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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 | ||