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189 lines
8.0 KiB
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189 lines
8.0 KiB
Plaintext
---
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title: Pipeline Functions
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teaser: Other built-in pipeline components and helpers
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source: spacy/pipeline/functions.py
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menu:
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- ['merge_noun_chunks', 'merge_noun_chunks']
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- ['merge_entities', 'merge_entities']
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- ['merge_subtokens', 'merge_subtokens']
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- ['token_splitter', 'token_splitter']
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- ['doc_cleaner', 'doc_cleaner']
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---
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## merge_noun_chunks {id="merge_noun_chunks",tag="function"}
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Merge noun chunks into a single token. Also available via the string name
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`"merge_noun_chunks"`.
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> #### Example
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>
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> ```python
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> texts = [t.text for t in nlp("I have a blue car")]
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> assert texts == ["I", "have", "a", "blue", "car"]
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>
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> nlp.add_pipe("merge_noun_chunks")
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> texts = [t.text for t in nlp("I have a blue car")]
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> assert texts == ["I", "have", "a blue car"]
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> ```
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<Infobox variant="warning">
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Since noun chunks require part-of-speech tags and the dependency parse, make
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sure to add this component _after_ the `"tagger"` and `"parser"` components. By
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default, `nlp.add_pipe` will add components to the end of the pipeline and after
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all other components.
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</Infobox>
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| Name | Description |
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| ----------- | -------------------------------------------------------------------- |
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| `doc` | The `Doc` object to process, e.g. the `Doc` in the pipeline. ~~Doc~~ |
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| **RETURNS** | The modified `Doc` with merged noun chunks. ~~Doc~~ |
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## merge_entities {id="merge_entities",tag="function"}
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Merge named entities into a single token. Also available via the string name
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`"merge_entities"`.
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> #### Example
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>
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> ```python
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> texts = [t.text for t in nlp("I like David Bowie")]
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> assert texts == ["I", "like", "David", "Bowie"]
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>
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> nlp.add_pipe("merge_entities")
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>
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> texts = [t.text for t in nlp("I like David Bowie")]
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> assert texts == ["I", "like", "David Bowie"]
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> ```
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<Infobox variant="warning">
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Since named entities are set by the entity recognizer, make sure to add this
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component _after_ the `"ner"` component. By default, `nlp.add_pipe` will add
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components to the end of the pipeline and after all other components.
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</Infobox>
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| Name | Description |
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| ----------- | -------------------------------------------------------------------- |
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| `doc` | The `Doc` object to process, e.g. the `Doc` in the pipeline. ~~Doc~~ |
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| **RETURNS** | The modified `Doc` with merged entities. ~~Doc~~ |
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## merge_subtokens {id="merge_subtokens",tag="function",version="2.1"}
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Merge subtokens into a single token. Also available via the string name
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`"merge_subtokens"`. As of v2.1, the parser is able to predict "subtokens" that
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should be merged into one single token later on. This is especially relevant for
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languages like Chinese, Japanese or Korean, where a "word" isn't defined as a
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whitespace-delimited sequence of characters. Under the hood, this component uses
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the [`Matcher`](/api/matcher) to find sequences of tokens with the dependency
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label `"subtok"` and then merges them into a single token.
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> #### Example
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>
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> Note that this example assumes a custom Chinese model that oversegments and
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> was trained to predict subtokens.
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>
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> ```python
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> doc = nlp("拜托")
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> print([(token.text, token.dep_) for token in doc])
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> # [('拜', 'subtok'), ('托', 'subtok')]
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>
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> nlp.add_pipe("merge_subtokens")
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> doc = nlp("拜托")
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> print([token.text for token in doc])
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> # ['拜托']
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> ```
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<Infobox variant="warning">
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Since subtokens are set by the parser, make sure to add this component _after_
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the `"parser"` component. By default, `nlp.add_pipe` will add components to the
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end of the pipeline and after all other components.
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</Infobox>
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| Name | Description |
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| ----------- | -------------------------------------------------------------------- |
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| `doc` | The `Doc` object to process, e.g. the `Doc` in the pipeline. ~~Doc~~ |
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| `label` | The subtoken dependency label. Defaults to `"subtok"`. ~~str~~ |
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| **RETURNS** | The modified `Doc` with merged subtokens. ~~Doc~~ |
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## token_splitter {id="token_splitter",tag="function",version="3.0"}
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Split tokens longer than a minimum length into shorter tokens. Intended for use
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with transformer pipelines where long spaCy tokens lead to input text that
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exceed the transformer model max length.
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> #### Example
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>
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> ```python
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> config = {"min_length": 20, "split_length": 5}
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> nlp.add_pipe("token_splitter", config=config, first=True)
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> doc = nlp("aaaaabbbbbcccccdddddee")
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> print([token.text for token in doc])
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> # ['aaaaa', 'bbbbb', 'ccccc', 'ddddd', 'ee']
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> ```
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| Setting | Description |
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| -------------- | --------------------------------------------------------------------- |
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| `min_length` | The minimum length for a token to be split. Defaults to `25`. ~~int~~ |
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| `split_length` | The length of the split tokens. Defaults to `5`. ~~int~~ |
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| **RETURNS** | The modified `Doc` with the split tokens. ~~Doc~~ |
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## doc_cleaner {id="doc_cleaner",tag="function",version="3.2.1"}
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Clean up `Doc` attributes. Intended for use at the end of pipelines with
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`tok2vec` or `transformer` pipeline components that store tensors and other
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values that can require a lot of memory and frequently aren't needed after the
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whole pipeline has run.
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> #### Example
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>
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> ```python
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> config = {"attrs": {"tensor": None}}
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> nlp.add_pipe("doc_cleaner", config=config)
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> doc = nlp("text")
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> assert doc.tensor is None
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> ```
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| Setting | Description |
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| ----------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
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| `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~~ |
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| `silent` | If `False`, show warnings if attributes aren't found or can't be set. Defaults to `True`. ~~bool~~ |
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| **RETURNS** | The modified `Doc` with the modified attributes. ~~Doc~~ |
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## span_cleaner {id="span_cleaner",tag="function,experimental"}
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Remove `SpanGroup`s from `doc.spans` based on a key prefix. This is used to
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clean up after the [`CoreferenceResolver`](/api/coref) when it's paired with a
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[`SpanResolver`](/api/span-resolver).
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<Infobox title="Important note" variant="warning">
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This pipeline function is not yet integrated into spaCy core, and is available
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via the extension package
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[`spacy-experimental`](https://github.com/explosion/spacy-experimental) starting
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in version 0.6.0. It exposes the component via
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[entry points](/usage/saving-loading/#entry-points), so if you have the package
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installed, using `factory = "span_cleaner"` in your
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[training config](/usage/training#config) or `nlp.add_pipe("span_cleaner")` will
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work out-of-the-box.
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</Infobox>
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> #### Example
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>
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> ```python
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> config = {"prefix": "coref_head_clusters"}
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> nlp.add_pipe("span_cleaner", config=config)
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> doc = nlp("text")
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> assert "coref_head_clusters_1" not in doc.spans
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> ```
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| Setting | Description |
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| ----------- | ------------------------------------------------------------------------------------------------------------------------- |
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| `prefix` | A prefix to check `SpanGroup` keys for. Any matching groups will be removed. Defaults to `"coref_head_clusters"`. ~~str~~ |
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| **RETURNS** | The modified `Doc` with any matching spans removed. ~~Doc~~ |
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