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Add docs [ci skip]

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Ines Montani 2020-08-31 16:10:41 +02:00
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6
website/docs/api/matcher.md
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@ -117,9 +117,11 @@ Find all token sequences matching the supplied patterns on the `Doc` or `Span`.
> ```
| Name | Description |
| ----------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| ------------------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `doclike` | The `Doc` or `Span` to match over. ~~Union[Doc, Span]~~ |
| **RETURNS** | A list of `(match_id, start, end)` tuples, describing the matches. A match tuple describes a span `doc[start:end`]. The `match_id` is the ID of the added match pattern. ~~List[Tuple[int, int, int]]~~ |
| _keyword-only_ | |
| `as_spans` <Tag variant="new">3</Tag> | Instead of tuples, return a list of [`Span`](/api/span) objects of the matches, with the `match_id` assigned as the span label. Defaults to `False`. ~~bool~~ |
| **RETURNS** | A list of `(match_id, start, end)` tuples, describing the matches. A match tuple describes a span `doc[start:end`]. The `match_id` is the ID of the added match pattern. If `as_spans` is set to `True`, a list of `Span` objects is returned instead. ~~Union[List[Tuple[int, int, int]], List[Span]]~~ |
## Matcher.pipe {#pipe tag="method"}

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website/docs/api/phrasematcher.md
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@ -58,9 +58,11 @@ Find all token sequences matching the supplied patterns on the `Doc`.
> ```
| Name | Description |
| ----------- | ----------------------------------- |
| ------------------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `doc` | The document to match over. ~~Doc~~ |
| **RETURNS** | list | A list of `(match_id, start, end)` tuples, describing the matches. A match tuple describes a span `doc[start:end]`. The `match_id` is the ID of the added match pattern. ~~List[Tuple[int, int, int]]~~ |
| _keyword-only_ | |
| `as_spans` <Tag variant="new">3</Tag> | Instead of tuples, return a list of [`Span`](/api/span) objects of the matches, with the `match_id` assigned as the span label. Defaults to `False`. ~~bool~~ |
| **RETURNS** | A list of `(match_id, start, end)` tuples, describing the matches. A match tuple describes a span `doc[start:end`]. The `match_id` is the ID of the added match pattern. If `as_spans` is set to `True`, a list of `Span` objects is returned instead. ~~Union[List[Tuple[int, int, int]], List[Span]]~~ |
<Infobox title="Note on retrieving the string representation of the match_id" variant="warning">

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website/docs/usage/rule-based-matching.md
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@ -493,6 +493,39 @@ you prefer.
| `i` | Index of the current match (`matches[i`]). ~~int~~ |
| `matches` | A list of `(match_id, start, end)` tuples, describing the matches. A match tuple describes a span `doc[start:end`]. ~~ List[Tuple[int, int int]]~~ |
### Creating spans from matches {#matcher-spans}
Creating [`Span`](/api/span) objects from the returned matches is a very common
use case. spaCy makes this easy by giving you access to the `start` and `end`
token of each match, which you can use to construct a new span with an optional
label. As of spaCy v3.0, you can also set `as_spans=True` when calling the
matcher on a `Doc`, which will return a list of [`Span`](/api/span) objects
using the `match_id` as the span label.
```python
### {executable="true"}
import spacy
from spacy.matcher import Matcher
from spacy.tokens import Span
nlp = spacy.blank("en")
matcher = Matcher(nlp.vocab)
matcher.add("PERSON", [[{"lower": "barack"}, {"lower": "obama"}]])
doc = nlp("Barack Obama was the 44th president of the United States")
# 1. Return (match_id, start, end) tuples
matches = matcher(doc)
for match_id, start, end in matches:
# Create the matched span and assign the match_id as a label
span = Span(doc, start, end, label=match_id)
print(span.text, span.label_)
# 2. Return Span objects directly
matches = matcher(doc, as_spans=True)
for span in matches:
print(span.text, span.label_)
```
### Using custom pipeline components {#matcher-pipeline}
Let's say your data also contains some annoying pre-processing artifacts, like