mirror of https://github.com/explosion/spaCy.git
357 lines
17 KiB
Markdown
357 lines
17 KiB
Markdown
---
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title: SpanResolver
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tag: class,experimental
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source: spacy-experimental/coref/span_resolver_component.py
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teaser: 'Pipeline component for resolving tokens into spans'
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api_base_class: /api/pipe
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api_string_name: span_resolver
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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 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_span_resolver"` in your
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[training config](/usage/training#config) or
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`nlp.add_pipe("experimental_span_resolver")` will work out-of-the-box.
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</Infobox>
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A `SpanResolver` component takes in tokens (represented as `Span` objects of
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length 1) and resolves them into `Span` objects of arbitrary length. The initial
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use case is as a post-processing step on word-level
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[coreference resolution](/api/coref). The input and output keys used to store
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`Span` objects are configurable.
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## Assigned Attributes {#assigned-attributes}
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Predictions will be saved to `Doc.spans` as [`SpanGroup`s](/api/spangroup).
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Input token spans will be read in using an input prefix, by default
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`"coref_head_clusters"`, and output spans will be saved using an output prefix
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(default `"coref_clusters"`) plus a serial number starting from one. The
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prefixes are configurable.
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| Location | Value |
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| ------------------------------------------------- | ------------------------------------------------------------------------- |
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| `Doc.spans[output_prefix + "_" + cluster_number]` | One group of predicted spans. Cluster number starts from 1. ~~SpanGroup~~ |
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## Config and implementation {#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.span_resolver_component import DEFAULT_SPAN_RESOLVER_MODEL
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> from spacy_experimental.coref.coref_util import DEFAULT_CLUSTER_PREFIX, DEFAULT_CLUSTER_HEAD_PREFIX
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> config={
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> "model": DEFAULT_SPAN_RESOLVER_MODEL,
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> "input_prefix": DEFAULT_CLUSTER_HEAD_PREFIX,
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> "output_prefix": DEFAULT_CLUSTER_PREFIX,
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> },
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> nlp.add_pipe("experimental_span_resolver", 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 [SpanResolver](/api/architectures#SpanResolver). ~~Model~~ |
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| `input_prefix` | The prefix to use for input `SpanGroup`s. Defaults to `coref_head_clusters`. ~~str~~ |
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| `output_prefix` | The prefix for predicted `SpanGroup`s. Defaults to `coref_clusters`. ~~str~~ |
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## SpanResolver.\_\_init\_\_ {#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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> span_resolver = nlp.add_pipe("experimental_span_resolver")
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>
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> # Construction via add_pipe with custom model
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> config = {"model": {"@architectures": "my_span_resolver.v1"}}
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> span_resolver = nlp.add_pipe("experimental_span_resolver", config=config)
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>
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> # Construction from class
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> from spacy_experimental.coref.span_resolver_component import SpanResolver
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> span_resolver = SpanResolver(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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| `input_prefix` | The prefix to use for input `SpanGroup`s. Defaults to `coref_head_clusters`. ~~str~~ |
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| `output_prefix` | The prefix for predicted `SpanGroup`s. Defaults to `coref_clusters`. ~~str~~ |
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## SpanResolver.\_\_call\_\_ {#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__`](#call) and [`pipe`](#pipe) delegate to the [`predict`](#predict)
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and [`set_annotations`](#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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> span_resolver = nlp.add_pipe("experimental_span_resolver")
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> # This usually happens under the hood
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> processed = span_resolver(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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## SpanResolver.pipe {#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/span-resolver#call) and
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[`pipe`](/api/span-resolver#pipe) delegate to the
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[`predict`](/api/span-resolver#predict) and
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[`set_annotations`](/api/span-resolver#set_annotations) methods.
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> #### Example
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>
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> ```python
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> span_resolver = nlp.add_pipe("experimental_span_resolver")
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> for doc in span_resolver.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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## SpanResolver.initialize {#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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> span_resolver = nlp.add_pipe("experimental_span_resolver")
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> span_resolver.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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## SpanResolver.predict {#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. Predictions 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 an input `SpanGroup`, and the `int`s
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correspond to token indices.
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> #### Example
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>
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> ```python
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> span_resolver = nlp.add_pipe("experimental_span_resolver")
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> spans = span_resolver.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 spans for the `Doc`s. ~~List[MentionClusters]~~ |
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## SpanResolver.set_annotations {#set_annotations tag="method"}
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Modify a batch of documents, saving predictions using the output prefix in
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`Doc.spans`.
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> #### Example
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>
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> ```python
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> span_resolver = nlp.add_pipe("experimental_span_resolver")
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> spans = span_resolver.predict([doc1, doc2])
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> span_resolver.set_annotations([doc1, doc2], spans)
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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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| `spans` | The predicted spans for the `docs`. ~~List[MentionClusters]~~ |
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## SpanResolver.update {#update tag="method"}
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Learn from a batch of [`Example`](/api/example) objects. Delegates to
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[`predict`](/api/span-resolver#predict).
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> #### Example
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>
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> ```python
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> span_resolver = nlp.add_pipe("experimental_span_resolver")
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> optimizer = nlp.initialize()
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> losses = span_resolver.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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## SpanResolver.create_optimizer {#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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> span_resolver = nlp.add_pipe("experimental_span_resolver")
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> optimizer = span_resolver.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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## SpanResolver.use_params {#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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> span_resolver = nlp.add_pipe("experimental_span_resolver")
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> with span_resolver.use_params(optimizer.averages):
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> span_resolver.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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## SpanResolver.to_disk {#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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> span_resolver = nlp.add_pipe("experimental_span_resolver")
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> span_resolver.to_disk("/path/to/span_resolver")
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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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## SpanResolver.from_disk {#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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> span_resolver = nlp.add_pipe("experimental_span_resolver")
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> span_resolver.from_disk("/path/to/span_resolver")
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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 `SpanResolver` object. ~~SpanResolver~~ |
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## SpanResolver.to_bytes {#to_bytes tag="method"}
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> #### Example
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>
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> ```python
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> span_resolver = nlp.add_pipe("experimental_span_resolver")
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> span_resolver_bytes = span_resolver.to_bytes()
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> ```
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Serialize the pipe to a bytestring.
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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 `SpanResolver` object. ~~bytes~~ |
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## SpanResolver.from_bytes {#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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> span_resolver_bytes = span_resolver.to_bytes()
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> span_resolver = nlp.add_pipe("experimental_span_resolver")
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> span_resolver.from_bytes(span_resolver_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 `SpanResolver` object. ~~SpanResolver~~ |
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## Serialization fields {#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 = span_resolver.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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