ShallowLearn 0.0.2

A collection of supervised learning models based on shallow neural network approaches (e.g., word2vec and fastText) with some additional exclusive features

A collection of supervised learning models based on shallow neural network approaches (e.g., word2vec and fastText) with some additional exclusive features. Written in Python and fully compatible with Scikit-learn.

Getting Started

Install the latest version:

pip install cython
pip install shallowlearn

Import models from shallowlearn.models, they implement the standard methods for supervised learning in Scikit-learn, e.g., fit(X, y), predict(X), etc.

Data is raw text, each sample is a list of tokens (words of a document), while each target value in y can be a single label (or a list in case of multi-label training set) associated with the relative sample.

Models

shallowlearn.models.GensimFastText

A supervised learning model based on the fastText algorithm [1]. The code is mostly taken and rewritten from Gensim, it takes advantage of its optimizations (e.g. Cython) and support.

shallowlearn.models.FastText

TODO: The supervised algorithm of fastText implemented in https://github.com/salestock/fastText.py

shallowlearn.models.DeepInverseRegression

TODO: Based on https://radimrehurek.com/gensim/models/word2vec.html#gensim.models.word2vec.Word2Vec.score

Exclusive Features

TODO

Benchmarks

The script scripts/document_classification_20newsgroups.py refers to this Scikit-learn example in which text classifiers are compared on a reference dataset; we added our models to the comparison. The current results, even if still preliminary, are comparable with other approaches, achieving the best performance in speed.

Results as of release 0.0.2, with chi2_select option set to 80%. The times take into account of tf-idf vectorization in the “classic” classifiers; the evaluation measure is macro F1.

References

[1]

1. Joulin, E. Grave, P. Bojanowski, T. Mikolov, Bag of Tricks for Efficient Text Classification