thinc 5.0.0

Learn sparse linear models

thinc is a Cython library for learning models with millions of parameters and dozens of classes. It drives http://honnibal.github.io/spaCy , a pipeline of very efficient NLP components. I’ve only used thinc from Cython; no real Python API is currently available.

Currently the only model implemented is the averaged perceptron, which is surprisingly competitive for these problems.

Despite the recent enthusiasm for deep learning, linear models can still perform very well, if the right feature engineering is applied. The key is adding good conjunction features — e.g., “next_word=X && next_next_word=Y”. For this, I have a helper-class thinc.features.Extractor, which you pass a list of templates, which then performs your feature extraction, given an array of atomic context items.

License

The MIT License (MIT)

Copyright (C) 2015 Matthew Honnibal

Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the “Software”), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.

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