How to classify text data using fastText on Linux?

To classify text data using fastText on Linux, you can use the fasttext command-line tool or the fasttext.FastText Python API. Here are some general steps to follow:

1. Prepare the text data: The text data should be in a text file, where each line contains the text to be classified. Make sure the data is encoded in UTF-8 format.
2. Train a supervised model: Train a supervised model using the fastText supervised command or API. The model should be trained on a labeled dataset, where each example has at least one label.
3. Prepare the test data: Prepare a separate test set with the same format as the training data.
4. Apply the model to the test data: Apply the trained model to the test data using the fasttext predict command or API. This will output the predicted label(s) for each example in the test set.
5. Evaluate the model: Evaluate the performance of the model using metrics such as accuracy, precision, recall, and F1 score.

Here are some more specific steps for text classification using the fasttext command-line tool:

1. Install fastText on Linux (either by building from source or by using pip).
2. Prepare the training data and put it in a text file called train.txt.
3. Train a supervised model using the fasttext supervised command:

fasttext supervised -input train.txt -output model

This will train a supervised model using the default hyperparameters and save the model files in the model directory.
4. Prepare the test data and put it in a text file called test.txt.
5. Apply the trained model to the test data using the fasttext predict command:

fasttext predict model.bin test.txt

This will output the predicted labels for each example in the test set.
6. Evaluate the performance of the model using the fasttext test command:

fasttext test model.bin test.txt

This will output various metrics such as accuracy, precision, recall, and F1 score.

That’s it! With these steps, you should be able to classify text data using fastText on Linux.