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.
Implements both Gensim-optimized and native fastText variants with support for hierarchical softmax, negative sampling, and feature hashing via the hashing trick for efficient online learning. Offers exclusive persistence features leveraging Gensim's SaveLoad interface with compression control, and includes pre-training of word embeddings via `fit_embeddings()`. Benchmarks demonstrate competitive speed on text classification while supporting incremental learning through `partial_fit()`.
198 stars and 12 monthly downloads. No commits in the last 6 months. Available on PyPI.
Stars
198
Forks
29
Language
Python
License
LGPL-3.0
Category
Last pushed
Aug 08, 2017
Monthly downloads
12
Commits (30d)
0
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