The Best Text Classification library for a Quick Baseline

Text classification is a very frequent use case for machine learning (ML) and natural language processing (NLP). It’s used for things like spam detection in emails, sentiment analysis for social media posts, or intent detection in chat bots.

In this series I am going to compare several libraries that can be used to train text classification models.

The fastText library

fastText is a tool from Facebook made specifically for efficient text classification. It’s written in C++ and optimized for multi-core training, so it’s very fast, being able to process hundreds of thousands of words per second per core. It’s very straightforward to use, either as a Python library or through a CLI tool.

Despite using an older machine learning model (a neural network architecture from 2016), fastText is still very competitive and provides an excellent baseline. If you also take into account resource usage, it will be all but impossible to improve on the fastText results, considering that the only models that perform better require powerful GPUs.

Getting started with text classification with fastText

fastText requires the training data for text classification to be in a special format: each document should be on a single line and the labels should be at the start of the line, with the prefix __label__, like this:

Training data format

__label__sauce __label__cheese How much does potato starch affect a cheese sauce recipe?
 __label__food-safety __label__acidity Dangerous pathogens capable of growing in acidic environments
 __label__cast-iron __label__stove How do I cover up the white spots on my cast iron stove?

If you use Doccano for annotating the text data, it has an option to export the data in fastText format. But even if you used another tool for annotation, it’s only a couple of lines of Python code to convert to the appropriate format. Let’s say we have our data in a JSONL format, with each JSON object having a labels key and a text key. To convert to fastText format, we can use the following short snippet:

with open("fasttext.txt", "w") as output:
    with open("dataset.jsonl", encoding="utf8") as f:
        for l in f:
            doc = json.loads(l)
            labels = [x.replace(" ", "_") for x in doc['labels']]
            labels = " ".join(f"__label__{x}" for x in labels)
            txt = " ".join(l['text'].splitlines())
            line = f"{labels} {txt}\n"

Training text classification models with fastText

After you have the data in the right format, the simplest way to use fastText is through it’s CLI tool. After you installed it, you can train a model with the supervised subcommand:

> ./fasttext supervised -input fasttext.txt -output model
Read 0M words
Number of words:  16568
Number of labels: 736
Progress: 100.0% words/sec/thread:   47065 lr:  0.000000 avg.loss: 10.027837 ETA:   0h 0m 0s

You can evaluate the model on a separate dataset with the test subcommand and you will get the precision and recall for the first candidate label:

> ./fasttext test model.bin validation.txt
N       15404
P@1     0.162
R@1     0.0701

You can also get predictions for new documents:

> ./fasttext predict model.bin -
How to make lasagna?
Best way to chop meat
How to store steak

fastText comes with a builtin hyperparameter optimizer, to find the best model on a validation dataset, within the given time (5 minutes by default):

> ./fasttext supervised -input fasttext.txt -output model -autotune-validation validation.txt

If we reevaluate this model we’ll find it performs much better:

> ./fasttext test model.bin validation.txt
N       15404
P@1     0.727
R@1     0.315

A precision of 0.72, compared to 0.16 before. Not bad, for 10 minutes of our time, out of which 5 was waiting for the computer to find us a better model1Autotuning and performance evaluation should happen on separate datasets, to avoid overfitting, so real world performance is likely a bit worse than we got here.

Optimizing for different metrics

This library provides a couple of knobs you can use to try to obtain better models, from what kind of n-grams to use, how big the learning rate should be, what should be the loss function, but also what metric are you trying to optimize. Is precision or recall better aligned with your business KPIs? Is it more important to have the top result be a really good one or are you looking for several good results among in the top 5? Are you only interested in high confidence results? All this depends on the problem you are trying to solve and fastText provides ways to optimize for each of those.

Cons of fastText

Of course, fastText has some disadvantages:

  • Not much flexibility – only one neural network architecture from 2016 implemented with very few parameters to tune
  • No option to speed up using GPU
  • Can be used only for text classification and word embeddings
  • Doesn’t have too wide support in other tools (for deployments for example)


fastText is a great library to use when you want to start solving a text classification problem. In less than half an hour, you can get a good baseline going, which will tell you if this is a problem that is worth pursuing or not.