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.

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.

explosion/spaCy/master/spacy/pipeline/senter.pyx

SentenceRecognizer.__init__ method

Initialize the sentence recognizer.

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__ 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.

SentenceRecognizer.pipe 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.

SentenceRecognizer.initialize 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.

SentenceRecognizer.predict method

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

SentenceRecognizer.set_annotations method

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

SentenceRecognizer.update 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.

SentenceRecognizer.rehearse methodexperimentalv3.0

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.

SentenceRecognizer.get_loss method

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

SentenceRecognizer.create_optimizer method

Create an optimizer for the pipeline component.

SentenceRecognizer.use_params methodcontextmanager

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

SentenceRecognizer.to_disk method

Serialize the pipe to disk.

SentenceRecognizer.from_disk method

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

SentenceRecognizer.to_bytes method

Serialize the pipe to a bytestring.

SentenceRecognizer.from_bytes method

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

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.