spaCy/website/docs/api/pipeline-functions.md

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Pipeline Functions	Other built-in pipeline components and helpers	spacy/pipeline/functions.py	merge_noun_chunks merge_noun_chunks merge_entities merge_entities merge_subtokens merge_subtokens token_splitter token_splitter doc_cleaner doc_cleaner

merge_noun_chunks

Merge noun chunks into a single token. Also available via the string name "merge_noun_chunks".

Example

texts = [t.text for t in nlp("I have a blue car")]
assert texts == ["I", "have", "a", "blue", "car"]

nlp.add_pipe("merge_noun_chunks")
texts = [t.text for t in nlp("I have a blue car")]
assert texts == ["I", "have", "a blue car"]

Since noun chunks require part-of-speech tags and the dependency parse, make sure to add this component after the "tagger" and "parser" components. By default, nlp.add_pipe will add components to the end of the pipeline and after all other components.

Name	Description
doc	The Doc object to process, e.g. the Doc in the pipeline. Doc
RETURNS	The modified Doc with merged noun chunks. Doc

merge_entities

Merge named entities into a single token. Also available via the string name "merge_entities".

Example

texts = [t.text for t in nlp("I like David Bowie")]
assert texts == ["I", "like", "David", "Bowie"]

nlp.add_pipe("merge_entities")

texts = [t.text for t in nlp("I like David Bowie")]
assert texts == ["I", "like", "David Bowie"]

Since named entities are set by the entity recognizer, make sure to add this component after the "ner" component. By default, nlp.add_pipe will add components to the end of the pipeline and after all other components.

Name	Description
doc	The Doc object to process, e.g. the Doc in the pipeline. Doc
RETURNS	The modified Doc with merged entities. Doc

merge_subtokens

Merge subtokens into a single token. Also available via the string name "merge_subtokens". As of v2.1, the parser is able to predict "subtokens" that should be merged into one single token later on. This is especially relevant for languages like Chinese, Japanese or Korean, where a "word" isn't defined as a whitespace-delimited sequence of characters. Under the hood, this component uses the Matcher to find sequences of tokens with the dependency label "subtok" and then merges them into a single token.

Example

Note that this example assumes a custom Chinese model that oversegments and was trained to predict subtokens.

doc = nlp("拜托")
print([(token.text, token.dep_) for token in doc])
# [('拜', 'subtok'), ('托', 'subtok')]

nlp.add_pipe("merge_subtokens")
doc = nlp("拜托")
print([token.text for token in doc])
# ['拜托']

Since subtokens are set by the parser, make sure to add this component after the "parser" component. By default, nlp.add_pipe will add components to the end of the pipeline and after all other components.

Name	Description
doc	The Doc object to process, e.g. the Doc in the pipeline. Doc
label	The subtoken dependency label. Defaults to "subtok". str
RETURNS	The modified Doc with merged subtokens. Doc

token_splitter

Split tokens longer than a minimum length into shorter tokens. Intended for use with transformer pipelines where long spaCy tokens lead to input text that exceed the transformer model max length.

Example

config = {"min_length": 20, "split_length": 5}
nlp.add_pipe("token_splitter", config=config, first=True)
doc = nlp("aaaaabbbbbcccccdddddee")
print([token.text for token in doc])
# ['aaaaa', 'bbbbb', 'ccccc', 'ddddd', 'ee']

Setting	Description
min_length	The minimum length for a token to be split. Defaults to 25. int
split_length	The length of the split tokens. Defaults to 5. int
RETURNS	The modified Doc with the split tokens. Doc

doc_cleaner

Clean up Doc attributes. Intended for use at the end of pipelines with tok2vec or transformer pipeline components that store tensors and other values that can require a lot of memory and frequently aren't needed after the whole pipeline has run.

Example

config = {"attrs": {"tensor": None}}
nlp.add_pipe("doc_cleaner", config=config)
doc = nlp("text")
assert doc.tensor is None

Setting	Description
attrs	A dict of the Doc attributes and the values to set them to. Defaults to {"tensor": None, "_.trf_data": None} to clean up after tok2vec and transformer components. dict
silent	If False, show warnings if attributes aren't found or can't be set. Defaults to True. bool
RETURNS	The modified Doc with the modified attributes. Doc

span_cleaner

Remove SpanGroups from doc.spans based on a key prefix. This is used to clean up after the CoreferenceResolver when it's paired with a SpanResolver.

This pipeline function is not yet integrated into spaCy core, and is available via the extension package spacy-experimental starting in version 0.6.0. It exposes the component via entry points, so if you have the package installed, using factory = "span_cleaner" in your training config or nlp.add_pipe("span_cleaner") will work out-of-the-box.

Example

config = {"prefix": "coref_head_clusters"}
nlp.add_pipe("span_cleaner", config=config)
doc = nlp("text")
assert "coref_head_clusters_1" not in doc.spans

Setting	Description
prefix	A prefix to check SpanGroup keys for. Any matching groups will be removed. Defaults to "coref_head_clusters". str
RETURNS	The modified Doc with any matching spans removed. Doc