From a44b7d4622108a42ddb95b62b642df6f142a3450 Mon Sep 17 00:00:00 2001 From: Paul O'Leary McCann Date: Tue, 27 Sep 2022 18:11:23 +0900 Subject: [PATCH] Add experimental coref docs (#11291) * Add experimental coref docs * Docs cleanup * Apply suggestions from code review Co-authored-by: Sofie Van Landeghem * Apply changes from code review * Fix prettier formatting It seems a period after a number made this think it was a list? * Update docs on examples for initialize * Add docs for coref scorers * Remove 3.4 notes from coref There won't be a "new" tag until it's in core. * Add docs for span cleaner * Fix docs * Fix docs to match spacy-experimental These weren't properly updated when the code was moved out of spacy core. * More doc fixes * Formatting * Update architectures * Fix links * Fix another link Co-authored-by: Sofie Van Landeghem Co-authored-by: svlandeg --- website/docs/api/architectures.md | 92 ++++++- website/docs/api/coref.md | 353 ++++++++++++++++++++++++ website/docs/api/pipeline-functions.md | 33 +++ website/docs/api/scorer.md | 59 ++++ website/docs/api/span-resolver.md | 356 +++++++++++++++++++++++++ website/meta/sidebars.json | 2 + 6 files changed, 889 insertions(+), 6 deletions(-) create mode 100644 website/docs/api/coref.md create mode 100644 website/docs/api/span-resolver.md diff --git a/website/docs/api/architectures.md b/website/docs/api/architectures.md index 2537faff6..4c5447f75 100644 --- a/website/docs/api/architectures.md +++ b/website/docs/api/architectures.md @@ -11,6 +11,7 @@ menu: - ['Text Classification', 'textcat'] - ['Span Classification', 'spancat'] - ['Entity Linking', 'entitylinker'] + - ['Coreference', 'coref-architectures'] --- A **model architecture** is a function that wires up a @@ -587,8 +588,8 @@ consists of either two or three subnetworks: run once for each batch. - **lower**: Construct a feature-specific vector for each `(token, feature)` pair. This is also run once for each batch. Constructing the state - representation is then a matter of summing the component features and - applying the non-linearity. + representation is then a matter of summing the component features and applying + the non-linearity. - **upper** (optional): A feed-forward network that predicts scores from the state representation. If not present, the output from the lower model is used as action scores directly. @@ -628,8 +629,8 @@ same signature, but the `use_upper` argument was `True` by default. > ``` Build a tagger model, using a provided token-to-vector component. The tagger -model adds a linear layer with softmax activation to predict scores given -the token vectors. +model adds a linear layer with softmax activation to predict scores given the +token vectors. | Name | Description | | ----------- | ------------------------------------------------------------------------------------------ | @@ -920,5 +921,84 @@ A function that reads an existing `KnowledgeBase` from file. A function that takes as input a [`KnowledgeBase`](/api/kb) and a [`Span`](/api/span) object denoting a named entity, and returns a list of plausible [`Candidate`](/api/kb/#candidate) objects. The default -`CandidateGenerator` uses the text of a mention to find its potential -aliases in the `KnowledgeBase`. Note that this function is case-dependent. +`CandidateGenerator` uses the text of a mention to find its potential aliases in +the `KnowledgeBase`. Note that this function is case-dependent. + +## Coreference {#coref-architectures tag="experimental"} + +A [`CoreferenceResolver`](/api/coref) component identifies tokens that refer to +the same entity. A [`SpanResolver`](/api/span-resolver) component infers spans +from single tokens. Together these components can be used to reproduce +traditional coreference models. You can also omit the `SpanResolver` if working +with only token-level clusters is acceptable. + +### spacy-experimental.Coref.v1 {#Coref tag="experimental"} + +> #### Example Config +> +> ```ini +> +> [model] +> @architectures = "spacy-experimental.Coref.v1" +> distance_embedding_size = 20 +> dropout = 0.3 +> hidden_size = 1024 +> depth = 2 +> antecedent_limit = 50 +> antecedent_batch_size = 512 +> +> [model.tok2vec] +> @architectures = "spacy-transformers.TransformerListener.v1" +> grad_factor = 1.0 +> upstream = "transformer" +> pooling = {"@layers":"reduce_mean.v1"} +> ``` + +The `Coref` model architecture is a Thinc `Model`. + +| Name | Description | +| ------------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | +| `tok2vec` | The [`tok2vec`](#tok2vec) layer of the model. ~~Model~~ | +| `distance_embedding_size` | A representation of the distance between candidates. ~~int~~ | +| `dropout` | The dropout to use internally. Unlike some Thinc models, this has separate dropout for the internal PyTorch layers. ~~float~~ | +| `hidden_size` | Size of the main internal layers. ~~int~~ | +| `depth` | Depth of the internal network. ~~int~~ | +| `antecedent_limit` | How many candidate antecedents to keep after rough scoring. This has a significant effect on memory usage. Typical values would be 50 to 200, or higher for very long documents. ~~int~~ | +| `antecedent_batch_size` | Internal batch size. ~~int~~ | +| **CREATES** | The model using the architecture. ~~Model[List[Doc], Floats2d]~~ | + +### spacy-experimental.SpanResolver.v1 {#SpanResolver tag="experimental"} + +> #### Example Config +> +> ```ini +> +> [model] +> @architectures = "spacy-experimental.SpanResolver.v1" +> hidden_size = 1024 +> distance_embedding_size = 64 +> conv_channels = 4 +> window_size = 1 +> max_distance = 128 +> prefix = "coref_head_clusters" +> +> [model.tok2vec] +> @architectures = "spacy-transformers.TransformerListener.v1" +> grad_factor = 1.0 +> upstream = "transformer" +> pooling = {"@layers":"reduce_mean.v1"} +> ``` + +The `SpanResolver` model architecture is a Thinc `Model`. Note that +`MentionClusters` is `List[List[Tuple[int, int]]]`. + +| Name | Description | +| ------------------------- | -------------------------------------------------------------------------------------------------------------------- | +| `tok2vec` | The [`tok2vec`](#tok2vec) layer of the model. ~~Model~~ | +| `hidden_size` | Size of the main internal layers. ~~int~~ | +| `distance_embedding_size` | A representation of the distance between two candidates. ~~int~~ | +| `conv_channels` | The number of channels in the internal CNN. ~~int~~ | +| `window_size` | The number of neighboring tokens to consider in the internal CNN. `1` means consider one token on each side. ~~int~~ | +| `max_distance` | The longest possible length of a predicted span. ~~int~~ | +| `prefix` | The prefix that indicates spans to use for input data. ~~string~~ | +| **CREATES** | The model using the architecture. ~~Model[List[Doc], List[MentionClusters]]~~ | diff --git a/website/docs/api/coref.md b/website/docs/api/coref.md new file mode 100644 index 000000000..8f54422d6 --- /dev/null +++ b/website/docs/api/coref.md @@ -0,0 +1,353 @@ +--- +title: CoreferenceResolver +tag: class,experimental +source: spacy-experimental/coref/coref_component.py +teaser: 'Pipeline component for word-level coreference resolution' +api_base_class: /api/pipe +api_string_name: coref +api_trainable: true +--- + +> #### Installation +> +> ```bash +> $ pip install -U spacy-experimental +> ``` + + + +This component is not yet integrated into spaCy core, and is available via the +extension package +[`spacy-experimental`](https://github.com/explosion/spacy-experimental) starting +in version 0.6.0. It exposes the component via +[entry points](/usage/saving-loading/#entry-points), so if you have the package +installed, using `factory = "experimental_coref"` in your +[training config](/usage/training#config) or +`nlp.add_pipe("experimental_coref")` will work out-of-the-box. + + + +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`](/api/span-resolver) to expand single tokens to spans. + +## Assigned Attributes {#assigned-attributes} + +Predictions will be saved to `Doc.spans` as a [`SpanGroup`](/api/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 {#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`](/api/language#add_pipe) or in your +[`config.cfg` for training](/usage/training#config). See the +[model architectures](/api/architectures#coref-architectures) documentation for +details on the architectures and their arguments and hyperparameters. + +> #### Example +> +> ```python +> 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`](https://thinc.ai/docs/api-model) powering the pipeline component. Defaults to [Coref](/api/architectures#Coref). ~~Model~~ | +| `span_cluster_prefix` | The prefix for the keys for clusters saved to `doc.spans`. Defaults to `coref_clusters`. ~~str~~ | + +## CoreferenceResolver.\_\_init\_\_ {#init tag="method"} + +> #### Example +> +> ```python +> # 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`](/api/language#add_pipe). + +| Name | Description | +| --------------------- | --------------------------------------------------------------------------------------------------- | +| `vocab` | The shared vocabulary. ~~Vocab~~ | +| `model` | The [`Model`](https://thinc.ai/docs/api-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\_\_ {#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__`](/api/coref#call) and [`pipe`](/api/coref#pipe) delegate to the +[`predict`](/api/coref#predict) and +[`set_annotations`](/api/coref#set_annotations) methods. + +> #### Example +> +> ```python +> 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 {#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__`](/api/coref#call) and +[`pipe`](/api/coref#pipe) delegate to the [`predict`](/api/coref#predict) and +[`set_annotations`](/api/coref#set_annotations) methods. + +> #### Example +> +> ```python +> 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 {#initialize tag="method"} + +Initialize the component for training. `get_examples` should be a function that +returns an iterable of [`Example`](/api/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](https://thinc.ai/docs/usage-models#validation) and +setting up the label scheme based on the data. This method is typically called +by [`Language.initialize`](/api/language#initialize). + +> #### Example +> +> ```python +> 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`](/api/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 {#predict tag="method"} + +Apply the component's model to a batch of [`Doc`](/api/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 `int`s, where each item corresponds to a cluster, and the `int`s correspond +to token indices. + +> #### Example +> +> ```python +> 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 {#set_annotations tag="method"} + +Modify a batch of documents, saving coreference clusters in `Doc.spans`. + +> #### Example +> +> ```python +> 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 {#update tag="method"} + +Learn from a batch of [`Example`](/api/example) objects. Delegates to +[`predict`](/api/coref#predict). + +> #### Example +> +> ```python +> coref = nlp.add_pipe("experimental_coref") +> optimizer = nlp.initialize() +> losses = coref.update(examples, sgd=optimizer) +> ``` + +| Name | Description | +| -------------- | ------------------------------------------------------------------------------------------------------------------------ | +| `examples` | A batch of [`Example`](/api/example) objects to learn from. ~~Iterable[Example]~~ | +| _keyword-only_ | | +| `drop` | The dropout rate. ~~float~~ | +| `sgd` | An optimizer. Will be created via [`create_optimizer`](#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 {#create_optimizer tag="method"} + +Create an optimizer for the pipeline component. + +> #### Example +> +> ```python +> coref = nlp.add_pipe("experimental_coref") +> optimizer = coref.create_optimizer() +> ``` + +| Name | Description | +| ----------- | ---------------------------- | +| **RETURNS** | The optimizer. ~~Optimizer~~ | + +## CoreferenceResolver.use_params {#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 +> +> ```python +> 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 {#to_disk tag="method"} + +Serialize the pipe to disk. + +> #### Example +> +> ```python +> 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](#serialization-fields) to exclude. ~~Iterable[str]~~ | + +## CoreferenceResolver.from_disk {#from_disk tag="method"} + +Load the pipe from disk. Modifies the object in place and returns it. + +> #### Example +> +> ```python +> 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](#serialization-fields) to exclude. ~~Iterable[str]~~ | +| **RETURNS** | The modified `CoreferenceResolver` object. ~~CoreferenceResolver~~ | + +## CoreferenceResolver.to_bytes {#to_bytes tag="method"} + +> #### Example +> +> ```python +> 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](#serialization-fields) to exclude. ~~Iterable[str]~~ | +| **RETURNS** | The serialized form of the `CoreferenceResolver` object. ~~bytes~~ | + +## CoreferenceResolver.from_bytes {#from_bytes tag="method"} + +Load the pipe from a bytestring. Modifies the object in place and returns it. + +> #### Example +> +> ```python +> 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](#serialization-fields) to exclude. ~~Iterable[str]~~ | +| **RETURNS** | The `CoreferenceResolver` object. ~~CoreferenceResolver~~ | + +## Serialization fields {#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 +> +> ```python +> data = coref.to_disk("/path", exclude=["vocab"]) +> ``` + +| Name | Description | +| ------- | -------------------------------------------------------------- | +| `vocab` | The shared [`Vocab`](/api/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. | diff --git a/website/docs/api/pipeline-functions.md b/website/docs/api/pipeline-functions.md index 1b7017ca7..070292782 100644 --- a/website/docs/api/pipeline-functions.md +++ b/website/docs/api/pipeline-functions.md @@ -153,3 +153,36 @@ whole pipeline has run. | `attrs` | A dict of the `Doc` attributes and the values to set them to. Defaults to `{"tensor": None, "_.trf_data": None}` to clean up after `tok2vec` and `transformer` components. ~~dict~~ | | `silent` | If `False`, show warnings if attributes aren't found or can't be set. Defaults to `True`. ~~bool~~ | | **RETURNS** | The modified `Doc` with the modified attributes. ~~Doc~~ | + +## span_cleaner {#span_cleaner tag="function,experimental"} + +Remove `SpanGroup`s from `doc.spans` based on a key prefix. This is used to +clean up after the [`CoreferenceResolver`](/api/coref) when it's paired with a +[`SpanResolver`](/api/span-resolver). + + + +This pipeline function is not yet integrated into spaCy core, and is available +via the extension package +[`spacy-experimental`](https://github.com/explosion/spacy-experimental) starting +in version 0.6.0. It exposes the component via +[entry points](/usage/saving-loading/#entry-points), so if you have the package +installed, using `factory = "span_cleaner"` in your +[training config](/usage/training#config) or `nlp.add_pipe("span_cleaner")` will +work out-of-the-box. + + + +> #### Example +> +> ```python +> config = {"prefix": "coref_head_clusters"} +> nlp.add_pipe("span_cleaner", config=config) +> doc = nlp("text") +> assert "coref_head_clusters_1" not in doc.spans +> ``` + +| Setting | Description | +| ----------- | ------------------------------------------------------------------------------------------------------------------------- | +| `prefix` | A prefix to check `SpanGroup` keys for. Any matching groups will be removed. Defaults to `"coref_head_clusters"`. ~~str~~ | +| **RETURNS** | The modified `Doc` with any matching spans removed. ~~Doc~~ | diff --git a/website/docs/api/scorer.md b/website/docs/api/scorer.md index 8dbe3b276..ca3462aa9 100644 --- a/website/docs/api/scorer.md +++ b/website/docs/api/scorer.md @@ -270,3 +270,62 @@ Compute micro-PRF and per-entity PRF scores. | Name | Description | | ---------- | ------------------------------------------------------------------------------------------------------------------- | | `examples` | The `Example` objects holding both the predictions and the correct gold-standard annotations. ~~Iterable[Example]~~ | + +## score_coref_clusters {#score_coref_clusters tag="experimental"} + +Returns LEA ([Moosavi and Strube, 2016](https://aclanthology.org/P16-1060/)) PRF +scores for coreference clusters. + + + +Note this scoring function is not yet included in spaCy core - for details, see +the [CoreferenceResolver](/api/coref) docs. + + + +> #### Example +> +> ```python +> scores = score_coref_clusters( +> examples, +> span_cluster_prefix="coref_clusters", +> ) +> print(scores["coref_f"]) +> ``` + +| Name | Description | +| --------------------- | ------------------------------------------------------------------------------------------------------------------- | +| `examples` | The `Example` objects holding both the predictions and the correct gold-standard annotations. ~~Iterable[Example]~~ | +| _keyword-only_ | | +| `span_cluster_prefix` | The prefix used for spans representing coreference clusters. ~~str~~ | +| **RETURNS** | A dictionary containing the scores. ~~Dict[str, Optional[float]]~~ | + +## score_span_predictions {#score_span_predictions tag="experimental"} + +Return accuracy for reconstructions of spans from single tokens. Only exactly +correct predictions are counted as correct, there is no partial credit for near +answers. Used by the [SpanResolver](/api/span-resolver). + + + +Note this scoring function is not yet included in spaCy core - for details, see +the [SpanResolver](/api/span-resolver) docs. + + + +> #### Example +> +> ```python +> scores = score_span_predictions( +> examples, +> output_prefix="coref_clusters", +> ) +> print(scores["span_coref_clusters_accuracy"]) +> ``` + +| Name | Description | +| --------------- | ------------------------------------------------------------------------------------------------------------------- | +| `examples` | The `Example` objects holding both the predictions and the correct gold-standard annotations. ~~Iterable[Example]~~ | +| _keyword-only_ | | +| `output_prefix` | The prefix used for spans representing the final predicted spans. ~~str~~ | +| **RETURNS** | A dictionary containing the scores. ~~Dict[str, Optional[float]]~~ | diff --git a/website/docs/api/span-resolver.md b/website/docs/api/span-resolver.md new file mode 100644 index 000000000..3e992cd03 --- /dev/null +++ b/website/docs/api/span-resolver.md @@ -0,0 +1,356 @@ +--- +title: SpanResolver +tag: class,experimental +source: spacy-experimental/coref/span_resolver_component.py +teaser: 'Pipeline component for resolving tokens into spans' +api_base_class: /api/pipe +api_string_name: span_resolver +api_trainable: true +--- + +> #### Installation +> +> ```bash +> $ pip install -U spacy-experimental +> ``` + + + +This component not yet integrated into spaCy core, and is available via the +extension package +[`spacy-experimental`](https://github.com/explosion/spacy-experimental) starting +in version 0.6.0. It exposes the component via +[entry points](/usage/saving-loading/#entry-points), so if you have the package +installed, using `factory = "experimental_span_resolver"` in your +[training config](/usage/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](/api/coref). The input and output keys used to store +`Span` objects are configurable. + +## Assigned Attributes {#assigned-attributes} + +Predictions will be saved to `Doc.spans` as [`SpanGroup`s](/api/spangroup). + +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 {#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`](/api/language#add_pipe) or in your +[`config.cfg` for training](/usage/training#config). See the +[model architectures](/api/architectures#coref-architectures) documentation for +details on the architectures and their arguments and hyperparameters. + +> #### Example +> +> ```python +> 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`](https://thinc.ai/docs/api-model) powering the pipeline component. Defaults to [SpanResolver](/api/architectures#SpanResolver). ~~Model~~ | +| `input_prefix` | The prefix to use for input `SpanGroup`s. Defaults to `coref_head_clusters`. ~~str~~ | +| `output_prefix` | The prefix for predicted `SpanGroup`s. Defaults to `coref_clusters`. ~~str~~ | + +## SpanResolver.\_\_init\_\_ {#init tag="method"} + +> #### Example +> +> ```python +> # 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`](/api/language#add_pipe). + +| Name | Description | +| --------------- | --------------------------------------------------------------------------------------------------- | +| `vocab` | The shared vocabulary. ~~Vocab~~ | +| `model` | The [`Model`](https://thinc.ai/docs/api-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 `SpanGroup`s. Defaults to `coref_head_clusters`. ~~str~~ | +| `output_prefix` | The prefix for predicted `SpanGroup`s. Defaults to `coref_clusters`. ~~str~~ | + +## SpanResolver.\_\_call\_\_ {#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__`](#call) and [`pipe`](#pipe) delegate to the [`predict`](#predict) +and [`set_annotations`](#set_annotations) methods. + +> #### Example +> +> ```python +> 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 {#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__`](/api/span-resolver#call) and +[`pipe`](/api/span-resolver#pipe) delegate to the +[`predict`](/api/span-resolver#predict) and +[`set_annotations`](/api/span-resolver#set_annotations) methods. + +> #### Example +> +> ```python +> 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 tag="method"} + +Initialize the component for training. `get_examples` should be a function that +returns an iterable of [`Example`](/api/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](https://thinc.ai/docs/usage-models#validation) and +setting up the label scheme based on the data. This method is typically called +by [`Language.initialize`](/api/language#initialize). + +> #### Example +> +> ```python +> 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`](/api/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 {#predict tag="method"} + +Apply the component's model to a batch of [`Doc`](/api/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 `int`s, where each item corresponds to an input `SpanGroup`, and the `int`s +correspond to token indices. + +> #### Example +> +> ```python +> 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 `Doc`s. ~~List[MentionClusters]~~ | + +## SpanResolver.set_annotations {#set_annotations tag="method"} + +Modify a batch of documents, saving predictions using the output prefix in +`Doc.spans`. + +> #### Example +> +> ```python +> 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 {#update tag="method"} + +Learn from a batch of [`Example`](/api/example) objects. Delegates to +[`predict`](/api/span-resolver#predict). + +> #### Example +> +> ```python +> 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`](/api/example) objects to learn from. ~~Iterable[Example]~~ | +| _keyword-only_ | | +| `drop` | The dropout rate. ~~float~~ | +| `sgd` | An optimizer. Will be created via [`create_optimizer`](#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_optimizer tag="method"} + +Create an optimizer for the pipeline component. + +> #### Example +> +> ```python +> span_resolver = nlp.add_pipe("experimental_span_resolver") +> optimizer = span_resolver.create_optimizer() +> ``` + +| Name | Description | +| ----------- | ---------------------------- | +| **RETURNS** | The optimizer. ~~Optimizer~~ | + +## SpanResolver.use_params {#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 +> +> ```python +> 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 {#to_disk tag="method"} + +Serialize the pipe to disk. + +> #### Example +> +> ```python +> 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](#serialization-fields) to exclude. ~~Iterable[str]~~ | + +## SpanResolver.from_disk {#from_disk tag="method"} + +Load the pipe from disk. Modifies the object in place and returns it. + +> #### Example +> +> ```python +> 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](#serialization-fields) to exclude. ~~Iterable[str]~~ | +| **RETURNS** | The modified `SpanResolver` object. ~~SpanResolver~~ | + +## SpanResolver.to_bytes {#to_bytes tag="method"} + +> #### Example +> +> ```python +> 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](#serialization-fields) to exclude. ~~Iterable[str]~~ | +| **RETURNS** | The serialized form of the `SpanResolver` object. ~~bytes~~ | + +## SpanResolver.from_bytes {#from_bytes tag="method"} + +Load the pipe from a bytestring. Modifies the object in place and returns it. + +> #### Example +> +> ```python +> 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](#serialization-fields) to exclude. ~~Iterable[str]~~ | +| **RETURNS** | The `SpanResolver` object. ~~SpanResolver~~ | + +## Serialization fields {#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 +> +> ```python +> data = span_resolver.to_disk("/path", exclude=["vocab"]) +> ``` + +| Name | Description | +| ------- | -------------------------------------------------------------- | +| `vocab` | The shared [`Vocab`](/api/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. | diff --git a/website/meta/sidebars.json b/website/meta/sidebars.json index 06fce7742..2d8745d77 100644 --- a/website/meta/sidebars.json +++ b/website/meta/sidebars.json @@ -94,6 +94,7 @@ "label": "Pipeline", "items": [ { "text": "AttributeRuler", "url": "/api/attributeruler" }, + { "text": "CoreferenceResolver", "url": "/api/coref" }, { "text": "DependencyParser", "url": "/api/dependencyparser" }, { "text": "EditTreeLemmatizer", "url": "/api/edittreelemmatizer" }, { "text": "EntityLinker", "url": "/api/entitylinker" }, @@ -104,6 +105,7 @@ { "text": "SentenceRecognizer", "url": "/api/sentencerecognizer" }, { "text": "Sentencizer", "url": "/api/sentencizer" }, { "text": "SpanCategorizer", "url": "/api/spancategorizer" }, + { "text": "SpanResolver", "url": "/api/span-resolver" }, { "text": "SpanRuler", "url": "/api/spanruler" }, { "text": "Tagger", "url": "/api/tagger" }, { "text": "TextCategorizer", "url": "/api/textcategorizer" },