This example demonstrates how to wrap a Fasttext model to be sklearn-compatible for direct compatibility with cleanlab.classification. The wrapper code is in fasttext_wrapper.py and the notebook demonstrates how to apply it to a text classification dataset.

Install pigz if you system doesn't already have it. This can be done with brew on macOS:

$ brew install pigz

Run bash script below to download all the data.

$ bash ./download_data.sh

Start Jupyter and run the notebook: fasttext_amazon_reviews.ipynb