A SpanResolver component takes in tokens (represented as Span objects of length 1) and resolves them into Span objects of arbitrary length. The initial use case is as a post-processing step on word-level coreference resolution. The input and output keys used to store Span objects are configurable.

Assigned Attributes

Predictions will be saved to Doc.spans as SpanGroups.

Input token spans will be read in using an input prefix, by default "coref_head_clusters", and output spans will be saved using an output prefix (default "coref_clusters") plus a serial number starting from one. The prefixes are configurable.

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.

SpanResolver.__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.

SpanResolver.__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.

SpanResolver.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.

SpanResolver.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.

SpanResolver.predict method

Apply the component’s model to a batch of Doc objects, without modifying them. Predictions are returned as a list of MentionClusters, one for each input Doc. A MentionClusters instance is just a list of lists of pairs of ints, where each item corresponds to an input SpanGroup, and the ints correspond to token indices.

SpanResolver.set_annotations method

Modify a batch of documents, saving predictions using the output prefix in Doc.spans.

SpanResolver.update method

Learn from a batch of Example objects. Delegates to predict.

SpanResolver.create_optimizer method

Create an optimizer for the pipeline component.

SpanResolver.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.

SpanResolver.to_disk method

Serialize the pipe to disk.

SpanResolver.from_disk method

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

SpanResolver.to_bytes method

Serialize the pipe to a bytestring.

SpanResolver.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.