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# sense2vec | ||
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Use spaCy to go beyond vanilla word2vec | ||
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Read about sense2vec here: | ||
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https://spacy.io/blog/sense2vec-with-spacy | ||
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You can use an online demo of the technology here: | ||
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https://sense2vec.spacy.io/ | ||
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We're currently refining the API, to make this technology easy to use. Once we've completed that, you'll be able | ||
to download the package on PyPi. For now, the code is available to clarify the blog post. | ||
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There are three relevant files in this repository: | ||
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# bin/merge_text.py | ||
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This script pre-processes text using spaCy, so that the sense2vec model can be trained using Gensim. | ||
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# bin/train_word2vec.py | ||
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This script reads a directory of text files, and then trains a word2vec model using Gensim. The script includes its own | ||
vocabulary counting code, because Gensim's vocabulary count is a bit slow for our large, sparse vocabulary. | ||
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# sense2vec/vectors.pyx | ||
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To serve the similarity queries, we wrote a small vector-store class in Cython. This made it easier to add an efficient | ||
cache in front of the service. It also less memory than Gensim's Word2Vec class, as it doesn't hold the keys as Python | ||
unicode strings. | ||
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Similarity queries could be faster, if we had made all vectors contiguous in memory, instead of holding them | ||
as an array of pointers. However, we wanted to allow a `.borrow()` method, so that vectors can be added to the store | ||
by reference, without copying the data. |
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