CoreferenceResolver

Installation

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$ pip install -U spacy-experimental

<Infobox title="Important note" variant="warning">

This component is not yet integrated into spaCy core, and is available via the extension package spacy-experimental starting in version 0.6.0. It exposes the component via entry points, so if you have the package installed, using factory = "experimental_coref" in your training config or nlp.add_pipe("experimental_coref") will work out-of-the-box.

</Infobox>

A CoreferenceResolver component groups tokens into clusters that refer to the same thing. Clusters are represented as SpanGroups that start with a prefix (coref_clusters by default).

A CoreferenceResolver component can be paired with a SpanResolver to expand single tokens to spans.

Assigned Attributes {id="assigned-attributes"}

Predictions will be saved to Doc.spans as a SpanGroup. The span key will be a prefix plus a serial number referring to the coreference cluster, starting from zero.

The span key prefix defaults to "coref_clusters", but can be passed as a parameter.

Location	Value
Doc.spans[prefix + "_" + cluster_number]	One coreference cluster, represented as single-token spans. Cluster numbers start from 1. SpanGroup

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_experimental.coref.coref_component import DEFAULT_COREF_MODEL
from spacy_experimental.coref.coref_util import DEFAULT_CLUSTER_PREFIX
config={
    "model": DEFAULT_COREF_MODEL,
    "span_cluster_prefix": DEFAULT_CLUSTER_PREFIX,
}
nlp.add_pipe("experimental_coref", config=config)

Setting	Description
model	The Model powering the pipeline component. Defaults to Coref. Model
span_cluster_prefix	The prefix for the keys for clusters saved to doc.spans. Defaults to coref_clusters. str

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

Example

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# Construction via add_pipe with default model
coref = nlp.add_pipe("experimental_coref")

# Construction via add_pipe with custom model
config = {"model": {"@architectures": "my_coref.v1"}}
coref = nlp.add_pipe("experimental_coref", config=config)

# Construction from class
from spacy_experimental.coref.coref_component import CoreferenceResolver
coref = CoreferenceResolver(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
name	String name of the component instance. Used to add entries to the losses during training. str
keyword-only
span_cluster_prefix	The prefix for the key for saving clusters of spans. bool

CoreferenceResolver.__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.")
coref = nlp.add_pipe("experimental_coref")
# This usually happens under the hood
processed = coref(doc)

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

CoreferenceResolver.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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coref = nlp.add_pipe("experimental_coref")
for doc in coref.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

CoreferenceResolver.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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coref = nlp.add_pipe("experimental_coref")
coref.initialize(lambda: examples, nlp=nlp)

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

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

Apply the component's model to a batch of Doc objects, without modifying them. Clusters 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 a cluster, and the ints correspond to token indices.

Example

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coref = nlp.add_pipe("experimental_coref")
clusters = coref.predict([doc1, doc2])

Name	Description
docs	The documents to predict. Iterable[Doc]
RETURNS	The predicted coreference clusters for the docs. List[MentionClusters]

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

Modify a batch of documents, saving coreference clusters in Doc.spans.

Example

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coref = nlp.add_pipe("experimental_coref")
clusters = coref.predict([doc1, doc2])
coref.set_annotations([doc1, doc2], clusters)

Name	Description
docs	The documents to modify. Iterable[Doc]
clusters	The predicted coreference clusters for the docs. List[MentionClusters]

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

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

Example

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coref = nlp.add_pipe("experimental_coref")
optimizer = nlp.initialize()
losses = coref.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]

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

Create an optimizer for the pipeline component.

Example

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coref = nlp.add_pipe("experimental_coref")
optimizer = coref.create_optimizer()

Name	Description
RETURNS	The optimizer. Optimizer

CoreferenceResolver.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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coref = nlp.add_pipe("experimental_coref")
with coref.use_params(optimizer.averages):
    coref.to_disk("/best_model")

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

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

Serialize the pipe to disk.

Example

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coref = nlp.add_pipe("experimental_coref")
coref.to_disk("/path/to/coref")

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]

CoreferenceResolver.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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coref = nlp.add_pipe("experimental_coref")
coref.from_disk("/path/to/coref")

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 CoreferenceResolver object. CoreferenceResolver

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

Example

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coref = nlp.add_pipe("experimental_coref")
coref_bytes = coref.to_bytes()

Serialize the pipe to a bytestring, including the KnowledgeBase.

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

CoreferenceResolver.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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coref_bytes = coref.to_bytes()
coref = nlp.add_pipe("experimental_coref")
coref.from_bytes(coref_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 CoreferenceResolver object. CoreferenceResolver

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 = coref.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.