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06bf130890
* Add better serializable sentencizer component * Replace default factory * Add tests * Tidy up * Pass test * Update docs
137 lines
5.2 KiB
Markdown
137 lines
5.2 KiB
Markdown
---
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title: Sentencizer
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tag: class
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source: spacy/pipeline/pipes.pyx
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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. The component is also available via
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the string name `"sentencizer"`. After initialization, it is typically added to
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the processing pipeline using [`nlp.add_pipe`](/api/language#add_pipe).
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<Infobox title="Important note" variant="warning">
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Compared to the previous `SentenceSegmenter` class, the `Sentencizer` component
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doesn't add a hook to `doc.user_hooks["sents"]`. Instead, it iterates over the
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tokens in the `Doc` and sets the `Token.is_sent_start` property. The
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`SentenceSegmenter` is still available if you import it directly:
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```python
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from spacy.pipeline import SentenceSegmenter
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```
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</Infobox>
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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 create_pipe
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> sentencizer = nlp.create_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 | Type | Description |
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| ------------- | ------------- | ------------------------------------------------------------------------------------------------------ |
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| `punct_chars` | list | Optional custom list of punctuation characters that mark sentence ends. Defaults to `[".", "!", "?"].` |
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| **RETURNS** | `Sentencizer` | The newly constructed object. |
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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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> sentencizer = nlp.create_pipe("sentencizer")
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> nlp.add_pipe(sentencizer)
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> doc = nlp(u"This is a sentence. This is another sentence.")
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> assert list(doc.sents) == 2
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> ```
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| Name | Type | Description |
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| ----------- | ----- | ------------------------------------------------------------ |
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| `doc` | `Doc` | The `Doc` object to process, e.g. the `Doc` in the pipeline. |
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| **RETURNS** | `Doc` | The modified `Doc` with added sentence boundaries. |
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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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> sentencizer = Sentencizer(punct_chars=[".", "?", "!", "。"])
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> sentencizer.to_disk("/path/to/sentencizer.jsonl")
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> ```
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| Name | Type | Description |
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| ------ | ---------------- | ---------------------------------------------------------------------------------------------------------------- |
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| `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. |
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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 = Sentencizer()
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> sentencizer.from_disk("/path/to/sentencizer.json")
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> ```
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| Name | Type | Description |
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| ----------- | ---------------- | -------------------------------------------------------------------------- |
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| `path` | unicode / `Path` | A path to a JSON file. Paths may be either strings or `Path`-like objects. |
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| **RETURNS** | `Sentencizer` | The modified `Sentencizer` object. |
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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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> sentencizer = Sentencizer(punct_chars=[".", "?", "!", "。"])
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> sentencizer_bytes = sentencizer.to_bytes()
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> ```
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| Name | Type | Description |
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| ----------- | ----- | -------------------- |
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| **RETURNS** | bytes | The serialized data. |
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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 = Sentencizer()
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> sentencizer.from_bytes(sentencizer_bytes)
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> ```
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| Name | Type | Description |
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| ------------ | ------------- | ---------------------------------- |
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| `bytes_data` | bytes | The bytestring to load. |
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| **RETURNS** | `Sentencizer` | The modified `Sentencizer` object. |
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