spaCy/website/docs/api/entitylinker.md

title	teaser	tag	source	new
EntityLinker	Functionality to disambiguate a named entity in text to a unique knowledge base identifier.	class	spacy/pipeline/pipes.pyx	2.2

This class is a subclass of Pipe and follows the same API. The pipeline component is available in the processing pipeline via the ID "entity_linker".

EntityLinker.__init__

Example

# Construction via create_pipe with default model
entity_linker = nlp.create_pipe("entity_linker")

# Construction via create_pipe with custom model
config = {"model": {"@architectures": "my_el"}}
entity_linker = nlp.create_pipe("entity_linker", config)

# Construction from class with custom model from file
from spacy.pipeline import EntityLinker
model = util.load_config("model.cfg", create_objects=True)["model"]
entity_linker = EntityLinker(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.create_pipe.

Name	Type	Description
vocab	Vocab	The shared vocabulary.
model	Model	The Model powering the pipeline component.
**cfg	-	Configuration parameters.

| RETURNS | EntityLinker | The newly constructed object. |

EntityLinker.__call__

Apply the pipe to one document. The document is modified in place, and returned. This usually happens under the hood when the nlp object is called on a text and all pipeline components are applied to the Doc in order. Both __call__ and pipe delegate to the predict and set_annotations methods.

Example

entity_linker = EntityLinker(nlp.vocab)
doc = nlp("This is a sentence.")
# This usually happens under the hood
processed = entity_linker(doc)

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

EntityLinker.pipe

Apply the pipe to a stream of documents. This usually happens under the hood when the nlp object is called on a text and all pipeline components are applied to the Doc in order. Both __call__ and pipe delegate to the predict and set_annotations methods.

Example

entity_linker = EntityLinker(nlp.vocab)
for doc in entity_linker.pipe(docs, batch_size=50):
    pass

Name	Type	Description
stream	Iterable[Doc]	A stream of documents.
batch_size	int	The number of texts to buffer. Defaults to 128.
YIELDS	Doc	Processed documents in the order of the original text.

EntityLinker.predict

Apply the pipeline's model to a batch of docs, without modifying them.

Example

entity_linker = EntityLinker(nlp.vocab)
kb_ids, tensors = entity_linker.predict([doc1, doc2])

Name	Type	Description
docs	iterable	The documents to predict.
RETURNS	tuple	A (kb_ids, tensors) tuple where kb_ids are the model's predicted KB identifiers for the entities in the docs, and tensors are the token representations used to predict these identifiers.

EntityLinker.set_annotations

Modify a batch of documents, using pre-computed entity IDs for a list of named entities.

Example

entity_linker = EntityLinker(nlp.vocab)
kb_ids, tensors = entity_linker.predict([doc1, doc2])
entity_linker.set_annotations([doc1, doc2], kb_ids, tensors)

Name	Type	Description
docs	iterable	The documents to modify.
kb_ids	iterable	The knowledge base identifiers for the entities in the docs, predicted by EntityLinker.predict.
tensors	iterable	The token representations used to predict the identifiers.

EntityLinker.update

Learn from a batch of Example objects, updating both the pipe's entity linking model and context encoder. Delegates to predict and get_loss.

Example

entity_linker = EntityLinker(nlp.vocab, nel_model)
optimizer = nlp.begin_training()
losses = entity_linker.update(examples, sgd=optimizer)

Name	Type	Description
examples	Iterable[Example]	A batch of Example objects to learn from.
keyword-only
drop	float	The dropout rate.
set_annotations	bool	Whether or not to update the Example objects with the predictions, delegating to set_annotations.
sgd	Optimizer	Optimizer object.
losses	Dict[str, float]	Optional record of the loss during training. The value keyed by the model's name is updated.
RETURNS	Dict[str, float]	The updated losses dictionary.

EntityLinker.get_loss

Find the loss and gradient of loss for the entities in a batch of documents and their predicted scores.

Example

entity_linker = EntityLinker(nlp.vocab)
kb_ids, tensors = entity_linker.predict(docs)
loss, d_loss = entity_linker.get_loss(docs, [gold1, gold2], kb_ids, tensors)

Name	Type	Description
docs	iterable	The batch of documents.
golds	iterable	The gold-standard data. Must have the same length as docs.
kb_ids	iterable	KB identifiers representing the model's predictions.
tensors	iterable	The token representations used to predict the identifiers
RETURNS	tuple	The loss and the gradient, i.e. (loss, gradient).

EntityLinker.set_kb

Define the knowledge base (KB) used for disambiguating named entities to KB identifiers.

Example

entity_linker = EntityLinker(nlp.vocab)
entity_linker.set_kb(kb)

Name	Type	Description
kb	KnowledgeBase	The KnowledgeBase.

EntityLinker.begin_training

Initialize the pipe for training, using data examples if available. Return an Optimizer object. Before calling this method, a knowledge base should have been defined with set_kb.

Example

entity_linker = EntityLinker(nlp.vocab)
entity_linker.set_kb(kb)
nlp.add_pipe(entity_linker, last=True)
optimizer = entity_linker.begin_training(pipeline=nlp.pipeline)

Name	Type	Description
get_examples	Iterable[Example]	Optional gold-standard annotations in the form of Example objects.
pipeline	List[(str, callable)]	Optional list of pipeline components that this component is part of.
sgd	Optimizer	An optional Optimizer object. Will be created via create_optimizer if not set.
RETURNS	Optimizer	An optimizer.

EntityLinker.create_optimizer

Create an optimizer for the pipeline component.

Example

entity_linker = EntityLinker(nlp.vocab)
optimizer = entity_linker.create_optimizer()

Name	Type	Description
RETURNS	callable	The optimizer.

EntityLinker.use_params

Modify the pipe's EL model, to use the given parameter values.

Example

entity_linker = EntityLinker(nlp.vocab)
with entity_linker.use_params(optimizer.averages):
    entity_linker.to_disk("/best_model")

Name	Type	Description
params	dict	The parameter values to use in the model. At the end of the context, the original parameters are restored.

EntityLinker.to_disk

Serialize the pipe to disk.

Example

entity_linker = EntityLinker(nlp.vocab)
entity_linker.to_disk("/path/to/entity_linker")

Name	Type	Description
path	str / Path	A path to a directory, which will be created if it doesn't exist. Paths may be either strings or Path-like objects.
exclude	list	String names of serialization fields to exclude.

EntityLinker.from_disk

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

Example

entity_linker = EntityLinker(nlp.vocab)
entity_linker.from_disk("/path/to/entity_linker")

Name	Type	Description
path	str / Path	A path to a directory. Paths may be either strings or Path-like objects.
exclude	list	String names of serialization fields to exclude.
RETURNS	EntityLinker	The modified EntityLinker object.

Serialization fields

During serialization, spaCy will export several data fields used to restore different aspects of the object. If needed, you can exclude them from serialization by passing in the string names via the exclude argument.

Example

data = entity_linker.to_disk("/path", exclude=["vocab"])

Name	Description
vocab	The shared Vocab.
cfg	The config file. You usually don't want to exclude this.
model	The binary model data. You usually don't want to exclude this.
kb	The knowledge base. You usually don't want to exclude this.