How to Use FastText Python for NLP Tasks

To use fasttext in Python for NLP, install the fasttext library, then train or load a model using fasttext.train_supervised() or fasttext.load_model(). Use the model's predict() method to classify text or get word vectors for text representation.

Syntax

The main steps to use FastText in Python are:

• import fasttext: Import the library.
• model = fasttext.train_supervised(input='data.txt'): Train a supervised model on labeled data.
• model = fasttext.load_model('model.bin'): Load a pre-trained model.
• labels, probabilities = model.predict(text): Predict labels for new text.
• vector = model.get_word_vector(word): Get vector representation of a word.

python

import fasttext

# Train a supervised model
model = fasttext.train_supervised(input='data.txt')

# Load a saved model
model = fasttext.load_model('model.bin')

# Predict label for a sentence
labels, probabilities = model.predict('example text')

# Get word vector
vector = model.get_word_vector('example')

Example

This example shows how to train a simple FastText supervised model on labeled data, save it, load it back, and predict labels for new sentences.

python

import fasttext

# Create a small training file with labels
with open('train.txt', 'w') as f:
    f.write('__label__greeting Hello world\n')
    f.write('__label__farewell Goodbye world\n')

# Train the model
model = fasttext.train_supervised(input='train.txt', epoch=5, lr=1.0)

# Save the model
model.save_model('model.bin')

# Load the model
loaded_model = fasttext.load_model('model.bin')

# Predict labels for new text
labels, probabilities = loaded_model.predict('Hello there')
print('Labels:', labels)
print('Probabilities:', probabilities)

Output

Labels: ['__label__greeting']
Probabilities: [0.99999994]

Common Pitfalls

Common mistakes when using FastText in Python include:

• Not formatting training data correctly: Each line must start with __label__labelname followed by the text.
• Using unlabeled data for supervised training causes errors.
• Forgetting to save the model after training.
• Loading a model with load_model before training or saving it.
• Confusing fasttext (the official library) with fasttext wrappers or older versions.

python

import fasttext

# Wrong: training data without labels
with open('bad_train.txt', 'w') as f:
    f.write('Hello world\n')

# This will raise an error
# model = fasttext.train_supervised(input='bad_train.txt')

# Right: training data with labels
with open('good_train.txt', 'w') as f:
    f.write('__label__greet Hello world\n')
model = fasttext.train_supervised(input='good_train.txt')

Quick Reference

FastText Python key functions:

Function	Description
fasttext.train_supervised(input, epoch=5, lr=0.1)	Train a supervised text classification model from labeled data file
fasttext.load_model(path)	Load a saved FastText model from disk
model.predict(text, k=1)	Predict top k labels for given text
model.get_word_vector(word)	Get vector representation of a word
model.save_model(path)	Save the trained model to disk

Key Takeaways

Format training data with labels starting with __label__ for supervised training.

Use fasttext.train_supervised() to train and fasttext.load_model() to load models.

Use model.predict() to classify new text and get labels with probabilities.

Save your trained model with model.save_model() to reuse later.

Get word vectors with model.get_word_vector() for text representation tasks.