metadata

license: mit
language:
  - fr
  - zh
  - fa
  - ky
  - ru
  - lt
  - uz
  - en
  - pt
  - bg
  - th
  - pl
  - ur
  - sw
  - tr
  - es
  - ar
  - it
  - hi
  - de
  - el
  - nl
  - vi
  - ja
pipeline_tag: text-classification
tags:
  - pytorch
  - mt0

language identification mt0

This model is a fine-tuned version of encoder from bigscience/mt0-small on the Language Identification dataset as well as some private data.

Limitations

Currently, it supports the following 20 languages:

arabic (ar), bulgarian (bg), german (de), modern greek (el), english (en), spanish (es), french (fr), hindi (hi), italian (it), kyrgyz (ky), uzbek (uz), persian (fa), lithuanian (lt), japanese (ja), dutch (nl), polish (pl), portuguese (pt), russian (ru), swahili (sw), thai (th), turkish (tr), urdu (ur), vietnamese (vi), and chinese (zh)

Inference

First you will need to have this library installed

pip install bert-for-sequence classfication

from bert_clf import EncoderCLF

model = EncoderCLF("whitefoxredhell/language_identification")

text = "London is the capital of Great Britain"

model.predict(text)
# 'en'

model.predict_proba(text)
# {
#   'fr': 3.022890814463608e-05,
#   'zh': 2.328997834410984e-05,
#   'fa': 5.344639430404641e-05,
#   'ky': 3.5296812711749226e-05,
#   'ru': 2.3277720174519345e-05,
#   'lt': 0.00021786204888485372,
#   'uz': 3.461417873040773e-05,
#   'en': 0.999232292175293,
#   'pt': 1.2590448022820055e-05,
#   'bg': 1.5775613064761274e-05,
#   'th': 9.429674719285686e-06,
#   'pl': 2.4624938305350952e-05,
#   'ur': 3.982995986007154e-05,
#   'sw': 4.8921840061666444e-05,
#   'tr': 2.6844283638638444e-05,
#   'es': 2.325668538105674e-05,
#   'ar': 2.4103366740746424e-05,
#   'it': 1.8611381165101193e-05,
#   'hi': 1.4575023669749498e-05,
#   'de': 2.210299498983659e-05,
#   'el': 1.3880739061278291e-05,
#   'nl': 2.767637124634348e-05,
#   'vi': 1.3878144272894133e-05,
#   'ja': 1.3629408385895658e-05
# }