I was trying to find solutions to gracefully handle Non-English based input to one of our deep learning-based NLP model which was trained only on English samples. Non-English words are out of vocabulary to the model, it wasn’t handling it well. Even though we wanted to make the model multi-lingual ( more on it in future posts) in the future, stumbling upon Fast text’s pre-trained language detection model was a pleasant surprise and made us consider it as an interim solution. So wanted to write a short post on it.
As a pre-requisite install the fastText library.
$ git clone https://github.com/facebookresearch/fastText.git
$ cd fastText
$ pip install
Download the pre-trained model from here. The compressed version of the model is just a little shy of 1MB and supports 176 languages. Which is an amazing work by Fast text team.
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Load the model in memory using the fastText library. Make sure the inputs are encoded in UTF-8. Model supports only UTF-8 as it was trained only on UTF-8 samples.
sys.setdefaultencoding('UTF8') # default encoding to utf-8
lid_model = fastText.load_model("lid.176.ftz")
prediction using the loaded model.
lid_model.predict("மதியும் மடந்தை முகனு மறியா பதியிற் கலங்கிய மீன்.")
#output - ((u'__label__ta',), array([0.99988115]))
# __label__ta - tamil
lid_model.predict("Incapaz de distinguir la luna y la cara de esta chica,Las estrellas se ponen nerviosas en el cielo.")
#output - ((u'__label__es',), array([0.93954092]))
# __label__es - spanish
lid_model.predict("Unable to tell apart the moon and this girl’s face,Stars are flustered up in the sky.")
#output - ((u'__label__en',), array([0.93129086]))
#__label__en - english
The output is a tuple of language label and prediction confidence. Language label is a string with “__lable__” followed by ISO 639 code of the language. Full code is here.