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126 lines
6.1 KiB
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126 lines
6.1 KiB
Plaintext
---
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title: EntityRuler
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version: 2.1
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teaser: 'Pipeline component for rule-based named entity recognition'
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api_string_name: entity_ruler
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api_trainable: false
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---
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<Infobox title="New in v4" variant="warning">
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As of spaCy v4, there is no separate `EntityRuler` class. The entity ruler is
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implemented as a special case of the `SpanRuler` component.
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See the [migration guide](#migrating) below for differences between the v3
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`EntityRuler` and v4 `SpanRuler` implementations of the `entity_ruler`
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component.
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See the [`SpanRuler`](/api/spanruler) API docs for the full API.
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</Infobox>
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The entity ruler lets you add spans to the [`Doc.ents`](/api/doc#ents) using
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token-based rules or exact phrase matches. It can be combined with the
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statistical [`EntityRecognizer`](/api/entityrecognizer) to boost accuracy, or
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used on its own to implement a purely rule-based entity recognition system. For
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usage examples, see the docs on
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[rule-based entity recognition](/usage/rule-based-matching#entityruler).
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## Assigned Attributes {id="assigned-attributes"}
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This component assigns predictions basically the same way as the
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[`EntityRecognizer`](/api/entityrecognizer).
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Predictions can be accessed under `Doc.ents` as a tuple. Each label will also be
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reflected in each underlying token, where it is saved in the `Token.ent_type`
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and `Token.ent_iob` fields. Note that by definition each token can only have one
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label.
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When setting `Doc.ents` to create training data, all the spans must be valid and
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non-overlapping, or an error will be thrown.
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| Location | Value |
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| ----------------- | ----------------------------------------------------------------- |
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| `Doc.ents` | The annotated spans. ~~Tuple[Span]~~ |
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| `Token.ent_iob` | An enum encoding of the IOB part of the named entity tag. ~~int~~ |
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| `Token.ent_iob_` | The IOB part of the named entity tag. ~~str~~ |
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| `Token.ent_type` | The label part of the named entity tag (hash). ~~int~~ |
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| `Token.ent_type_` | The label part of the named entity tag. ~~str~~ |
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## Config and implementation {id="config"}
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The default config is defined by the pipeline component factory and describes
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how the component should be configured. You can override its settings via the
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`config` argument on [`nlp.add_pipe`](/api/language#add_pipe) or in your
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[`config.cfg` for training](/usage/training#config).
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> #### Example
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>
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> ```python
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> config = {
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> "phrase_matcher_attr": None,
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> "validate": True,
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> "overwrite_ents": False,
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> "ent_id_sep": "||",
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> }
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> nlp.add_pipe("entity_ruler", config=config)
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> ```
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| Setting | Description |
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| ---------------------------------------------------- | --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
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| `phrase_matcher_attr` | Optional attribute name match on for the internal [`PhraseMatcher`](/api/phrasematcher), e.g. `LOWER` to match on the lowercase token text. Defaults to `None`. ~~Optional[Union[int, str]]~~ |
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| `matcher_fuzzy_compare` <Tag variant="new">3.5</Tag> | The fuzzy comparison method, passed on to the internal `Matcher`. Defaults to `spacy.matcher.levenshtein.levenshtein_compare`. ~~Callable~~ |
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| `validate` | Whether patterns should be validated (passed to the `Matcher` and `PhraseMatcher`). Defaults to `False`. ~~bool~~ |
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| `overwrite_ents` | If existing entities are present, e.g. entities added by the model, overwrite them by matches if necessary. Defaults to `False`. ~~bool~~ |
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| `ent_id_sep` | Separator used internally for entity IDs. Defaults to `"\|\|"`. ~~str~~ |
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| `scorer` | The scoring method. Defaults to [`spacy.scorer.get_ner_prf`](/api/scorer#get_ner_prf). ~~Optional[Callable]~~ |
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## Migrating from v3 {id="migrating"}
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### Loading patterns
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Unlike the v3 `EntityRuler`, the `SpanRuler` cannot load patterns on
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initialization with `SpanRuler(patterns=patterns)` or directly from a JSONL file
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path with `SpanRuler.from_disk(jsonl_path)`. Patterns should be loaded from the
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JSONL file separately and then added through
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[`SpanRuler.initialize`](/api/spanruler#initialize]) or
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[`SpanRuler.add_patterns`](/api/spanruler#add_patterns).
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```diff
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ruler = nlp.get_pipe("entity_ruler")
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- ruler.from_disk("patterns.jsonl")
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+ import srsly
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+ patterns = srsly.read_jsonl("patterns.jsonl")
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+ ruler.add_patterns(patterns)
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```
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### Saving patterns
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`SpanRuler.to_disk` always saves the full component data to a directory and does
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not include an option to save the patterns to a single JSONL file.
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```diff
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ruler = nlp.get_pipe("entity_ruler")
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- ruler.to_disk("patterns.jsonl")
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+ import srsly
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+ srsly.write_jsonl("patterns.jsonl", ruler.patterns)
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```
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### Accessing token and phrase patterns
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The separate token patterns and phrase patterns are no longer accessible under
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`ruler.token_patterns` or `ruler.phrase_patterns`. You can access the combined
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patterns in their original format using the property
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[`SpanRuler.patterns`](/api/spanruler#patterns).
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### Removing patterns by ID
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[`SpanRuler.remove`](/api/spanruler#remove) removes by label rather than ID. To
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remove by ID, use [`SpanRuler.remove_by_id`](/api/spanruler#remove_by_id):
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```diff
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ruler = nlp.get_pipe("entity_ruler")
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- ruler.remove("id")
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+ ruler.remove_by_id("id")
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```
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