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title tag source teaser api_base_class api_string_name api_trainable
SpanResolver class,experimental spacy-experimental/coref/span_resolver_component.py Pipeline component for resolving tokens into spans /api/pipe span_resolver true

Installation

$ pip install -U spacy-experimental

This component 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_span_resolver" in your training config or nlp.add_pipe("experimental_span_resolver") will work out-of-the-box.

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.

Location Value
Doc.spans[output_prefix + "_" + cluster_number] One group of predicted spans. Cluster number starts from 1. SpanGroup

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.

Example

from spacy_experimental.coref.span_resolver_component import DEFAULT_SPAN_RESOLVER_MODEL
from spacy_experimental.coref.coref_util import DEFAULT_CLUSTER_PREFIX, DEFAULT_CLUSTER_HEAD_PREFIX
config={
    "model": DEFAULT_SPAN_RESOLVER_MODEL,
    "input_prefix": DEFAULT_CLUSTER_HEAD_PREFIX,
    "output_prefix": DEFAULT_CLUSTER_PREFIX,
},
nlp.add_pipe("experimental_span_resolver", config=config)
Setting Description
model The Model powering the pipeline component. Defaults to SpanResolver. Model
input_prefix The prefix to use for input SpanGroups. Defaults to coref_head_clusters. str
output_prefix The prefix for predicted SpanGroups. Defaults to coref_clusters. str

SpanResolver.__init__

Example

# Construction via add_pipe with default model
span_resolver = nlp.add_pipe("experimental_span_resolver")

# Construction via add_pipe with custom model
config = {"model": {"@architectures": "my_span_resolver.v1"}}
span_resolver = nlp.add_pipe("experimental_span_resolver", config=config)

# Construction from class
from spacy_experimental.coref.span_resolver_component import SpanResolver
span_resolver = SpanResolver(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
input_prefix The prefix to use for input SpanGroups. Defaults to coref_head_clusters. str
output_prefix The prefix for predicted SpanGroups. Defaults to coref_clusters. str

SpanResolver.__call__

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

doc = nlp("This is a sentence.")
span_resolver = nlp.add_pipe("experimental_span_resolver")
# This usually happens under the hood
processed = span_resolver(doc)
Name Description
doc The document to process. Doc
RETURNS The processed document. Doc

SpanResolver.pipe

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

span_resolver = nlp.add_pipe("experimental_span_resolver")
for doc in span_resolver.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

SpanResolver.initialize

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

span_resolver = nlp.add_pipe("experimental_span_resolver")
span_resolver.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]

SpanResolver.predict

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.

Example

span_resolver = nlp.add_pipe("experimental_span_resolver")
spans = span_resolver.predict([doc1, doc2])
Name Description
docs The documents to predict. Iterable[Doc]
RETURNS The predicted spans for the Docs. List[MentionClusters]

SpanResolver.set_annotations

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

Example

span_resolver = nlp.add_pipe("experimental_span_resolver")
spans = span_resolver.predict([doc1, doc2])
span_resolver.set_annotations([doc1, doc2], spans)
Name Description
docs The documents to modify. Iterable[Doc]
spans The predicted spans for the docs. List[MentionClusters]

SpanResolver.update

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

Example

span_resolver = nlp.add_pipe("experimental_span_resolver")
optimizer = nlp.initialize()
losses = span_resolver.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]

SpanResolver.create_optimizer

Create an optimizer for the pipeline component.

Example

span_resolver = nlp.add_pipe("experimental_span_resolver")
optimizer = span_resolver.create_optimizer()
Name Description
RETURNS The optimizer. Optimizer

SpanResolver.use_params

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

Example

span_resolver = nlp.add_pipe("experimental_span_resolver")
with span_resolver.use_params(optimizer.averages):
    span_resolver.to_disk("/best_model")
Name Description
params The parameter values to use in the model. dict

SpanResolver.to_disk

Serialize the pipe to disk.

Example

span_resolver = nlp.add_pipe("experimental_span_resolver")
span_resolver.to_disk("/path/to/span_resolver")
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]

SpanResolver.from_disk

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

Example

span_resolver = nlp.add_pipe("experimental_span_resolver")
span_resolver.from_disk("/path/to/span_resolver")
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 SpanResolver object. SpanResolver

SpanResolver.to_bytes

Example

span_resolver = nlp.add_pipe("experimental_span_resolver")
span_resolver_bytes = span_resolver.to_bytes()

Serialize the pipe to a bytestring.

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

SpanResolver.from_bytes

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

Example

span_resolver_bytes = span_resolver.to_bytes()
span_resolver = nlp.add_pipe("experimental_span_resolver")
span_resolver.from_bytes(span_resolver_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 SpanResolver object. SpanResolver

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

data = span_resolver.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.