How to use fastText for topic modeling on Linux?

FastText provides support for topic modeling using the latent Dirichlet allocation (LDA) algorithm. Here are the steps to use fastText for topic modeling on Linux:

1. Prepare the training data: Prepare the training data in fastText format, where each line is a single text document. Unlike in document classification, labels are not required for topic modeling.
2. Train the model: Train the fastText model on the training data using the fasttext command-line tool with the supervised option followed by the -lda flag. You can specify the number of topics to be generated, the number of iterations for the LDA algorithm, and other hyperparameters such as learning rate and dimensionality of the word vectors. For example, to train a topic modeling model with 50 topics, you can run:

fasttext supervised -input train.txt -output model -dim 100 -lr 0.1 -epoch 25 -lda 50 -pretrainedVectors embeddings.vec

This will create a model file model.bin that contains the word vectors and the topic distributions.
3. Get the topic distribution for new documents: Use the predict-prob command-line tool to get the topic distribution for new documents. You can specify the model file, the input file that contains the documents to be classified, and the number of topics to output per document. For example:

fasttext predict-prob model.bin test.txt 5

This will output the top 5 topics and their probabilities for each document in the test.txt file.

That’s it! With these steps, you should be able to use fastText for topic modeling on Linux.