mirror of https://github.com/explosion/spaCy.git
6c783f8045
* Fix code for bag-of-words feature extraction The _ml.py module had a redundant copy of a function to extract unigram bag-of-words features, except one had a bug that set values to 0. Another function allowed extraction of bigram features. Replace all three with a new function that supports arbitrary ngram sizes and also allows control of which attribute is used (e.g. ORTH, LOWER, etc). * Support 'bow' architecture for TextCategorizer This allows efficient ngram bag-of-words models, which are better when the classifier needs to run quickly, especially when the texts are long. Pass architecture="bow" to use it. The extra arguments ngram_size and attr are also available, e.g. ngram_size=2 means unigram and bigram features will be extracted. * Fix size limits in train_textcat example * Explain architectures better in docs |
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annotation.md | ||
cli.md | ||
cython-classes.md | ||
cython-structs.md | ||
cython.md | ||
dependencyparser.md | ||
doc.md | ||
entityrecognizer.md | ||
entityruler.md | ||
goldcorpus.md | ||
goldparse.md | ||
index.md | ||
language.md | ||
lemmatizer.md | ||
lexeme.md | ||
matcher.md | ||
phrasematcher.md | ||
pipeline-functions.md | ||
sentencizer.md | ||
span.md | ||
stringstore.md | ||
tagger.md | ||
textcategorizer.md | ||
token.md | ||
tokenizer.md | ||
top-level.md | ||
vectors.md | ||
vocab.md |