Text Classification With FastText

FastText is a library for efficient text classification and representation learning developed by Facebook. It’s built on top of the PyTorch deep learning framework.

Here’s an example of how you can use FastText for text classification in Python:

import fasttext

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

# Classify a single string
text = 'This is a positive review'
label, probability = model.predict(text)
print(label, probability)

# Classify a list of strings
texts = ['This is a positive review', 'This is a negative review']
labels, probabilities = model.predict(texts)
print(labels, probabilities)

You can also use FastText for training your own text classification model. Here’s an example:

import fasttext

# Load the dataset
x_train = ['This is a positive review', 'This is a negative review']
y_train = ['positive', 'negative']

# Create the model
model = fasttext.train_supervised(input=x_train, labels=y_train)

# Save the model