giacbrd/ShallowLearn
An experiment about re-implementing supervised learning models based on shallow neural network approaches (e.g. fastText) with some additional exclusive features and nice API. Written in Python and fully compatible with Scikit-learn.
This project helps data scientists and machine learning practitioners quickly classify text documents. You provide raw text (like sentences or paragraphs) and their corresponding categories or labels. The output is a model that can predict the category of new, unseen text, offering a fast and efficient solution for text classification tasks.
198 stars. No commits in the last 6 months. Available on PyPI.
Use this if you need to build text classification models that are fast, efficient, and compatible with the Scikit-learn ecosystem for tasks like sentiment analysis, spam detection, or document routing.
Not ideal if you primarily work with structured numerical data rather than raw text, or if you require deep neural networks for highly complex language understanding tasks.
Stars
198
Forks
29
Language
Python
License
LGPL-3.0
Category
Last pushed
Aug 08, 2017
Commits (30d)
0
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