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6b07be2110
* Add `Language.distill` This method is the distillation counterpart of `Language.update`. It takes a teacher `Language` instance and distills the student pipes on the teacher pipes. * Apply suggestions from code review Co-authored-by: Madeesh Kannan <shadeMe@users.noreply.github.com> * Clarify that how Example is used in distillation * Update transition parser distill docstring for examples argument * Pass optimizer to `TrainablePipe.distill` * Annotate pipe before update As discussed internally, we want to let a pipe annotate before doing an update with gold/silver data. Otherwise, the output may be (too) informed by the gold/silver data. * Rename `component_map` to `student_to_teacher` * Better synopsis in `Language.distill` docstring * `name` -> `student_name` * Fix labels type in docstring * Mark distill test as slow * Fix `student_to_teacher` type in docs --------- Co-authored-by: Madeesh Kannan <shadeMe@users.noreply.github.com>
431 lines
23 KiB
Plaintext
431 lines
23 KiB
Plaintext
---
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title: SentenceRecognizer
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tag: class
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source: spacy/pipeline/senter.pyx
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version: 3
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teaser: 'Pipeline component for sentence segmentation'
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api_base_class: /api/tagger
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api_string_name: senter
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api_trainable: true
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---
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A trainable pipeline component for sentence segmentation. For a simpler,
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rule-based strategy, see the [`Sentencizer`](/api/sentencizer).
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## Assigned Attributes {id="assigned-attributes"}
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Predicted values will be assigned to `Token.is_sent_start`. The resulting
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sentences can be accessed using `Doc.sents`.
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| Location | Value |
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| --------------------- | ------------------------------------------------------------------------------------------------------------------------------ |
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| `Token.is_sent_start` | A boolean value indicating whether the token starts a sentence. This will be either `True` or `False` for all tokens. ~~bool~~ |
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| `Doc.sents` | An iterator over sentences in the `Doc`, determined by `Token.is_sent_start` values. ~~Iterator[Span]~~ |
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## Config and implementation {id="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). See the
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[model architectures](/api/architectures) documentation for details on the
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architectures and their arguments and hyperparameters.
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> #### Example
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>
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> ```python
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> from spacy.pipeline.senter import DEFAULT_SENTER_MODEL
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> config = {"model": DEFAULT_SENTER_MODEL,}
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> nlp.add_pipe("senter", config=config)
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> ```
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| Setting | Description |
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| ----------------------------------------------- | --------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
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| `model` | The [`Model`](https://thinc.ai/docs/api-model) powering the pipeline component. Defaults to [Tagger](/api/architectures#Tagger). ~~Model[List[Doc], List[Floats2d]]~~ |
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| `overwrite` <Tag variant="new">3.2</Tag> | Whether existing annotation is overwritten. Defaults to `False`. ~~bool~~ |
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| `scorer` <Tag variant="new">3.2</Tag> | The scoring method. Defaults to [`Scorer.score_spans`](/api/scorer#score_spans) for the attribute `"sents"`. ~~Optional[Callable]~~ |
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| `save_activations` <Tag variant="new">4.0</Tag> | Save activations in `Doc` when annotating. Saved activations are `"probabilities"` and `"label_ids"`. ~~Union[bool, list[str]]~~ |
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```python
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%%GITHUB_SPACY/spacy/pipeline/senter.pyx
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```
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## SentenceRecognizer.\_\_init\_\_ {id="init",tag="method"}
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Initialize the sentence recognizer.
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> #### Example
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>
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> ```python
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> # Construction via add_pipe with default model
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> senter = nlp.add_pipe("senter")
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>
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> # Construction via create_pipe with custom model
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> config = {"model": {"@architectures": "my_senter"}}
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> senter = nlp.add_pipe("senter", config=config)
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>
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> # Construction from class
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> from spacy.pipeline import SentenceRecognizer
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> senter = SentenceRecognizer(nlp.vocab, model)
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> ```
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Create a new pipeline instance. In your application, you would normally use a
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shortcut for this and instantiate the component using its string name and
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[`nlp.add_pipe`](/api/language#add_pipe).
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| Name | Description |
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| ---------------------------------------- | ----------------------------------------------------------------------------------------------------------------------------------- |
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| `vocab` | The shared vocabulary. ~~Vocab~~ |
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| `model` | The [`Model`](https://thinc.ai/docs/api-model) powering the pipeline component. ~~Model[List[Doc], List[Floats2d]]~~ |
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| `name` | String name of the component instance. Used to add entries to the `losses` during training. ~~str~~ |
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| _keyword-only_ | |
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| `overwrite` <Tag variant="new">3.2</Tag> | Whether existing annotation is overwritten. Defaults to `False`. ~~bool~~ |
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| `scorer` <Tag variant="new">3.2</Tag> | The scoring method. Defaults to [`Scorer.score_spans`](/api/scorer#score_spans) for the attribute `"sents"`. ~~Optional[Callable]~~ |
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## SentenceRecognizer.\_\_call\_\_ {id="call",tag="method"}
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Apply the pipe to one document. The document is modified in place, and returned.
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This usually happens under the hood when the `nlp` object is called on a text
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and all pipeline components are applied to the `Doc` in order. Both
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[`__call__`](/api/sentencerecognizer#call) and
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[`pipe`](/api/sentencerecognizer#pipe) delegate to the
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[`predict`](/api/sentencerecognizer#predict) and
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[`set_annotations`](/api/sentencerecognizer#set_annotations) methods.
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> #### Example
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>
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> ```python
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> doc = nlp("This is a sentence.")
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> senter = nlp.add_pipe("senter")
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> # This usually happens under the hood
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> processed = senter(doc)
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> ```
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| Name | Description |
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| ----------- | -------------------------------- |
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| `doc` | The document to process. ~~Doc~~ |
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| **RETURNS** | The processed document. ~~Doc~~ |
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## SentenceRecognizer.distill {id="distill", tag="method,experimental", version="4"}
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Train a pipe (the student) on the predictions of another pipe (the teacher). The
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student is typically trained on the probability distribution of the teacher, but
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details may differ per pipe. The goal of distillation is to transfer knowledge
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from the teacher to the student.
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The distillation is performed on ~~Example~~ objects. The `Example.reference`
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and `Example.predicted` ~~Doc~~s must have the same number of tokens and the
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same orthography. Even though the reference does not need have to have gold
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annotations, the teacher could adds its own annotations when necessary.
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This feature is experimental.
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> #### Example
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>
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> ```python
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> teacher_pipe = teacher.add_pipe("senter")
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> student_pipe = student.add_pipe("senter")
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> optimizer = nlp.resume_training()
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> losses = student.distill(teacher_pipe, examples, sgd=optimizer)
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> ```
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| Name | Description |
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| -------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
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| `teacher_pipe` | The teacher pipe to learn from. ~~Optional[TrainablePipe]~~ |
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| `examples` | A batch of [`Example`](/api/example) distillation examples. The reference (teacher) and predicted (student) docs must have the same number of tokens and orthography. ~~Iterable[Example]~~ |
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| _keyword-only_ | |
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| `drop` | Dropout rate. ~~float~~ |
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| `sgd` | An optimizer. Will be created via [`create_optimizer`](#create_optimizer) if not set. ~~Optional[Optimizer]~~ |
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| `losses` | Optional record of the loss during distillation. Updated using the component name as the key. ~~Optional[Dict[str, float]]~~ |
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| **RETURNS** | The updated `losses` dictionary. ~~Dict[str, float]~~ |
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## SentenceRecognizer.pipe {id="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. Both [`__call__`](/api/sentencerecognizer#call)
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and [`pipe`](/api/sentencerecognizer#pipe) delegate to the
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[`predict`](/api/sentencerecognizer#predict) and
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[`set_annotations`](/api/sentencerecognizer#set_annotations) methods.
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> #### Example
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>
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> ```python
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> senter = nlp.add_pipe("senter")
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> for doc in senter.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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## SentenceRecognizer.initialize {id="initialize",tag="method"}
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Initialize the component for training. `get_examples` should be a function that
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returns an iterable of [`Example`](/api/example) objects. **At least one example
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should be supplied.** The data examples are used to **initialize the model** of
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the component and can either be the full training data or a representative
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sample. Initialization includes validating the network,
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[inferring missing shapes](https://thinc.ai/docs/usage-models#validation) and
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setting up the label scheme based on the data. This method is typically called
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by [`Language.initialize`](/api/language#initialize).
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> #### Example
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>
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> ```python
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> senter = nlp.add_pipe("senter")
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> senter.initialize(lambda: examples, nlp=nlp)
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> ```
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| Name | Description |
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| -------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
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| `get_examples` | Function that returns gold-standard annotations in the form of [`Example`](/api/example) objects. Must contain at least one `Example`. ~~Callable[[], Iterable[Example]]~~ |
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| _keyword-only_ | |
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| `nlp` | The current `nlp` object. Defaults to `None`. ~~Optional[Language]~~ |
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## SentenceRecognizer.predict {id="predict",tag="method"}
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Apply the component's model to a batch of [`Doc`](/api/doc) objects, without
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modifying them.
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> #### Example
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>
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> ```python
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> senter = nlp.add_pipe("senter")
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> scores = senter.predict([doc1, doc2])
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> ```
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| Name | Description |
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| ----------- | ------------------------------------------- |
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| `docs` | The documents to predict. ~~Iterable[Doc]~~ |
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| **RETURNS** | The model's prediction for each document. |
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## SentenceRecognizer.set_annotations {id="set_annotations",tag="method"}
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Modify a batch of [`Doc`](/api/doc) objects, using pre-computed scores.
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> #### Example
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>
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> ```python
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> senter = nlp.add_pipe("senter")
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> scores = senter.predict([doc1, doc2])
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> senter.set_annotations([doc1, doc2], scores)
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> ```
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| Name | Description |
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| -------- | ------------------------------------------------------------ |
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| `docs` | The documents to modify. ~~Iterable[Doc]~~ |
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| `scores` | The scores to set, produced by `SentenceRecognizer.predict`. |
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## SentenceRecognizer.update {id="update",tag="method"}
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Learn from a batch of [`Example`](/api/example) objects containing the
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predictions and gold-standard annotations, and update the component's model.
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Delegates to [`predict`](/api/sentencerecognizer#predict) and
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[`get_loss`](/api/sentencerecognizer#get_loss).
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> #### Example
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>
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> ```python
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> senter = nlp.add_pipe("senter")
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> optimizer = nlp.initialize()
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> losses = senter.update(examples, sgd=optimizer)
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> ```
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| Name | Description |
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| -------------- | ------------------------------------------------------------------------------------------------------------------------ |
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| `examples` | A batch of [`Example`](/api/example) objects to learn from. ~~Iterable[Example]~~ |
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| _keyword-only_ | |
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| `drop` | The dropout rate. ~~float~~ |
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| `sgd` | An optimizer. Will be created via [`create_optimizer`](#create_optimizer) if not set. ~~Optional[Optimizer]~~ |
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| `losses` | Optional record of the loss during training. Updated using the component name as the key. ~~Optional[Dict[str, float]]~~ |
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| **RETURNS** | The updated `losses` dictionary. ~~Dict[str, float]~~ |
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## SentenceRecognizer.rehearse {id="rehearse",tag="method,experimental",version="3"}
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Perform a "rehearsal" update from a batch of data. Rehearsal updates teach the
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current model to make predictions similar to an initial model to try to address
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the "catastrophic forgetting" problem. This feature is experimental.
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> #### Example
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>
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> ```python
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> senter = nlp.add_pipe("senter")
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> optimizer = nlp.resume_training()
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> losses = senter.rehearse(examples, sgd=optimizer)
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> ```
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| Name | Description |
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| -------------- | ------------------------------------------------------------------------------------------------------------------------ |
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| `examples` | A batch of [`Example`](/api/example) objects to learn from. ~~Iterable[Example]~~ |
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| _keyword-only_ | |
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| `drop` | The dropout rate. ~~float~~ |
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| `sgd` | An optimizer. Will be created via [`create_optimizer`](#create_optimizer) if not set. ~~Optional[Optimizer]~~ |
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| `losses` | Optional record of the loss during training. Updated using the component name as the key. ~~Optional[Dict[str, float]]~~ |
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| **RETURNS** | The updated `losses` dictionary. ~~Dict[str, float]~~ |
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## SentenceRecognizer.get_loss {id="get_loss",tag="method"}
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Find the loss and gradient of loss for the batch of documents and their
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predicted scores.
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> #### Example
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>
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> ```python
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> senter = nlp.add_pipe("senter")
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> scores = senter.predict([eg.predicted for eg in examples])
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> loss, d_loss = senter.get_loss(examples, scores)
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> ```
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| Name | Description |
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| ----------- | --------------------------------------------------------------------------- |
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| `examples` | The batch of examples. ~~Iterable[Example]~~ |
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| `scores` | Scores representing the model's predictions. |
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| **RETURNS** | The loss and the gradient, i.e. `(loss, gradient)`. ~~Tuple[float, float]~~ |
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## SentenceRecognizer.get_teacher_student_loss {id="get_teacher_student_loss", tag="method", version="4"}
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Calculate the loss and its gradient for the batch of student scores relative to
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the teacher scores.
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> #### Example
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>
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> ```python
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> teacher_senter = teacher.get_pipe("senter")
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> student_senter = student.add_pipe("senter")
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> student_scores = student_senter.predict([eg.predicted for eg in examples])
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> teacher_scores = teacher_senter.predict([eg.predicted for eg in examples])
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> loss, d_loss = student_senter.get_teacher_student_loss(teacher_scores, student_scores)
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> ```
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| Name | Description |
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| ---------------- | --------------------------------------------------------------------------- |
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| `teacher_scores` | Scores representing the teacher model's predictions. |
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| `student_scores` | Scores representing the student model's predictions. |
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| **RETURNS** | The loss and the gradient, i.e. `(loss, gradient)`. ~~Tuple[float, float]~~ |
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## SentenceRecognizer.create_optimizer {id="create_optimizer",tag="method"}
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Create an optimizer for the pipeline component.
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> #### Example
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>
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> ```python
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> senter = nlp.add_pipe("senter")
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> optimizer = senter.create_optimizer()
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> ```
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| Name | Description |
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| ----------- | ---------------------------- |
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| **RETURNS** | The optimizer. ~~Optimizer~~ |
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## SentenceRecognizer.use_params {id="use_params",tag="method, contextmanager"}
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Modify the pipe's model, to use the given parameter values. At the end of the
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context, the original parameters are restored.
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> #### Example
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>
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> ```python
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> senter = nlp.add_pipe("senter")
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> with senter.use_params(optimizer.averages):
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> senter.to_disk("/best_model")
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> ```
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| Name | Description |
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| -------- | -------------------------------------------------- |
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| `params` | The parameter values to use in the model. ~~dict~~ |
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## SentenceRecognizer.to_disk {id="to_disk",tag="method"}
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Serialize the pipe to disk.
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> #### Example
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>
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> ```python
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> senter = nlp.add_pipe("senter")
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> senter.to_disk("/path/to/senter")
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> ```
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| Name | Description |
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| -------------- | ------------------------------------------------------------------------------------------------------------------------------------------ |
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| `path` | A path to a directory, 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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| _keyword-only_ | |
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| `exclude` | String names of [serialization fields](#serialization-fields) to exclude. ~~Iterable[str]~~ |
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## SentenceRecognizer.from_disk {id="from_disk",tag="method"}
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Load the pipe from disk. 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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> senter = nlp.add_pipe("senter")
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> senter.from_disk("/path/to/senter")
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> ```
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| Name | Description |
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| -------------- | ----------------------------------------------------------------------------------------------- |
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| `path` | A path to a directory. Paths may be either strings or `Path`-like objects. ~~Union[str, Path]~~ |
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| _keyword-only_ | |
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| `exclude` | String names of [serialization fields](#serialization-fields) to exclude. ~~Iterable[str]~~ |
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| **RETURNS** | The modified `SentenceRecognizer` object. ~~SentenceRecognizer~~ |
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## SentenceRecognizer.to_bytes {id="to_bytes",tag="method"}
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> #### Example
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>
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> ```python
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> senter = nlp.add_pipe("senter")
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> senter_bytes = senter.to_bytes()
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> ```
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Serialize the pipe to a bytestring.
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| Name | Description |
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| -------------- | ------------------------------------------------------------------------------------------- |
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| _keyword-only_ | |
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| `exclude` | String names of [serialization fields](#serialization-fields) to exclude. ~~Iterable[str]~~ |
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| **RETURNS** | The serialized form of the `SentenceRecognizer` object. ~~bytes~~ |
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## SentenceRecognizer.from_bytes {id="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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> senter_bytes = senter.to_bytes()
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> senter = nlp.add_pipe("senter")
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> senter.from_bytes(senter_bytes)
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> ```
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| Name | Description |
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| -------------- | ------------------------------------------------------------------------------------------- |
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| `bytes_data` | The data to load from. ~~bytes~~ |
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| _keyword-only_ | |
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| `exclude` | String names of [serialization fields](#serialization-fields) to exclude. ~~Iterable[str]~~ |
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| **RETURNS** | The `SentenceRecognizer` object. ~~SentenceRecognizer~~ |
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## Serialization fields {id="serialization-fields"}
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During serialization, spaCy will export several data fields used to restore
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different aspects of the object. If needed, you can exclude them from
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serialization by passing in the string names via the `exclude` argument.
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> #### Example
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>
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> ```python
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> data = senter.to_disk("/path", exclude=["vocab"])
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> ```
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| Name | Description |
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| ------- | -------------------------------------------------------------- |
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| `vocab` | The shared [`Vocab`](/api/vocab). |
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| `cfg` | The config file. You usually don't want to exclude this. |
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| `model` | The binary model data. You usually don't want to exclude this. |
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