FastText embeddings in NLP

Introduction

FastText embeddings help computers understand words by turning them into numbers that keep word meanings and parts. This helps machines work better with language.

When you want to understand the meaning of words in a text message app.

When you need to find similar words even if they are spelled differently or new.

When building a chatbot that should understand slang or misspelled words.

When analyzing customer reviews with many typos or new words.

When you want to improve search results by understanding word parts.

Syntax

NLP

from gensim.models import FastText

# Train FastText model
model = FastText(sentences, vector_size=100, window=5, min_count=1, epochs=10)

# Get word vector
vector = model.wv['word']

sentences should be a list of tokenized sentences (list of lists of words).

vector_size sets the size of the word vectors (usually 50-300).

Examples

Train FastText on two simple sentences and get the vector for 'hello'.

NLP

from gensim.models import FastText

sentences = [['hello', 'world'], ['fasttext', 'embeddings', 'are', 'useful']]
model = FastText(sentences, vector_size=50, window=3, min_count=1, epochs=5)
vector = model.wv['hello']

Get the vector for the word 'embeddings' after training.

NLP

vector = model.wv.get_vector('embeddings')

Find top 3 words similar to 'fasttext'.

NLP

similar_words = model.wv.most_similar('fasttext', topn=3)

Sample Model

This program trains FastText on a few sentences, gets the vector for 'fasttext', and finds two similar words.

NLP

from gensim.models import FastText

# Sample sentences
sentences = [
    ['machine', 'learning', 'is', 'fun'],
    ['fasttext', 'helps', 'with', 'word', 'representations'],
    ['embeddings', 'capture', 'meaning', 'of', 'words'],
    ['fasttext', 'uses', 'subword', 'information']
]

# Train FastText model
model = FastText(sentences, vector_size=20, window=3, min_count=1, epochs=10)

# Get vector for a word
vector = model.wv['fasttext']

# Find similar words
similar = model.wv.most_similar('fasttext', topn=2)

print('Vector for "fasttext":', vector)
print('Top 2 words similar to "fasttext":', similar)

Important Notes

FastText creates vectors using parts of words, so it works well with rare or new words.

Training on more sentences improves the quality of embeddings.

You can save and load FastText models using model.save() and FastText.load().

Summary

FastText turns words into numbers by looking at word parts.

It helps understand new or misspelled words better than some other methods.

Use FastText when you want smart word representations for language tasks.