0	0,96	0	0
Cornish	0	0,792	0	0
Corsican	0,88	0,016	0	0
Croatian	0,688	0,806	0,982	0,932
Czech	0,978	0,986	0,984	0,982
Danish	0,886	0,958	0,95	0,896
Dhivehi	0,996	0,998	0	0
Dutch	0,9	0,978	0,968	0,97
English	0,992	1	0,998	0,986
Esperanto	0,936	0,978	0	0,948
Estonian	0,918	0,952	0,948	0,932
Faroese	0,912	0	0	0,618
Finnish	0,988	0,998	0,998	0,994
French	0,946	0,996	0,99	0,992
Galician	0,89	0,912	0	0,93
Georgian	0,974	0,976	0	0,976
German	0,958	0,984	0,978	0,978
Guarani	0,968	0,728	0	0
Gujarati	0,932	0,932	0,93	0,932
Haitian Creole	0,988	0,536	0	0,99
Hausa	0,976	0	0	0
Hebrew	0,994	0,996	0,998	0,998
Hindi	0,982	0,984	0,982	0,972
Hungarian	0,96	0,988	0,968	0,986
Icelandic	0,984	0,996	0	0,996
Ido	0	0,76	0	0
Igbo	0,798	0	0	0
Indonesian	0,88	0,946	0,958	0,836
Interlingua	0,27	0,688	0	0
Interlingue	0,198	0,192	0	0
Irish	0,968	0,978	0	0,984
Italian	0,866	0,948	0,932	0,936
Japanese	0,97	0,986	0,98	0,986
Javanese	0	0,864	0	0,938
Kannada	0,998	0,998	0,998	0,998
Kazakh	0,978	0,992	0	0,916
Kinyarwanda	0,86	0	0	0,44
Kirghiz	0,974	0,99	0	0,408
Komi	0	0,544	0	0
Korean	0,986	0,99	0,988	0,99
Kurdish	0	0,972	0	0,976
Lao	0,84	0,842	0	0,85
Latin	0,778	0,864	0	0,854
Latvian	0,98	0,992	0,992	0,99
Limburgan	0	0,324	0	0
Lingala	0,85	0	0	0
Lithuanian	0,96	0,976	0,974	0,97
Luganda	0,952	0	0	0
Luxembourgish	0,864	0,894	0	0,93
Macedonian	0,88	0,984	0,982	0,974
Malagasy	0,99	0,99	0	0,988
Malay	0,896	0,586	0	0,39
Malayalam	0,988	0,988	0,988	0,988
Maltese	0,962	0,966	0	0,964
Manx	0,972	0,294	0	0
Maori	0,994	0	0	0
Marathi	0,958	0,966	0,964	0,942
Modern Greek	0,99	0,992	0,99	0,992
Mongolian	0,964	0,994	0	0,996
Navajo	0	0	0	0
Nepali (macrolanguage)	0,96	0,98	0,978	0,922
Northern Sami	0	0	0	0,866
Norwegian Nynorsk	0,94	0,79	0	0,796
Occitan	0,66	0,48	0	0,724
Oriya	0,96	0,958	0	0,96
Oromo	0,956	0	0	0
Ossetian	0	0,938	0	0
Panjabi	0,994	0,994	0,994	0,994
Persian	0,992	0,998	0,996	0,998
Polish	0,982	0,998	0,998	0,992
Portuguese	0,908	0,956	0,946	0,952
Pushto	0,938	0,922	0	0,754
Quechua	0,926	0,808	0	0,852
Romanian	0,932	0,986	0,984	0,984
Romansh	0,934	0,328	0	0
Russian	0,728	0,986	0,984	0,988
Sanskrit	0,964	0,976	0	0
Sardinian	0	0,01	0	0
Scottish Gaelic	0,964	0,942	0	0
Serbian	0,942	0,946	0	0,902
Serbo-Croatian	0	0,402	0	0
Shona	0,844	0	0	0
Sindhi	0,978	0,982	0	0
Sinhala	0,962	0,962	0	0,962
Slovak	0,964	0,974	0,982	0,97
Slovene	0,876	0,966	0,968	0,946
Somali	0,924	0,696	0,956	0
Spanish	0,894	0,986	0,976	0,98
Standard Chinese	0,946	0,984	0,746	0,978
Sundanese	0,91	0,854	0	0
Swahili (macrolanguage)	0,924	0,92	0,938	0,934
Swedish	0,872	0,994	0,992	0,986
Tagalog	0,928	0,972	0,974	0,964
Tajik	0,82	0,85	0	0
Tamil	0,992	0,992	0,992	0,994
Tatar	0,978	0,984	0	0
Telugu	0,958	0,958	0,958	0,96
Thai	0,988	0,988	0,988	0,988
Tibetan	0,986	0,992	0	0
Tongan	0,968	0	0	0
Tswana	0,928	0	0	0
Turkish	0,968	0,986	0,982	0,976
Turkmen	0,94	0,936	0	0
Uighur	0,978	0,986	0	0,964
Ukrainian	0,97	0,988	0,986	0,986
Urdu	0,86	0,958	0,89	0,896
Uzbek	0,984	0,99	0	0
Vietnamese	0,978	0,986	0,984	0,984
Volapük	0,994	0,982	0	0,986
Walloon	0	0,664	0	0,98
Welsh	0,98	0,992	0,992	0,984
Western Frisian	0,888	0,956	0	0
Wolof	0,926	0	0	0
Xhosa	0,928	0	0	0,912
Yiddish	0,956	0,958	0	0
Yoruba	0,75	0,262	0	0

Star History

References

[1] A. Joulin, E. Grave, P. Bojanowski, T. Mikolov, Bag of Tricks for Efficient Text Classification

@article{joulin2016bag,
  title={Bag of Tricks for Efficient Text Classification},
  author={Joulin, Armand and Grave, Edouard and Bojanowski, Piotr and Mikolov, Tomas},
  journal={arXiv preprint arXiv:1607.01759},
  year={2016}
}

[2] A. Joulin, E. Grave, P. Bojanowski, M. Douze, H. Jégou, T. Mikolov, FastText.zip: Compressing text classification models

@article{joulin2016fasttext,
  title={FastText.zip: Compressing text classification models},
  author={Joulin, Armand and Grave, Edouard and Bojanowski, Piotr and Douze, Matthijs and J{\'e}gou, H{\'e}rve and Mikolov, Tomas},
  journal={arXiv preprint arXiv:1612.03651},
  year={2016}
}