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Adjust formatting [ci skip]
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@ -38,7 +38,7 @@ be shown.
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| Name | Type | Description |
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| --------------------------------------- | --------------- | ------------------------------------------------------------------------------------------- |
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| `vocab` | `Vocab` | The vocabulary object, which must be shared with the documents the matcher will operate on. |
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| `max_length` | int | Deprecated argument - the `PhraseMatcher` does not have a phrase length limit anymore. |
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| `max_length` | int | Deprecated argument - the `PhraseMatcher` does not have a phrase length limit anymore. |
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| `attr` <Tag variant="new">2.1</Tag> | int / unicode | The token attribute to match on. Defaults to `ORTH`, i.e. the verbatim token text. |
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| `validate` <Tag variant="new">2.1</Tag> | bool | Validate patterns added to the matcher. |
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| **RETURNS** | `PhraseMatcher` | The newly constructed object. |
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@ -72,15 +72,16 @@ Find all token sequences matching the supplied patterns on the `Doc`.
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<Infobox title="Note on retrieving the string representation of the match_id" variant="warning">
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Because spaCy stores all strings as integers, the match_id you get back will be an integer, too – but you can always get the string representation by looking it up in the vocabulary's StringStore, i.e. nlp.vocab.strings:
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Because spaCy stores all strings as integers, the `match_id` you get back will
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be an integer, too – but you can always get the string representation by looking
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it up in the vocabulary's `StringStore`, i.e. `nlp.vocab.strings`:
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```
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```python
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match_id_string = nlp.vocab.strings[match_id]
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```
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</Infobox>
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## PhraseMatcher.pipe {#pipe tag="method"}
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Match a stream of documents, yielding them in turn.
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