spaCy/website/docs/api/sentencerecognizer.md

title	tag	source	new	teaser	api_base_class	api_string_name	api_trainable
SentenceRecognizer	class	spacy/pipeline/senter.pyx	3	Pipeline component for sentence segmentation	/api/tagger	senter	true

A trainable pipeline component for sentence segmentation. For a simpler, ruse-based strategy, see the Sentencizer.

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. See the model architectures documentation for details on the architectures and their arguments and hyperparameters.

Example

from spacy.pipeline.senter import DEFAULT_SENTER_MODEL
config = {"model": DEFAULT_SENTER_MODEL,}
nlp.add_pipe("senter", config=config)

Setting	Type	Description	Default
model	Model	The model to use.	Tagger

https://github.com/explosion/spaCy/blob/develop/spacy/pipeline/senter.pyx

SentenceRecognizer.__init__

Initialize the sentence recognizer.

Example

# Construction via add_pipe with default model
senter = nlp.add_pipe("senter")

# Construction via create_pipe with custom model
config = {"model": {"@architectures": "my_senter"}}
senter = nlp.add_pipe("senter", config=config)

# Construction from class
from spacy.pipeline import SentenceRecognizer
senter = SentenceRecognizer(nlp.vocab, model)

Create a new pipeline instance. In your application, you would normally use a shortcut for this and instantiate the component using its string name and nlp.add_pipe.

Name	Type	Description
vocab	Vocab	The shared vocabulary.
model	Model	The Model powering the pipeline component.
name	str	String name of the component instance. Used to add entries to the losses during training.

SentenceRecognizer.__call__

Apply the pipe to one document. The document is modified in place, and returned. 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. Both __call__ and pipe delegate to the predict and set_annotations methods.

Example

doc = nlp("This is a sentence.")
senter = nlp.add_pipe("senter")
# This usually happens under the hood
processed = senter(doc)

Name	Type	Description
doc	Doc	The document to process.
RETURNS	Doc	The processed document.

SentenceRecognizer.pipe

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. Both __call__ and pipe delegate to the predict and set_annotations methods.

Example

senter = nlp.add_pipe("senter")
for doc in senter.pipe(docs, batch_size=50):
    pass

Name	Type	Description
stream	Iterable[Doc]	A stream of documents.
keyword-only
batch_size	int	The number of texts to buffer. Defaults to 128.
YIELDS	Doc	Processed documents in the order of the original text.

SentenceRecognizer.begin_training

Initialize the pipe for training, using data examples if available. Returns an Optimizer object.

Example

senter = nlp.add_pipe("senter")
optimizer = senter.begin_training(pipeline=nlp.pipeline)

Name	Type	Description
get_examples	Callable[[], Iterable[Example]]	Optional function that returns gold-standard annotations in the form of Example objects.
keyword-only
pipeline	List[Tuple[str, Callable]]	Optional list of pipeline components that this component is part of.
sgd	Optimizer	An optional optimizer. Will be created via create_optimizer if not set.
RETURNS	Optimizer	The optimizer.

SentenceRecognizer.predict

Apply the component's model to a batch of Doc objects, without modifying them.

Example

senter = nlp.add_pipe("senter")
scores = senter.predict([doc1, doc2])

Name	Type	Description
docs	Iterable[Doc]	The documents to predict.
RETURNS	-	The model's prediction for each document.

SentenceRecognizer.set_annotations

Modify a batch of Doc objects, using pre-computed scores.

Example

senter = nlp.add_pipe("senter")
scores = senter.predict([doc1, doc2])
senter.set_annotations([doc1, doc2], scores)

Name	Type	Description
docs	Iterable[Doc]	The documents to modify.
scores	-	The scores to set, produced by SentenceRecognizer.predict.

SentenceRecognizer.update

Learn from a batch of Example objects containing the predictions and gold-standard annotations, and update the component's model. Delegates to predict and get_loss.

Example

senter = nlp.add_pipe("senter")
optimizer = nlp.begin_training()
losses = senter.update(examples, sgd=optimizer)

Name	Type	Description
examples	Iterable[Example]	A batch of Example objects to learn from.
keyword-only
drop	float	The dropout rate.
set_annotations	bool	Whether or not to update the Example objects with the predictions, delegating to set_annotations.
sgd	Optimizer	The optimizer.
losses	Dict[str, float]	Optional record of the loss during training. The value keyed by the model's name is updated.
RETURNS	Dict[str, float]	The updated losses dictionary.

SentenceRecognizer.rehearse

Perform a "rehearsal" update from a batch of data. Rehearsal updates teach the current model to make predictions similar to an initial model, to try to address the "catastrophic forgetting" problem. This feature is experimental.

Example

senter = nlp.add_pipe("senter")
optimizer = nlp.resume_training()
losses = senter.rehearse(examples, sgd=optimizer)

Name	Type	Description
examples	Iterable[Example]	A batch of Example objects to learn from.
keyword-only
drop	float	The dropout rate.
sgd	Optimizer	The optimizer.
losses	Dict[str, float]	Optional record of the loss during training. Updated using the component name as the key.
RETURNS	Dict[str, float]	The updated losses dictionary.

SentenceRecognizer.get_loss

Find the loss and gradient of loss for the batch of documents and their predicted scores.

Example

senter = nlp.add_pipe("senter")
scores = senter.predict([eg.predicted for eg in examples])
loss, d_loss = senter.get_loss(examples, scores)

Name	Type	Description
examples	Iterable[Example]	The batch of examples.
scores	-	Scores representing the model's predictions.
RETURNS	Tuple[float, float]	The loss and the gradient, i.e. (loss, gradient).

SentenceRecognizer.score

Score a batch of examples.

Example

scores = senter.score(examples)

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

SentenceRecognizer.create_optimizer

Create an optimizer for the pipeline component.

Example

senter = nlp.add_pipe("senter")
optimizer = senter.create_optimizer()

Name	Type	Description
RETURNS	Optimizer	The optimizer.

SentenceRecognizer.use_params

Modify the pipe's model, to use the given parameter values. At the end of the context, the original parameters are restored.

Example

senter = nlp.add_pipe("senter")
with senter.use_params(optimizer.averages):
    senter.to_disk("/best_model")

Name	Type	Description
params	dict	The parameter values to use in the model.

SentenceRecognizer.to_disk

Serialize the pipe to disk.

Example

senter = nlp.add_pipe("senter")
senter.to_disk("/path/to/senter")

Name	Type	Description
path	str / Path	A path to a directory, which will be created if it doesn't exist. Paths may be either strings or Path-like objects.
keyword-only
exclude	Iterable[str]	String names of serialization fields to exclude.

SentenceRecognizer.from_disk

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

Example

senter = nlp.add_pipe("senter")
senter.from_disk("/path/to/senter")

Name	Type	Description
path	str / Path	A path to a directory. Paths may be either strings or Path-like objects.
keyword-only
exclude	Iterable[str]	String names of serialization fields to exclude.
RETURNS	SentenceRecognizer	The modified SentenceRecognizer object.

SentenceRecognizer.to_bytes

Example

senter = nlp.add_pipe("senter")
senter_bytes = senter.to_bytes()

Serialize the pipe to a bytestring.

Name	Type	Description
keyword-only
exclude	Iterable[str]	String names of serialization fields to exclude.
RETURNS	bytes	The serialized form of the SentenceRecognizer object.

SentenceRecognizer.from_bytes

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

Example

senter_bytes = senter.to_bytes()
senter = nlp.add_pipe("senter")
senter.from_bytes(senter_bytes)

Name	Type	Description
bytes_data	bytes	The data to load from.
keyword-only
exclude	Iterable[str]	String names of serialization fields to exclude.
RETURNS	SentenceRecognizer	The SentenceRecognizer object.

Serialization fields

During serialization, spaCy will export several data fields used to restore different aspects of the object. If needed, you can exclude them from serialization by passing in the string names via the exclude argument.

Example

data = senter.to_disk("/path", exclude=["vocab"])

Name	Description
vocab	The shared Vocab.
cfg	The config file. You usually don't want to exclude this.
model	The binary model data. You usually don't want to exclude this.