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, rule-based strategy, see the Sentencizer.

Assigned Attributes

Predicted values will be assigned to Token.is_sent_start. The resulting sentences can be accessed using Doc.sents.

Location	Value
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
Doc.sents	An iterator over sentences in the Doc, determined by Token.is_sent_start values. Iterator[Span]

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	Description
model	The Model powering the pipeline component. Defaults to Tagger. Model[List[Doc], List[Floats2d]]

%%GITHUB_SPACY/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	Description
vocab	The shared vocabulary. Vocab
model	The Model powering the pipeline component. Model[List[Doc], List[Floats2d]]
name	String name of the component instance. Used to add entries to the losses during training. str

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

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	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

SentenceRecognizer.initialize

Initialize the component for training. get_examples should be a function that returns an iterable of Example objects. The data examples are used to initialize the model of the component and can either be the full training data or a representative sample. Initialization includes validating the network, inferring missing shapes and setting up the label scheme based on the data. This method is typically called by Language.initialize.

Example

senter = nlp.add_pipe("senter")
senter.initialize(lambda: [], nlp=nlp)

Name	Description
get_examples	Function that returns gold-standard annotations in the form of Example objects. Callable], Iterable[Example
keyword-only
nlp	The current nlp object. Defaults to None. Optional[Language]

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	Description
docs	The documents to predict. Iterable[Doc]
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	Description
docs	The documents to modify. Iterable[Doc]
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.initialize()
losses = senter.update(examples, sgd=optimizer)

Name	Description
examples	A batch of Example objects to learn from. Iterable[Example]
keyword-only
drop	The dropout rate. float
sgd	An optimizer. Will be created via create_optimizer if not set. Optional[Optimizer]
losses	Optional record of the loss during training. Updated using the component name as the key. Optional[Dict[str, float]]
RETURNS	The updated losses dictionary. Dict[str, float]

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	Description
examples	A batch of Example objects to learn from. Iterable[Example]
keyword-only
drop	The dropout rate. float
sgd	An optimizer. Will be created via create_optimizer if not set. Optional[Optimizer]
losses	Optional record of the loss during training. Updated using the component name as the key. Optional[Dict[str, float]]
RETURNS	The updated losses dictionary. Dict[str, float]

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

SentenceRecognizer.score

Score a batch of examples.

Example

scores = senter.score(examples)

Name	Description
examples	The examples to score. Iterable[Example]
RETURNS	The scores, produced by Scorer.score_token_attr for the attributes "pos", "tag" and "lemma". Dict[str, float]

SentenceRecognizer.create_optimizer

Create an optimizer for the pipeline component.

Example

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

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

SentenceRecognizer.to_disk

Serialize the pipe to disk.

Example

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

Name	Description
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]
keyword-only
exclude	String names of serialization fields to exclude. Iterable[str]

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

SentenceRecognizer.to_bytes

Example

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

Serialize the pipe to a bytestring.

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

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

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.