spaCy/website/docs/api/attributeruler.md

title	tag	source	new	teaser	api_string_name	api_trainable
AttributeRuler	class	spacy/pipeline/attributeruler.py	3	Pipeline component for rule-based token attribute assignment	attribute_ruler	false

The attribute ruler lets you set token attributes for tokens identified by Matcher patterns. The attribute ruler is typically used to handle exceptions for token attributes and to map values between attributes such as mapping fine-grained POS tags to coarse-grained POS tags.

Config and implementation

The default config is defined by the pipeline component factory and describes how the component should be configured. You can override its settings via the config argument on nlp.add_pipe or in your config.cfg for training.

Example

config = {
   "pattern_dicts": None,
   "validate": True,
}
nlp.add_pipe("attribute_ruler", config=config)

Setting	Type	Description	Default
pattern_dicts	Iterable[dict]	A list of pattern dicts with the keys as the arguments to AttributeRuler.add (patterns/attrs/index) to add as patterns.	None
validate	bool	Whether patterns should be validated (passed to the Matcher).	False

https://github.com/explosion/spaCy/blob/develop/spacy/pipeline/attributeruler.py

AttributeRuler.__init__

Initialize the attribute ruler. If pattern dicts are supplied here, they need to be a list of dictionaries with "patterns", "attrs", and optional "index" keys, e.g.:

pattern_dicts = \[
    {"patterns": \[\[{"TAG": "VB"}\]\], "attrs": {"POS": "VERB"}},
    {"patterns": \[\[{"LOWER": "an"}\]\], "attrs": {"LEMMA": "a"}},
\]

Example

# Construction via add_pipe
attribute_ruler = nlp.add_pipe("attribute_ruler")

Name	Type	Description
vocab	Vocab	The shared nlp object to pass the vocab to the matchers and process phrase patterns.
name	str	Instance name of the current pipeline component. Typically passed in automatically from the factory when the component is added. Used to disable the current entity ruler while creating phrase patterns with the nlp object.
keyword-only
pattern_dicts	Iterable[Dict]]	Optional patterns to load in on initialization. Defaults to None.
validate	bool	Whether patterns should be validated (passed to the Matcher). Defaults to False.

AttributeRuler.__call__

Apply the attribute ruler to a Doc, setting token attributes for tokens matched by the provided patterns.

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.

AttributeRuler.add

Add patterns to the attribute ruler. The patterns are a list of Matcher patterns and the attributes are a dict of attributes to set on the matched token. If the pattern matches a span of more than one token, the index can be used to set the attributes for the token at that index in the span. The index may be negative to index from the end of the span.

Example

attribute_ruler = nlp.add_pipe("attribute_ruler")
patterns = [[{"TAG": "VB"}]]
attrs = {"POS": "VERB"}
attribute_ruler.add(patterns=patterns, attrs=attrs)

Name	Type	Description
patterns	Iterable[List[Dict]]	A list of Matcher patterns.
attrs	dict	The attributes to assign to the target token in the matched span.
index	int	The index of the token in the matched span to modify. May be negative to index from the end of the span. Defaults to 0.

AttributeRuler.add_patterns

Example

attribute_ruler = nlp.add_pipe("attribute_ruler")
pattern_dicts = \[
  {
    "patterns": \[\[{"TAG": "VB"}\]\],
    "attrs": {"POS": "VERB"}
  },
  {
    "patterns": \[\[{"LOWER": "two"}, {"LOWER": "apples"}\]\],
    "attrs": {"LEMMA": "apple"},
    "index": -1
  },
\]
attribute_ruler.add_patterns(pattern_dicts)

Add patterns from a list of pattern dicts with the keys as the arguments to AttributeRuler.add.

Name	Type	Description
pattern_dicts	Iterable[Dict]]	The patterns to add.

AttributeRuler.patterns

Get all patterns that have been added to the attribute ruler in the patterns_dict format accepted by AttributeRuler.add_patterns.

Name	Type	Description
RETURNS	List[dict]	The patterns added to the attribute ruler.

AttributeRuler.load_from_tag_map

Load attribute ruler patterns from a tag map.

Name	Type	Description
tag_map	dict	The tag map that maps fine-grained tags to coarse-grained tags and morphological features.

AttributeRuler.load_from_morph_rules

Load attribute ruler patterns from morph rules.

Name	Type	Description
morph_rules	dict	The morph rules that map token text and fine-grained tags to coarse-grained tags, lemmas and morphological features.

AttributeRuler.to_disk

Serialize the pipe to disk.

Example

attribute_ruler = nlp.add_pipe("attribute_ruler")
attribute_ruler.to_disk("/path/to/attribute_ruler")

Name	Type	Description
path	str / Path	A path to a directory, which will be created if it doesn't exist. Paths may be either strings or Path-like objects.
keyword-only
exclude	Iterable[str]	String names of serialization fields to exclude.

AttributeRuler.from_disk

Load the pipe from disk. Modifies the object in place and returns it.

Example

attribute_ruler = nlp.add_pipe("attribute_ruler")
attribute_ruler.from_disk("/path/to/attribute_ruler")

Name	Type	Description
path	str / Path	A path to a directory. Paths may be either strings or Path-like objects.
keyword-only
exclude	Iterable[str]	String names of serialization fields to exclude.
RETURNS	AttributeRuler	The modified AttributeRuler object.

AttributeRuler.to_bytes

Example

attribute_ruler = nlp.add_pipe("attribute_ruler")
attribute_ruler_bytes = attribute_ruler.to_bytes()

Serialize the pipe to a bytestring.

Name	Type	Description
keyword-only
exclude	Iterable[str]	String names of serialization fields to exclude.
RETURNS	bytes	The serialized form of the AttributeRuler object.

AttributeRuler.from_bytes

Load the pipe from a bytestring. Modifies the object in place and returns it.

Example

attribute_ruler_bytes = attribute_ruler.to_bytes()
attribute_ruler = nlp.add_pipe("attribute_ruler")
attribute_ruler.from_bytes(attribute_ruler_bytes)

Name	Type	Description
bytes_data	bytes	The data to load from.
keyword-only
exclude	Iterable[str]	String names of serialization fields to exclude.
RETURNS	AttributeRuler	The AttributeRuler object.

Serialization fields

During serialization, spaCy will export several data fields used to restore different aspects of the object. If needed, you can exclude them from serialization by passing in the string names via the exclude argument.

Example

data = attribute_ruler.to_disk("/path", exclude=["vocab"])

Name	Description
vocab	The shared Vocab.
patterns	The Matcher patterns. You usually don't want to exclude this.
attrs	The attributes to set. You usually don't want to exclude this.
indices	The token indices. You usually don't want to exclude this.