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Extend what's new in v2.3 with vocab / is_oov (#5635)
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@ -182,6 +182,51 @@ If you're adding data for a new language, the normalization table should be
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added to `spacy-lookups-data`. See
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[adding norm exceptions](/usage/adding-languages#norm-exceptions).
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#### No preloaded lexemes/vocab for models with vectors
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To reduce the initial loading time, the lexemes in `nlp.vocab` are no longer
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loaded on initialization for models with vectors. As you process texts, the
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lexemes will be added to the vocab automatically, just as in models without
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vectors.
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To see the number of unique vectors and number of words with vectors, see
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`nlp.meta['vectors']`, for example for `en_core_web_md` there are `20000`
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unique vectors and `684830` words with vectors:
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```python
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{
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'width': 300,
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'vectors': 20000,
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'keys': 684830,
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'name': 'en_core_web_md.vectors'
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}
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```
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If required, for instance if you are working directly with word vectors rather
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than processing texts, you can load all lexemes for words with vectors at once:
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```python
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for orth in nlp.vocab.vectors:
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_ = nlp.vocab[orth]
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```
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#### Lexeme.is_oov and Token.is_oov
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<Infobox title="Important note" variant="warning">
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Due to a bug, the values for `is_oov` are reversed in v2.3.0, but this will be
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fixed in the next patch release v2.3.1.
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</Infobox>
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In v2.3, `Lexeme.is_oov` and `Token.is_oov` are `True` if the lexeme does not
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have a word vector. This is equivalent to `token.orth not in
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nlp.vocab.vectors`.
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Previously in v2.2, `is_oov` corresponded to whether a lexeme had stored
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probability and cluster features. The probability and cluster features are no
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longer included in the provided medium and large models (see the next section).
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#### Probability and cluster features
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> #### Load and save extra prob lookups table
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