spaCy/website/docs/api/attributeruler.md

title	tag	source	new	teaser	api_base_class	api_string_name	api_trainable
AttributeRuler	class	spacy/pipeline/attributeruler.py	3	Pipeline component for rule-based token attribute assignment	/api/pipe	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. See the usage guide for examples.

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 = {"validate": True}
nlp.add_pipe("attribute_ruler", config=config)

Setting	Description
validate	Whether patterns should be validated (passed to the Matcher). Defaults to False. bool

%%GITHUB_SPACY/spacy/pipeline/attributeruler.py

AttributeRuler.__init__

Initialize the attribute ruler.

Example

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

Name	Description
vocab	The shared vocabulary to pass to the matcher. Vocab
name	Instance name of the current pipeline component. Typically passed in automatically from the factory when the component is added. str
keyword-only
validate	Whether patterns should be validated (passed to the Matcher). Defaults to False. bool

AttributeRuler.__call__

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

Name	Description
doc	The document to process. Doc
RETURNS	The processed document. Doc

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

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

Name	Description
patterns	The Matcher patterns to add. Iterable[List[Dict[Union[int, str], Any]]]
attrs	The attributes to assign to the target token in the matched span. Dict[str, Any]
index	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. int

AttributeRuler.add_patterns

Example

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

Add patterns from a list of pattern dicts. Each pattern dict can specify the keys "patterns", "attrs" and "index", which match the arguments of AttributeRuler.add.

Name	Description
patterns	The patterns to add. Iterable[Dict[str, Union[List[dict], dict, int]]]

AttributeRuler.patterns

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

Name	Description
RETURNS	The patterns added to the attribute ruler. List[Dict[str, Union[List[dict], dict, int]]]

AttributeRuler.initialize

Initialize the component with data and used before training to load in rules from a file. This method is typically called by Language.initialize and lets you customize arguments it receives via the [initialize.components] block in the config.

Example

ruler = nlp.add_pipe("attribute_ruler")
ruler.initialize(lambda: [], nlp=nlp, patterns=patterns)

### config.cfg
[initialize.components.attribute_ruler]

[initialize.components.attribute_ruler.patterns]
@readers = "srsly.read_json.v1"
path = "corpus/attribute_ruler_patterns.json

Name	Description
get_examples	Function that returns gold-standard annotations in the form of Example objects (the training data). Not used by this component. Callable], Iterable[Example
keyword-only
nlp	The current nlp object. Defaults to None. Optional[Language]
patterns	A list of pattern dicts with the keys as the arguments to AttributeRuler.add (patterns/attrs/index) to add as patterns. Defaults to None. Optional[Iterable[Dict[str, Union[List[dict], dict, int]]]]
tag_map	The tag map that maps fine-grained tags to coarse-grained tags and morphological features. Defaults to None. Optional[Dict[str, Dict[Union[int, str], Union[int, str]]]]
morph_rules	The morph rules that map token text and fine-grained tags to coarse-grained tags, lemmas and morphological features. Defaults to None. Optional[Dict[str, Dict[str, Dict[Union[int, str], Union[int, str]]]]]

AttributeRuler.load_from_tag_map

Load attribute ruler patterns from a tag map.

Name	Description
tag_map	The tag map that maps fine-grained tags to coarse-grained tags and morphological features. Dict[str, Dict[Union[int, str], Union[int, str]]]

AttributeRuler.load_from_morph_rules

Load attribute ruler patterns from morph rules.

Name	Description
morph_rules	The morph rules that map token text and fine-grained tags to coarse-grained tags, lemmas and morphological features. Dict[str, Dict[str, Dict[Union[int, str], Union[int, str]]]]

AttributeRuler.score

Score a batch of examples.

Example

scores = ruler.score(examples)

Name	Description
examples	The examples to score. Iterable[Example]
RETURNS	The scores, produced by Scorer.score_token_attr for the attributes "tag", "pos", "morph" and "lemma" if present in any of the target token attributes. Dict[str, float]

AttributeRuler.to_disk

Serialize the pipe to disk.

Example

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

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

AttributeRuler.from_disk

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

Example

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

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

AttributeRuler.to_bytes

Example

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

Serialize the pipe to a bytestring.

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

AttributeRuler.from_bytes

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

Example

ruler_bytes = ruler.to_bytes()
ruler = nlp.add_pipe("attribute_ruler")
ruler.from_bytes(ruler_bytes)

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

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 = 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.