gensim.models.FastText

gensim.models.FastText is a class in Gensim library that is used to train and use FastText models for natural language processing tasks. FastText is an extension of the Word2Vec model that is capable of capturing subword information. It is particularly useful for dealing with rare or out-of-vocabulary words.

The FastText model learns vector representations of words by considering the morphological structure of words, such as prefixes, suffixes, and n-grams. The model is trained on a corpus of text and can be used to find similar words, perform text classification, and more.

The FastText model in Gensim has several parameters that can be adjusted to customize the training process, including the number of dimensions for the word vectors, the size of the sliding window, and the minimum count of words to consider.

To use the FastText model in Gensim, you first need to preprocess your text data and create a corpus object. You can then instantiate a FastText model and train it on the corpus using the train() method. Once the model is trained, you can use it to perform various natural language processing tasks.