SentenceRecognizer

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

Assigned Attributes {id="assigned-attributes"}

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

Config and implementation {id="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 or in your config.cfg for training. See the model architectures documentation for details on the architectures and their arguments and hyperparameters.

Example

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

%%GITHUB_SPACY/spacy/pipeline/senter.pyx

SentenceRecognizer.__init__ {id="init",tag="method"}

Initialize the sentence recognizer.

Example

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# 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.

SentenceRecognizer.__call__ {id="call",tag="method"}

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

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

SentenceRecognizer.pipe {id="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. Both __call__ and pipe delegate to the predict and set_annotations methods.

Example

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

SentenceRecognizer.initialize {id="initialize",tag="method"}

Initialize the component for training. get_examples should be a function that returns an iterable of Example objects. At least one example should be supplied. 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

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

SentenceRecognizer.predict {id="predict",tag="method"}

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

Example

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

SentenceRecognizer.set_annotations {id="set_annotations",tag="method"}

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

Example

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

SentenceRecognizer.update {id="update",tag="method"}

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

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

SentenceRecognizer.rehearse {id="rehearse",tag="method,experimental",version="3"}

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

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

SentenceRecognizer.get_loss {id="get_loss",tag="method"}

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

Example

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

SentenceRecognizer.create_optimizer {id="create_optimizer",tag="method"}

Create an optimizer for the pipeline component.

Example

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

SentenceRecognizer.use_params {id="use_params",tag="method, contextmanager"}

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

Example

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

SentenceRecognizer.to_disk {id="to_disk",tag="method"}

Serialize the pipe to disk.

Example

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

SentenceRecognizer.from_disk {id="from_disk",tag="method"}

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

Example

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

SentenceRecognizer.to_bytes {id="to_bytes",tag="method"}

Example

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

Serialize the pipe to a bytestring.

SentenceRecognizer.from_bytes {id="from_bytes",tag="method"}

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

Example

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

Serialization fields {id="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

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