Sentencizer · spaCy API Documentation

A simple pipeline component, to allow custom sentence boundary detection logic that doesn’t require the dependency parse. By default, sentence segmentation is performed by the DependencyParser, so the Sentencizer lets you implement a simpler, rule-based strategy that doesn’t require a statistical model to be loaded. The component is also available via the string name "sentencizer". After initialization, it is typically added to the processing pipeline using nlp.add_pipe.

Important note

Compared to the previous SentenceSegmenter class, the Sentencizer component doesn’t add a hook to doc.user_hooks["sents"]. Instead, it iterates over the tokens in the Doc and sets the Token.is_sent_start property. The SentenceSegmenter is still available if you import it directly:

from spacy.pipeline import SentenceSegmenter

Sentencizer.init method

Initialize the sentencizer.

Name	Type	Description
`punct_chars`	list	Optional custom list of punctuation characters that mark sentence ends. Defaults to `[".", "!", "?"].`
RETURNS	`Sentencizer`	The newly constructed object.

Sentencizer.call method

Apply the sentencizer on a Doc. Typically, this happens automatically after the component has been added to the pipeline using nlp.add_pipe.

Example

from spacy.lang.en import English

nlp = English()
sentencizer = nlp.create_pipe("sentencizer")
nlp.add_pipe(sentencizer)
doc = nlp("This is a sentence. This is another sentence.")
assert len(list(doc.sents)) == 2

Name	Type	Description
`doc`	`Doc`	The `Doc` object to process, e.g. the `Doc` in the pipeline.
RETURNS	`Doc`	The modified `Doc` with added sentence boundaries.

Sentencizer.to_disk method

Save the sentencizer settings (punctuation characters) a directory. Will create a file sentencizer.json. This also happens automatically when you save an nlp object with a sentencizer added to its pipeline.

Name	Type	Description
`path`	unicode / `Path`	A path to a file, which will be created if it doesn’t exist. Paths may be either strings or `Path`-like objects.

Sentencizer.from_disk method

Load the sentencizer settings from a file. Expects a JSON file. This also happens automatically when you load an nlp object or model with a sentencizer added to its pipeline.

Name	Type	Description
`path`	unicode / `Path`	A path to a JSON file. Paths may be either strings or `Path`-like objects.
RETURNS	`Sentencizer`	The modified `Sentencizer` object.

Sentencizer.to_bytes method

Serialize the sentencizer settings to a bytestring.

Name	Type	Description
RETURNS	bytes	The serialized data.

Sentencizer.from_bytes method

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

Name	Type	Description
`bytes_data`	bytes	The bytestring to load.
RETURNS	`Sentencizer`	The modified `Sentencizer` object.

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2019	2020	2021