The span finder identifies potentially overlapping, unlabeled spans. It identifies tokens that start or end spans and annotates unlabeled spans between starts and ends, with optional filters for min and max span length. It is intended for use in combination with a component like SpanCategorizer that may further filter or label the spans. Predicted spans will be saved in a SpanGroup on the doc under doc.spans[spans_key], where spans_key is a component config setting.

Assigned Attributes

Predictions will be saved to Doc.spans[spans_key] as a SpanGroup.

spans_key defaults to "sc", but can be passed as a parameter. The span_finder component will overwrite any existing spans under the spans key doc.spans[spans_key].

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/span_finder.py

SpanFinder.__init__ method

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.

SpanFinder.__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.

SpanFinder.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.

SpanFinder.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 and inferring missing shapes This method is typically called by Language.initialize and lets you customize arguments it receives via the [initialize.components] block in the config.

SpanFinder.predict method

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

SpanFinder.set_annotations method

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

SpanFinder.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.

SpanFinder.get_loss method

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

SpanFinder.create_optimizer method

Create an optimizer for the pipeline component.

SpanFinder.use_params methodcontextmanager

Modify the pipe’s model to use the given parameter values.

SpanFinder.to_disk method

Serialize the pipe to disk.

SpanFinder.from_disk method

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

SpanFinder.to_bytes method

Serialize the pipe to a bytestring.

SpanFinder.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.