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Update README.md
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README.md
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---
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license: mit
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---
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# language identification mt0
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---
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license: mit
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language:
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- fr
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- zh
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- fa
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- ky
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- ru
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- lt
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- uz
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- en
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- pt
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- bg
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- th
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- pl
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- ur
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- sw
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- tr
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- es
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- ar
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- it
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- hi
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- de
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- el
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- nl
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- vi
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- ja
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pipeline_tag: text-classification
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---
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This model is a fine-tuned version of encoder from [bigscience/mt0-small](https://huggingface.co/bigscience/mt0-small) on the [Language Identification](https://huggingface.co/datasets/papluca/language-identification#additional-information) dataset as well as some private data.
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## Limitations
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Currently, it supports the following 20 languages:
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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)
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## Inference
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First you will need to have this library installed
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```python
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pip install bert-for-sequence classfication
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```
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```python
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from bert_clf import EncoderCLF
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model = EncoderCLF("whitefoxredhell/language_identification")
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text = "London is the capital of Great Britain"
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model.predict(text)
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# 'en'
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model.predict_proba(text)
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# {
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# 'fr': 3.022890814463608e-05,
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# 'zh': 2.328997834410984e-05,
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# 'fa': 5.344639430404641e-05,
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# 'ky': 3.5296812711749226e-05,
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# 'ru': 2.3277720174519345e-05,
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# 'lt': 0.00021786204888485372,
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# 'uz': 3.461417873040773e-05,
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# 'en': 0.999232292175293,
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# 'pt': 1.2590448022820055e-05,
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# 'bg': 1.5775613064761274e-05,
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# 'th': 9.429674719285686e-06,
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# 'pl': 2.4624938305350952e-05,
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# 'ur': 3.982995986007154e-05,
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# 'sw': 4.8921840061666444e-05,
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# 'tr': 2.6844283638638444e-05,
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# 'es': 2.325668538105674e-05,
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# 'ar': 2.4103366740746424e-05,
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# 'it': 1.8611381165101193e-05,
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# 'hi': 1.4575023669749498e-05,
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# 'de': 2.210299498983659e-05,
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# 'el': 1.3880739061278291e-05,
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# 'nl': 2.767637124634348e-05,
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# 'vi': 1.3878144272894133e-05,
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# 'ja': 1.3629408385895658e-05
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# }
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```
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