Text Language Identification is the process of predicting the language of a given a piece of text. You might have encountered it when Chrome shows a popup to translate a webpage when it detects that the content is not in English. Behind the scenes, Chrome is using a model to predict the language of text used on a webpage.
When working with a dataset for NLP, the corpus may contain a mixed set of languages. Here, language identification can be useful to either filter out a few languages or to translate the corpus to a single language and then use for your downstream tasks.
In this post, I will demonstrate how to use the Fasttext library for language identification.
Facebook’s Fasttext library
Fasttext is an open-source library in Python for word embeddings and text classification. It is built for production use rather than research and hence is optimized for performance and size. It extends the Word2Vec model with ideas such as using subword information and model compression.
For our purpose of language identification, we can use the pre-trained models provided by fastText. The model was trained on a dataset drawn from Wikipedia, Tatoeba, and SETimes. The basic idea is to prepare a training data of (text, language) pairs and then train a classifier on it.
The benchmark shows that the pre-trained models are better than langid.py, another popular language identification tool. Fasttext has better accuracy and also the inference time is very fast. It supports a wide variety of languages including French, German, English, Spanish, Chinese.
- Install the
Fasttextlibrary using pip.
pip install fasttext
- There are two versions of the pre-trained models. Choose the model which fits your memory and space requirements:
- lid.176.bin: faster and slightly more accurate but 126MB in size
lid.176.ftz: a compressed version of the model, with a file size of 917kB
Download the pre-trained model from Fasttext to some location. You’ll need to specify this location later in the code. In our example, we download it to the /tmp directory.
wget -O /tmp/lid.176.bin https://dl.fbaipublicfiles.com/fasttext/supervised-models/lid.176.bin
- Now, we import fasttext and then load the model from the pretrained path we downloaded earlier.
import fasttext PRETRAINED_MODEL_PATH = '/tmp/lid.176.bin' model = fasttext.load_model(PRETRAINED_MODEL_PATH)
- Let’s take an example sentence in French which means ‘I eat food’. To detect it’s language, just pass a list of sentences to the predict function. The sentences should be in the UTF-8 format.
sentences = ['je mange de la nourriture'] predictions = model.predict(sentences) print(predictions) # ([['__label__fr']], array([[0.96568173]]))
The model returns back two tuples back. One of them is an array of language labels and the other is the confidence for each sentence. Here ‘fr’ is the ISO 639 code for French. The model is 96.56% confident that the language is French.
Fasttext returns the ISO code for the most probable one among the 170 languages. You can refer to the page on ISO 639 codes to find language for each symbol.
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 ceb ckb 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
- To programmatically convert language symbols back to the language name, you can use pycountry package. Install the package using pip.
pip install pycountry
- Now, pass the symbol to pycountry and you will get back the language name.
from pycountry import languages language_name = languages.get(alpha_2='fr').name print(language_name) # french