mirror of https://github.com/explosion/spaCy.git
199 lines
8.4 KiB
Markdown
199 lines
8.4 KiB
Markdown
---
|
||
title: Sentencizer
|
||
tag: class
|
||
source: spacy/pipeline/sentencizer.pyx
|
||
teaser: 'Pipeline component for rule-based sentence boundary detection'
|
||
api_base_class: /api/pipe
|
||
api_string_name: sentencizer
|
||
api_trainable: false
|
||
---
|
||
|
||
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`](/api/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 {#config}
|
||
|
||
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`](/api/language#add_pipe) or in your
|
||
[`config.cfg` for training](/usage/training#config).
|
||
|
||
> #### Example
|
||
>
|
||
> ```python
|
||
> config = {"punct_chars": None}
|
||
> nlp.add_pipe("entity_ruler", config=config)
|
||
> ```
|
||
|
||
| 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]]~~ | `None` |
|
||
|
||
```python
|
||
%%GITHUB_SPACY/spacy/pipeline/sentencizer.pyx
|
||
```
|
||
|
||
## Sentencizer.\_\_init\_\_ {#init tag="method"}
|
||
|
||
Initialize the sentencizer.
|
||
|
||
> #### Example
|
||
>
|
||
> ```python
|
||
> # Construction via add_pipe
|
||
> sentencizer = nlp.add_pipe("sentencizer")
|
||
>
|
||
> # Construction from class
|
||
> from spacy.pipeline import Sentencizer
|
||
> sentencizer = Sentencizer()
|
||
> ```
|
||
|
||
| Name | Description |
|
||
| -------------- | ----------------------------------------------------------------------------------------------------------------------- |
|
||
| _keyword-only_ | |
|
||
| `punct_chars` | Optional custom list of punctuation characters that mark sentence ends. See below for defaults. ~~Optional[List[str]]~~ |
|
||
|
||
```python
|
||
### punct_chars defaults
|
||
['!', '.', '?', '։', '؟', '۔', '܀', '܁', '܂', '߹', '।', '॥', '၊', '။', '።',
|
||
'፧', '፨', '᙮', '᜵', '᜶', '᠃', '᠉', '᥄', '᥅', '᪨', '᪩', '᪪', '᪫',
|
||
'᭚', '᭛', '᭞', '᭟', '᰻', '᰼', '᱾', '᱿', '‼', '‽', '⁇', '⁈', '⁉',
|
||
'⸮', '⸼', '꓿', '꘎', '꘏', '꛳', '꛷', '꡶', '꡷', '꣎', '꣏', '꤯', '꧈',
|
||
'꧉', '꩝', '꩞', '꩟', '꫰', '꫱', '꯫', '﹒', '﹖', '﹗', '！', '．', '？',
|
||
'𐩖', '𐩗', '𑁇', '𑁈', '𑂾', '𑂿', '𑃀', '𑃁', '𑅁', '𑅂', '𑅃', '𑇅',
|
||
'𑇆', '𑇍', '𑇞', '𑇟', '𑈸', '𑈹', '𑈻', '𑈼', '𑊩', '𑑋', '𑑌', '𑗂',
|
||
'𑗃', '𑗉', '𑗊', '𑗋', '𑗌', '𑗍', '𑗎', '𑗏', '𑗐', '𑗑', '𑗒', '𑗓',
|
||
'𑗔', '𑗕', '𑗖', '𑗗', '𑙁', '𑙂', '𑜼', '𑜽', '𑜾', '𑩂', '𑩃', '𑪛',
|
||
'𑪜', '𑱁', '𑱂', '𖩮', '𖩯', '𖫵', '𖬷', '𖬸', '𖭄', '𛲟', '𝪈', '｡', '。']
|
||
```
|
||
|
||
## Sentencizer.\_\_call\_\_ {#call tag="method"}
|
||
|
||
Apply the sentencizer on a `Doc`. Typically, this happens automatically after
|
||
the component has been added to the pipeline using
|
||
[`nlp.add_pipe`](/api/language#add_pipe).
|
||
|
||
> #### Example
|
||
>
|
||
> ```python
|
||
> from spacy.lang.en import English
|
||
>
|
||
> nlp = English()
|
||
> nlp.add_pipe("sentencizer")
|
||
> doc = nlp("This is a sentence. This is another sentence.")
|
||
> assert len(list(doc.sents)) == 2
|
||
> ```
|
||
|
||
| 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 {#pipe tag="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.
|
||
|
||
> #### Example
|
||
>
|
||
> ```python
|
||
> sentencizer = nlp.add_pipe("sentencizer")
|
||
> for doc in sentencizer.pipe(docs, batch_size=50):
|
||
> pass
|
||
> ```
|
||
|
||
| 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 {#score tag="method" new="3"}
|
||
|
||
Score a batch of examples.
|
||
|
||
> #### Example
|
||
>
|
||
> ```python
|
||
> scores = sentencizer.score(examples)
|
||
> ```
|
||
|
||
| Name | Description |
|
||
| ----------- | --------------------------------------------------------------------------------------------------------------------- |
|
||
| `examples` | The examples to score. ~~Iterable[Example]~~ |
|
||
| **RETURNS** | The scores, produced by [`Scorer.score_spans`](/api/scorer#score_spans). ~~Dict[str, Union[float, Dict[str, float]]~~ |
|
||
|
||
## Sentencizer.to_disk {#to_disk tag="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.
|
||
|
||
> #### Example
|
||
>
|
||
> ```python
|
||
> config = {"punct_chars": [".", "?", "!", "。"]}
|
||
> sentencizer = nlp.add_pipe("sentencizer", config=config)
|
||
> sentencizer.to_disk("/path/to/sentencizer.json")
|
||
> ```
|
||
|
||
| 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 {#from_disk tag="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.
|
||
|
||
> #### Example
|
||
>
|
||
> ```python
|
||
> sentencizer = nlp.add_pipe("sentencizer")
|
||
> sentencizer.from_disk("/path/to/sentencizer.json")
|
||
> ```
|
||
|
||
| 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 {#to_bytes tag="method"}
|
||
|
||
Serialize the sentencizer settings to a bytestring.
|
||
|
||
> #### Example
|
||
>
|
||
> ```python
|
||
> config = {"punct_chars": [".", "?", "!", "。"]}
|
||
> sentencizer = nlp.add_pipe("sentencizer", config=config)
|
||
> sentencizer_bytes = sentencizer.to_bytes()
|
||
> ```
|
||
|
||
| Name | Description |
|
||
| ----------- | ------------------------------ |
|
||
| **RETURNS** | The serialized data. ~~bytes~~ |
|
||
|
||
## Sentencizer.from_bytes {#from_bytes tag="method"}
|
||
|
||
Load the pipe from a bytestring. Modifies the object in place and returns it.
|
||
|
||
> #### Example
|
||
>
|
||
> ```python
|
||
> sentencizer_bytes = sentencizer.to_bytes()
|
||
> sentencizer = nlp.add_pipe("sentencizer")
|
||
> sentencizer.from_bytes(sentencizer_bytes)
|
||
> ```
|
||
|
||
| Name | Description |
|
||
| ------------ | -------------------------------------------------- |
|
||
| `bytes_data` | The bytestring to load. ~~bytes~~ |
|
||
| **RETURNS** | The modified `Sentencizer` object. ~~Sentencizer~~ |
|