RubixML/Sentiment
An example project using a feed-forward neural network for text sentiment classification trained with 25,000 movie reviews from the IMDB website.
Implements a preprocessing pipeline combining text normalization, bag-of-words vectorization with a 10,000-word vocabulary, TF-IDF weighting, and Z-scale standardization before feeding data into a 5-layer perceptron with LeakyReLU and PReLU activations. Built on the Rubix ML framework in PHP with optional Tensor extension acceleration, the model includes batch normalization and AdaMax optimization for efficient convergence on the 50,000-sample dataset.
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115
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Language
PHP
License
MIT
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Last pushed
Jul 25, 2025
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