FastText is a popular open-source library for efficient learning of word representations and sentence classification. Developed by Facebook’s AI Research (FAIR) team, FastText is implemented in C++ and has bindings for Python. It is designed to be highly efficient and is particularly well-suited for large-scale applications.
We will start by installing the library and then move on to some basic usage examples.
To install FastText, you will need to have the following dependencies installed:
- C++ compiler
To install FastText, you can use
pip as follows:
pip install fasttext
Alternatively, you can clone the FastText repository from GitHub and build the library from source.
git clone https://github.com/facebookresearch/fastText.git cd fastText pip install .
To use FastText in Python, you will first need to import the
FastText provides several methods for working with word embeddings and text classification. Let’s take a look at some examples.
Word embeddings are a way to represent words in a continuous vector space. FastText provides a simple method for learning word embeddings from a text corpus.
To learn word embeddings, you can use the
FastText.train_unsupervised method. This method takes in a path to a text file and returns a
FastText model that you can use to obtain word vectors.
Here’s an example of how to learn word embeddings with FastText:
model = fasttext.train_unsupervised('text.txt')
Once you have a trained model, you can obtain the vector representation of a word using the
vector = model.get_word_vector('word')
You can also obtain the vector representation of a sentence by averaging the vectors of the individual words.
sentence_vector = model.get_sentence_vector('This is a sentence.')
FastText can also be used for text classification tasks. To train a text classifier, you will need a labeled dataset in the following format:
__label__positive This is a great movie! __label__negative This movie was terrible.
To train a classifier, you can use the
FastText.train_supervised method. This method takes in a path to a file with the labeled data and returns a
FastText model that you can use to make predictions.
model = fasttext.train_supervised('data.txt')
Once you have a trained model, you can use the
predict method to classify new text.
labels, probabilities = model.predict('This is a new text.')
predict method returns a tuple with the predicted labels and their associated probabilities.
That’s it! These are
just a few examples of how to use FastText in Python for natural language processing tasks. There are many more features and capabilities available in the library, such as the ability to save and load models, fine-tune models on additional data, and perform efficient subword embeddings.
If you’re interested in learning more about FastText, I recommend checking out the official documentation and the examples provided in the repository.