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.

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.

Setting	Description
`punct_chars`	Optional custom list of punctuation characters that mark sentence ends. See below for defaults if not set. Defaults to `None`. `Optional[List[str]]`

explosion/spaCy/master/spacy/pipeline/sentencizer.pyx
Can't fetch code example from GitHub :(

Please use the link below to view the example. If you've come across
a broken link, we always appreciate a pull request to the repository,
or a report on the issue tracker. Thanks!

Sentencizer.init method

Initialize the sentencizer.

Name	Description
keyword-only
`punct_chars`	Optional custom list of punctuation characters that mark sentence ends. See below for defaults. `Optional[List[str]]`

punct_chars defaults
['!', '.', '?', '։', '؟', '۔', '܀', '܁', '܂', '߹', '।', '॥', '၊', '။', '።',
 '፧', '፨', '᙮', '᜵', '᜶', '᠃', '᠉', '᥄', '᥅', '᪨', '᪩', '᪪', '᪫',
 '᭚', '᭛', '᭞', '᭟', '᰻', '᰼', '᱾', '᱿', '‼', '‽', '⁇', '⁈', '⁉',
 '⸮', '⸼', '꓿', '꘎', '꘏', '꛳', '꛷', '꡶', '꡷', '꣎', '꣏', '꤯', '꧈',
 '꧉', '꩝', '꩞', '꩟', '꫰', '꫱', '꯫', '﹒', '﹖', '﹗', '！', '．', '？',
 '𐩖', '𐩗', '𑁇', '𑁈', '𑂾', '𑂿', '𑃀', '𑃁', '𑅁', '𑅂', '𑅃', '𑇅',
 '𑇆', '𑇍', '𑇞', '𑇟', '𑈸', '𑈹', '𑈻', '𑈼', '𑊩', '𑑋', '𑑌', '𑗂',
 '𑗃', '𑗉', '𑗊', '𑗋', '𑗌', '𑗍', '𑗎', '𑗏', '𑗐', '𑗑', '𑗒', '𑗓',
 '𑗔', '𑗕', '𑗖', '𑗗', '𑙁', '𑙂', '𑜼', '𑜽', '𑜾', '𑩂', '𑩃', '𑪛',
 '𑪜', '𑱁', '𑱂', '𖩮', '𖩯', '𖫵', '𖬷', '𖬸', '𖭄', '𛲟', '𝪈', '｡', '。']

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.

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

Sentencizer.pipe method

Apply the pipe to a stream of documents. This usually happens under the hood when the nlp object is called on a text and all pipeline components are applied to the Doc in order.

Name	Description
`stream`	A stream of documents. `Iterable[Doc]`
keyword-only
`batch_size`	The number of documents to buffer. Defaults to `128`. `int`
YIELDS	The processed documents in order. `Doc`

Sentencizer.score methodv3.0

Score a batch of examples.

Name	Description
`examples`	The examples to score. `Iterable[Example]`
RETURNS	The scores, produced by `Scorer.score_spans`. `Dict[str, Union[float, Dict[str, float]]`

Sentencizer.to_disk method

Save the sentencizer settings (punctuation characters) to 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	Description
`path`	A path to a JSON file, which will be created if it doesn’t exist. Paths may be either strings or `Path`-like objects. `Union[str, Path]`

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	Description
`path`	A path to a JSON file. Paths may be either strings or `Path`-like objects. `Union[str, Path]`
RETURNS	The modified `Sentencizer` object. `Sentencizer`

Sentencizer.to_bytes method

Serialize the sentencizer settings to a bytestring.

Name	Description
RETURNS	The serialized data. `bytes`

Sentencizer.from_bytes method

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

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