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354 lines
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354 lines
17 KiB
Plaintext
---
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title: CoreferenceResolver
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tag: class,experimental
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source: spacy-experimental/coref/coref_component.py
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teaser: 'Pipeline component for word-level coreference resolution'
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api_base_class: /api/pipe
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api_string_name: coref
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api_trainable: true
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---
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> #### Installation
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>
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> ```bash
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> $ pip install -U spacy-experimental
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> ```
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<Infobox title="Important note" variant="warning">
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This component is not yet integrated into spaCy core, and is available via the
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extension package
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[`spacy-experimental`](https://github.com/explosion/spacy-experimental) starting
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in version 0.6.0. It exposes the component via
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[entry points](/usage/saving-loading/#entry-points), so if you have the package
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installed, using `factory = "experimental_coref"` in your
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[training config](/usage/training#config) or
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`nlp.add_pipe("experimental_coref")` will work out-of-the-box.
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</Infobox>
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A `CoreferenceResolver` component groups tokens into clusters that refer to the
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same thing. Clusters are represented as SpanGroups that start with a prefix
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(`coref_clusters` by default).
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A `CoreferenceResolver` component can be paired with a
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[`SpanResolver`](/api/span-resolver) to expand single tokens to spans.
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## Assigned Attributes {id="assigned-attributes"}
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Predictions will be saved to `Doc.spans` as a [`SpanGroup`](/api/spangroup). The
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span key will be a prefix plus a serial number referring to the coreference
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cluster, starting from zero.
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The span key prefix defaults to `"coref_clusters"`, but can be passed as a
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parameter.
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| Location | Value |
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| ------------------------------------------ | ------------------------------------------------------------------------------------------------------- |
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| `Doc.spans[prefix + "_" + cluster_number]` | One coreference cluster, represented as single-token spans. Cluster numbers start from 1. ~~SpanGroup~~ |
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## Config and implementation {id="config"}
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The default config is defined by the pipeline component factory and describes
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how the component should be configured. You can override its settings via the
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`config` argument on [`nlp.add_pipe`](/api/language#add_pipe) or in your
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[`config.cfg` for training](/usage/training#config). See the
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[model architectures](/api/architectures#coref-architectures) documentation for
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details on the architectures and their arguments and hyperparameters.
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> #### Example
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>
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> ```python
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> from spacy_experimental.coref.coref_component import DEFAULT_COREF_MODEL
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> from spacy_experimental.coref.coref_util import DEFAULT_CLUSTER_PREFIX
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> config={
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> "model": DEFAULT_COREF_MODEL,
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> "span_cluster_prefix": DEFAULT_CLUSTER_PREFIX,
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> }
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> nlp.add_pipe("experimental_coref", config=config)
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> ```
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| Setting | Description |
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| --------------------- | ---------------------------------------------------------------------------------------------------------------------------------------- |
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| `model` | The [`Model`](https://thinc.ai/docs/api-model) powering the pipeline component. Defaults to [Coref](/api/architectures#Coref). ~~Model~~ |
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| `span_cluster_prefix` | The prefix for the keys for clusters saved to `doc.spans`. Defaults to `coref_clusters`. ~~str~~ |
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## CoreferenceResolver.\_\_init\_\_ {id="init",tag="method"}
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> #### Example
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>
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> ```python
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> # Construction via add_pipe with default model
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> coref = nlp.add_pipe("experimental_coref")
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>
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> # Construction via add_pipe with custom model
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> config = {"model": {"@architectures": "my_coref.v1"}}
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> coref = nlp.add_pipe("experimental_coref", config=config)
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>
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> # Construction from class
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> from spacy_experimental.coref.coref_component import CoreferenceResolver
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> coref = CoreferenceResolver(nlp.vocab, model)
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> ```
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Create a new pipeline instance. In your application, you would normally use a
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shortcut for this and instantiate the component using its string name and
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[`nlp.add_pipe`](/api/language#add_pipe).
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| Name | Description |
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| --------------------- | --------------------------------------------------------------------------------------------------- |
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| `vocab` | The shared vocabulary. ~~Vocab~~ |
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| `model` | The [`Model`](https://thinc.ai/docs/api-model) powering the pipeline component. ~~Model~~ |
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| `name` | String name of the component instance. Used to add entries to the `losses` during training. ~~str~~ |
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| _keyword-only_ | |
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| `span_cluster_prefix` | The prefix for the key for saving clusters of spans. ~~bool~~ |
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## CoreferenceResolver.\_\_call\_\_ {id="call",tag="method"}
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Apply the pipe to one document. The document is modified in place and returned.
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This usually happens under the hood when the `nlp` object is called on a text
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and all pipeline components are applied to the `Doc` in order. Both
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[`__call__`](/api/coref#call) and [`pipe`](/api/coref#pipe) delegate to the
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[`predict`](/api/coref#predict) and
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[`set_annotations`](/api/coref#set_annotations) methods.
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> #### Example
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>
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> ```python
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> doc = nlp("This is a sentence.")
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> coref = nlp.add_pipe("experimental_coref")
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> # This usually happens under the hood
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> processed = coref(doc)
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> ```
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| Name | Description |
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| ----------- | -------------------------------- |
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| `doc` | The document to process. ~~Doc~~ |
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| **RETURNS** | The processed document. ~~Doc~~ |
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## CoreferenceResolver.pipe {id="pipe",tag="method"}
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Apply the pipe to a stream of documents. This usually happens under the hood
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when the `nlp` object is called on a text and all pipeline components are
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applied to the `Doc` in order. Both [`__call__`](/api/coref#call) and
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[`pipe`](/api/coref#pipe) delegate to the [`predict`](/api/coref#predict) and
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[`set_annotations`](/api/coref#set_annotations) methods.
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> #### Example
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>
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> ```python
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> coref = nlp.add_pipe("experimental_coref")
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> for doc in coref.pipe(docs, batch_size=50):
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> pass
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> ```
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| Name | Description |
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| -------------- | ------------------------------------------------------------- |
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| `stream` | A stream of documents. ~~Iterable[Doc]~~ |
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| _keyword-only_ | |
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| `batch_size` | The number of documents to buffer. Defaults to `128`. ~~int~~ |
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| **YIELDS** | The processed documents in order. ~~Doc~~ |
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## CoreferenceResolver.initialize {id="initialize",tag="method"}
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Initialize the component for training. `get_examples` should be a function that
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returns an iterable of [`Example`](/api/example) objects. **At least one example
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should be supplied.** The data examples are used to **initialize the model** of
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the component and can either be the full training data or a representative
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sample. Initialization includes validating the network,
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[inferring missing shapes](https://thinc.ai/docs/usage-models#validation) and
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setting up the label scheme based on the data. This method is typically called
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by [`Language.initialize`](/api/language#initialize).
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> #### Example
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>
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> ```python
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> coref = nlp.add_pipe("experimental_coref")
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> coref.initialize(lambda: examples, nlp=nlp)
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> ```
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| Name | Description |
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| -------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
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| `get_examples` | Function that returns gold-standard annotations in the form of [`Example`](/api/example) objects. Must contain at least one `Example`. ~~Callable[[], Iterable[Example]]~~ |
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| _keyword-only_ | |
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| `nlp` | The current `nlp` object. Defaults to `None`. ~~Optional[Language]~~ |
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## CoreferenceResolver.predict {id="predict",tag="method"}
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Apply the component's model to a batch of [`Doc`](/api/doc) objects, without
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modifying them. Clusters are returned as a list of `MentionClusters`, one for
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each input `Doc`. A `MentionClusters` instance is just a list of lists of pairs
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of `int`s, where each item corresponds to a cluster, and the `int`s correspond
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to token indices.
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> #### Example
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>
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> ```python
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> coref = nlp.add_pipe("experimental_coref")
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> clusters = coref.predict([doc1, doc2])
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> ```
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| Name | Description |
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| ----------- | ---------------------------------------------------------------------------- |
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| `docs` | The documents to predict. ~~Iterable[Doc]~~ |
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| **RETURNS** | The predicted coreference clusters for the `docs`. ~~List[MentionClusters]~~ |
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## CoreferenceResolver.set_annotations {id="set_annotations",tag="method"}
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Modify a batch of documents, saving coreference clusters in `Doc.spans`.
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> #### Example
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>
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> ```python
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> coref = nlp.add_pipe("experimental_coref")
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> clusters = coref.predict([doc1, doc2])
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> coref.set_annotations([doc1, doc2], clusters)
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> ```
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| Name | Description |
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| ---------- | ---------------------------------------------------------------------------- |
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| `docs` | The documents to modify. ~~Iterable[Doc]~~ |
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| `clusters` | The predicted coreference clusters for the `docs`. ~~List[MentionClusters]~~ |
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## CoreferenceResolver.update {id="update",tag="method"}
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Learn from a batch of [`Example`](/api/example) objects. Delegates to
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[`predict`](/api/coref#predict).
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> #### Example
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>
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> ```python
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> coref = nlp.add_pipe("experimental_coref")
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> optimizer = nlp.initialize()
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> losses = coref.update(examples, sgd=optimizer)
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> ```
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| Name | Description |
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| -------------- | ------------------------------------------------------------------------------------------------------------------------ |
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| `examples` | A batch of [`Example`](/api/example) objects to learn from. ~~Iterable[Example]~~ |
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| _keyword-only_ | |
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| `drop` | The dropout rate. ~~float~~ |
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| `sgd` | An optimizer. Will be created via [`create_optimizer`](#create_optimizer) if not set. ~~Optional[Optimizer]~~ |
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| `losses` | Optional record of the loss during training. Updated using the component name as the key. ~~Optional[Dict[str, float]]~~ |
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| **RETURNS** | The updated `losses` dictionary. ~~Dict[str, float]~~ |
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## CoreferenceResolver.create_optimizer {id="create_optimizer",tag="method"}
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Create an optimizer for the pipeline component.
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> #### Example
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>
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> ```python
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> coref = nlp.add_pipe("experimental_coref")
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> optimizer = coref.create_optimizer()
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> ```
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| Name | Description |
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| ----------- | ---------------------------- |
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| **RETURNS** | The optimizer. ~~Optimizer~~ |
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## CoreferenceResolver.use_params {id="use_params",tag="method, contextmanager"}
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Modify the pipe's model, to use the given parameter values. At the end of the
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context, the original parameters are restored.
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> #### Example
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>
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> ```python
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> coref = nlp.add_pipe("experimental_coref")
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> with coref.use_params(optimizer.averages):
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> coref.to_disk("/best_model")
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> ```
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| Name | Description |
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| -------- | -------------------------------------------------- |
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| `params` | The parameter values to use in the model. ~~dict~~ |
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## CoreferenceResolver.to_disk {id="to_disk",tag="method"}
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Serialize the pipe to disk.
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> #### Example
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>
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> ```python
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> coref = nlp.add_pipe("experimental_coref")
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> coref.to_disk("/path/to/coref")
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> ```
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| Name | Description |
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| -------------- | ------------------------------------------------------------------------------------------------------------------------------------------ |
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| `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]~~ |
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| _keyword-only_ | |
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| `exclude` | String names of [serialization fields](#serialization-fields) to exclude. ~~Iterable[str]~~ |
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## CoreferenceResolver.from_disk {id="from_disk",tag="method"}
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Load the pipe from disk. Modifies the object in place and returns it.
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> #### Example
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>
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> ```python
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> coref = nlp.add_pipe("experimental_coref")
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> coref.from_disk("/path/to/coref")
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> ```
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| Name | Description |
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| -------------- | ----------------------------------------------------------------------------------------------- |
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| `path` | A path to a directory. Paths may be either strings or `Path`-like objects. ~~Union[str, Path]~~ |
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| _keyword-only_ | |
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| `exclude` | String names of [serialization fields](#serialization-fields) to exclude. ~~Iterable[str]~~ |
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| **RETURNS** | The modified `CoreferenceResolver` object. ~~CoreferenceResolver~~ |
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## CoreferenceResolver.to_bytes {id="to_bytes",tag="method"}
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> #### Example
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>
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> ```python
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> coref = nlp.add_pipe("experimental_coref")
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> coref_bytes = coref.to_bytes()
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> ```
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Serialize the pipe to a bytestring, including the `KnowledgeBase`.
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| Name | Description |
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| -------------- | ------------------------------------------------------------------------------------------- |
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| _keyword-only_ | |
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| `exclude` | String names of [serialization fields](#serialization-fields) to exclude. ~~Iterable[str]~~ |
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| **RETURNS** | The serialized form of the `CoreferenceResolver` object. ~~bytes~~ |
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## CoreferenceResolver.from_bytes {id="from_bytes",tag="method"}
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Load the pipe from a bytestring. Modifies the object in place and returns it.
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> #### Example
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>
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> ```python
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> coref_bytes = coref.to_bytes()
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> coref = nlp.add_pipe("experimental_coref")
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> coref.from_bytes(coref_bytes)
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> ```
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| Name | Description |
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| -------------- | ------------------------------------------------------------------------------------------- |
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| `bytes_data` | The data to load from. ~~bytes~~ |
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| _keyword-only_ | |
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| `exclude` | String names of [serialization fields](#serialization-fields) to exclude. ~~Iterable[str]~~ |
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| **RETURNS** | The `CoreferenceResolver` object. ~~CoreferenceResolver~~ |
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## Serialization fields {id="serialization-fields"}
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During serialization, spaCy will export several data fields used to restore
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different aspects of the object. If needed, you can exclude them from
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serialization by passing in the string names via the `exclude` argument.
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> #### Example
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>
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> ```python
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> data = coref.to_disk("/path", exclude=["vocab"])
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> ```
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| Name | Description |
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| ------- | -------------------------------------------------------------- |
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| `vocab` | The shared [`Vocab`](/api/vocab). |
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| `cfg` | The config file. You usually don't want to exclude this. |
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| `model` | The binary model data. You usually don't want to exclude this. |
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