The EntityRuler lets you add spans to the Doc.ents using
token-based rules or exact phrase matches. It can be combined with the
statistical EntityRecognizer to boost accuracy, or
used on its own to implement a purely rule-based entity recognition system.
After initialization, the component is typically added to the processing
pipeline using nlp.add_pipe. For usage examples, see
the docs on
rule-based entity recognition.
EntityRuler.__init__
Initialize the entity ruler. If patterns are supplied here, they need to be a
list of dictionaries with a "label" and "pattern" key. A pattern can either
be a token pattern (list) or a phrase pattern (string). For example:
{'label': 'ORG', 'pattern': 'Apple'}.
Example
# Construction via create_piperuler=nlp.create_pipe("entity_ruler")# Construction from classfromspacy.pipelineimportEntityRulerruler=EntityRuler(nlp,overwrite_ents=True)
Name
Type
Description
nlp
Language
The shared nlp object to pass the vocab to the matchers and process phrase patterns.
patterns
iterable
Optional patterns to load in.
phrase_matcher_attr
int / unicode
Optional attr to pass to the internal PhraseMatcher. defaults to None
validate
bool
Whether patterns should be validated, passed to Matcher and PhraseMatcher as validate. Defaults to False.
overwrite_ents
bool
If existing entities are present, e.g. entities added by the model, overwrite them by matches if necessary. Defaults to False.
**cfg
-
Other config parameters. If pipeline component is loaded as part of a model pipeline, this will include all keyword arguments passed to spacy.load.
RETURNS
EntityRuler
The newly constructed object.
EntityRuler._\len__
The number of all patterns added to the entity ruler.
Find matches in the Doc and add them to the doc.ents. Typically, this
happens automatically after the component has been added to the pipeline using
nlp.add_pipe. If the entity ruler was initialized
with overwrite_ents=True, existing entities will be replaced if they overlap
with the matches. When matches overlap in a Doc, the entity ruler prioritizes longer
patterns over shorter, and if equal the match occuring first in the Doc is chosen.
Example
ruler=EntityRuler(nlp)ruler.add_patterns([{"label":"ORG","pattern":"Apple"}])nlp.add_pipe(ruler)doc=nlp("A text about Apple.")ents=[(ent.text,ent.label_)forentindoc.ents]assertents==[("Apple","ORG")]
Name
Type
Description
doc
Doc
The Doc object to process, e.g. the Doc in the pipeline.
RETURNS
Doc
The modified Doc with added entities, if available.
EntityRuler.add_patterns
Add patterns to the entity ruler. A pattern can either be a token pattern (list
of dicts) or a phrase pattern (string). For more details, see the usage guide on
rule-based matching.
Save the entity ruler patterns to a directory. The patterns will be saved as
newline-delimited JSON (JSONL). If a file with the suffix .jsonl is provided,
only the patterns are saved as JSONL. If a directory name is provided, a
patterns.jsonl and cfg file with the component configuration is exported.
Example
ruler=EntityRuler(nlp)ruler.to_disk("/path/to/patterns.jsonl")# saves patterns onlyruler.to_disk("/path/to/entity_ruler")# saves patterns and config
Name
Type
Description
path
unicode / Path
A path to a JSONL file or directory, which will be created if it doesn't exist. Paths may be either strings or Path-like objects.
EntityRuler.from_disk
Load the entity ruler from a file. Expects either a file containing
newline-delimited JSON (JSONL) with one entry per line, or a directory
containing a patterns.jsonl file and a cfg file with the component
configuration.
Example
ruler=EntityRuler(nlp)ruler.from_disk("/path/to/patterns.jsonl")# loads patterns onlyruler.from_disk("/path/to/entity_ruler")# loads patterns and config
Name
Type
Description
path
unicode / Path
A path to a JSONL file or directory. Paths may be either strings or Path-like objects.
RETURNS
EntityRuler
The modified EntityRuler object.
EntityRuler.to_bytes
Serialize the entity ruler patterns to a bytestring.