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199 lines
8.4 KiB
Markdown
199 lines
8.4 KiB
Markdown
---
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title: Sentencizer
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tag: class
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source: spacy/pipeline/sentencizer.pyx
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teaser: 'Pipeline component for rule-based sentence boundary detection'
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api_base_class: /api/pipe
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api_string_name: sentencizer
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api_trainable: false
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---
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A simple pipeline component, to allow custom sentence boundary detection logic
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that doesn't require the dependency parse. By default, sentence segmentation is
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performed by the [`DependencyParser`](/api/dependencyparser), so the
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`Sentencizer` lets you implement a simpler, rule-based strategy that doesn't
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require a statistical model to be loaded.
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## Config and implementation {#config}
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The default config is defined by the pipeline component factory and describes
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how the component should be configured. You can override its settings via the
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`config` argument on [`nlp.add_pipe`](/api/language#add_pipe) or in your
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[`config.cfg` for training](/usage/training#config).
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> #### Example
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>
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> ```python
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> config = {"punct_chars": None}
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> nlp.add_pipe("entity_ruler", config=config)
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> ```
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| Setting | Description |
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| ------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------ |
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| `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]]~~ | `None` |
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```python
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https://github.com/explosion/spaCy/blob/develop/spacy/pipeline/sentencizer.pyx
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```
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## Sentencizer.\_\_init\_\_ {#init tag="method"}
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Initialize the sentencizer.
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> #### Example
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>
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> ```python
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> # Construction via add_pipe
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> sentencizer = nlp.add_pipe("sentencizer")
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>
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> # Construction from class
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> from spacy.pipeline import Sentencizer
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> sentencizer = Sentencizer()
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> ```
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| Name | Description |
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| -------------- | ----------------------------------------------------------------------------------------------------------------------- |
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| _keyword-only_ | | |
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| `punct_chars` | Optional custom list of punctuation characters that mark sentence ends. See below for defaults. ~~Optional[List[str]]~~ |
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```python
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### punct_chars defaults
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['!', '.', '?', '։', '؟', '۔', '܀', '܁', '܂', '߹', '।', '॥', '၊', '။', '።',
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'፧', '፨', '᙮', '᜵', '᜶', '᠃', '᠉', '᥄', '᥅', '᪨', '᪩', '᪪', '᪫',
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'᭚', '᭛', '᭞', '᭟', '᰻', '᰼', '᱾', '᱿', '‼', '‽', '⁇', '⁈', '⁉',
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'⸮', '⸼', '꓿', '꘎', '꘏', '꛳', '꛷', '꡶', '꡷', '꣎', '꣏', '꤯', '꧈',
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'꧉', '꩝', '꩞', '꩟', '꫰', '꫱', '꯫', '﹒', '﹖', '﹗', '！', '．', '？',
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'𐩖', '𐩗', '𑁇', '𑁈', '𑂾', '𑂿', '𑃀', '𑃁', '𑅁', '𑅂', '𑅃', '𑇅',
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'𑇆', '𑇍', '𑇞', '𑇟', '𑈸', '𑈹', '𑈻', '𑈼', '𑊩', '𑑋', '𑑌', '𑗂',
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'𑗃', '𑗉', '𑗊', '𑗋', '𑗌', '𑗍', '𑗎', '𑗏', '𑗐', '𑗑', '𑗒', '𑗓',
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'𑗔', '𑗕', '𑗖', '𑗗', '𑙁', '𑙂', '𑜼', '𑜽', '𑜾', '𑩂', '𑩃', '𑪛',
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'𑪜', '𑱁', '𑱂', '𖩮', '𖩯', '𖫵', '𖬷', '𖬸', '𖭄', '𛲟', '𝪈', '｡', '。']
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```
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## Sentencizer.\_\_call\_\_ {#call tag="method"}
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Apply the sentencizer on a `Doc`. Typically, this happens automatically after
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the component has been added to the pipeline using
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[`nlp.add_pipe`](/api/language#add_pipe).
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> #### Example
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>
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> ```python
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> from spacy.lang.en import English
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>
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> nlp = English()
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> nlp.add_pipe("sentencizer")
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> doc = nlp("This is a sentence. This is another sentence.")
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> assert len(list(doc.sents)) == 2
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> ```
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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 added sentence boundaries. ~~Doc~~ |
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## Sentencizer.pipe {#pipe tag="method"}
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Apply the pipe to a stream of documents. This usually happens under the hood
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when the `nlp` object is called on a text and all pipeline components are
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applied to the `Doc` in order.
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> #### Example
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>
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> ```python
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> sentencizer = nlp.add_pipe("sentencizer")
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> for doc in sentencizer.pipe(docs, batch_size=50):
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> pass
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> ```
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| Name | Description |
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| -------------- | ------------------------------------------------------------- |
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| `stream` | A stream of documents. ~~Iterable[Doc]~~ |
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| _keyword-only_ | |
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| `batch_size` | The number of documents to buffer. Defaults to `128`. ~~int~~ |
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| **YIELDS** | The processed documents in order. ~~Doc~~ |
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## Sentencizer.score {#score tag="method" new="3"}
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Score a batch of examples.
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> #### Example
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>
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> ```python
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> scores = sentencizer.score(examples)
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> ```
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| Name | Description |
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| ----------- | --------------------------------------------------------------------------------------------------------------------- |
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| `examples` | The examples to score. ~~Iterable[Example]~~ |
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| **RETURNS** | The scores, produced by [`Scorer.score_spans`](/api/scorer#score_spans). ~~Dict[str, Union[float, Dict[str, float]]~~ |
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## Sentencizer.to_disk {#to_disk tag="method"}
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Save the sentencizer settings (punctuation characters) a directory. Will create
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a file `sentencizer.json`. This also happens automatically when you save an
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`nlp` object with a sentencizer added to its pipeline.
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> #### Example
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>
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> ```python
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> config = {"punct_chars": [".", "?", "!", "。"]}
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> sentencizer = nlp.add_pipe("sentencizer", config=config)
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> sentencizer.to_disk("/path/to/sentencizer.json")
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> ```
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| Name | Description |
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| ------ | ------------------------------------------------------------------------------------------------------------------------------------------ |
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| `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]~~ |
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## Sentencizer.from_disk {#from_disk tag="method"}
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Load the sentencizer settings from a file. Expects a JSON file. This also
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happens automatically when you load an `nlp` object or model with a sentencizer
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added to its pipeline.
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> #### Example
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>
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> ```python
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> sentencizer = nlp.add_pipe("sentencizer")
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> sentencizer.from_disk("/path/to/sentencizer.json")
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> ```
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| Name | Description |
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| ----------- | ----------------------------------------------------------------------------------------------- |
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| `path` | A path to a JSON file. Paths may be either strings or `Path`-like objects. ~~Union[str, Path]~~ |
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| **RETURNS** | The modified `Sentencizer` object. ~~Sentencizer~~ |
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## Sentencizer.to_bytes {#to_bytes tag="method"}
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Serialize the sentencizer settings to a bytestring.
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> #### Example
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>
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> ```python
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> config = {"punct_chars": [".", "?", "!", "。"]}
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> sentencizer = nlp.add_pipe("sentencizer", config=config)
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> sentencizer_bytes = sentencizer.to_bytes()
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> ```
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| Name | Description |
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| ----------- | ------------------------------ |
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| **RETURNS** | The serialized data. ~~bytes~~ |
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## Sentencizer.from_bytes {#from_bytes tag="method"}
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Load the pipe from a bytestring. Modifies the object in place and returns it.
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> #### Example
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>
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> ```python
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> sentencizer_bytes = sentencizer.to_bytes()
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> sentencizer = nlp.add_pipe("sentencizer")
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> sentencizer.from_bytes(sentencizer_bytes)
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> ```
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| Name | Description |
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| ------------ | -------------------------------------------------- |
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| `bytes_data` | The bytestring to load. ~~bytes~~ |
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| **RETURNS** | The modified `Sentencizer` object. ~~Sentencizer~~ |
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