How to use fastText for text clustering on Linux?

To use fastText for text clustering on Linux, you can follow these steps:

1. Install fastText: Install the fastText package on your Linux machine. You can download the package from the fastText GitHub repository or install it using a package manager such as apt.
2. Prepare the text data: Convert the text data to fastText format, which is a plain text file where each line corresponds to a single text document. Each line should start with a label, followed by a tab, and then the text content of the document.
3. Train the model: Train the fastText model on the text data using the fasttext command-line tool. You can specify the training data file, the number of clusters you want to create, and other hyperparameters such as learning rate and dimensionality of the word vectors. For example, to create 10 clusters, you can run:

fasttext cluster -input data.txt -k 10 -output model

This will create a model file model.bin that contains the word vectors and the clusters.
4. Evaluate the model: Evaluate the model on a test set to see how well it performs in clustering the text data. You can also visualize the clusters using dimensionality reduction techniques such as t-SNE.
5. Use the model for text clustering: Use the trained model to cluster new text documents by computing the word vectors for the documents and assigning them to the closest cluster based on cosine similarity.

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