How to save fasttext model in vec format?

With fasttext, you can effortlessly obtain word vectors in vec format. To achieve this, delve into the intricacies of the provided code:

from fasttext import load_model

# Load your pre-trained fasttext BIN model
model = load_model(YOUR-BIN-MODEL-PATH)

# Initialize an empty list to store lines
lines = []

# Extract all words from the model's vocabulary
words = model.get_words()

# Open a file for writing in the vec format
with open(YOUR-VEC-FILE-PATH, 'w') as file_out:
    # Write the first line containing the total number of words and vector dimension
    file_out.write(str(len(words)) + " " + str(model.get_dimension()) + "\n")

    # Iterate through the words and their vectors
    for w in words:
        # Retrieve the vector for the current word
        v = model.get_word_vector(w)

        # Convert the vector components to a string
        vstr = ""
        for vi in v:
            vstr += " " + str(vi)

        # Write the word and its vector to the file
        try:
            file_out.write(w + vstr + '\n')
        except:
            pass

# The resulting VEC file contains all word vectors from the fasttext model

To minimize the file size, you can adjust the format of the vector components:

# Replace this line
vstr += " " + str(vi)

# With this line to keep only 4 decimal digits
vstr += " " + "{:.4f}".format(vi)

Alternatively, consider using the gensim library to generate fasttext embeddings:

from gensim.models import FastText

# Load your data
sentences = open('data.txt', 'r').readlines()
tokenized_sentences = tokenize(sentences)

# Create and train the FastText model
model = FastText(vector_size=300, window=5, min_count=1, sentences=tokenized_sentences, epochs=10)

# Save vectors to .vec file
model.wv.save_word2vec_format("embeddings.vec")

In conclusion, you have the flexibility to leverage either the fasttext library or gensim to obtain word vectors in the vec format, catering to your specific needs and preferences.