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.Model

Initialize a model for the pipe. The model should implement the thinc.neural.Model API, and should contain a field tok2vec that contains the context encoder. Wrappers are under development for most major machine learning libraries.

Name	Type	Description
**kwargs	-	Parameters for initializing the model
RETURNS	object	The initialized model.

EntityLinker.__init__

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.

Example

# Construction via create_pipe
entity_linker = nlp.create_pipe("entity_linker")

# Construction from class
from spacy.pipeline import EntityLinker
entity_linker = EntityLinker(nlp.vocab)
entity_linker.from_disk("/path/to/model")

Name	Type	Description
vocab	Vocab	The shared vocabulary.
model	thinc.neural.Model / True	The model powering the pipeline component. If no model is supplied, the model is created when you call begin_training, from_disk or from_bytes.
hidden_width	int	Width of the hidden layer of the entity linking model, defaults to 128.
incl_prior	bool	Whether or not to include prior probabilities in the model. Defaults to True.
incl_context	bool	Whether or not to include the local context in the model (if not: only prior probabilities are used). Defaults to True.
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	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 documents and gold-standard information, updating both the pipe's entity linking model and context encoder. Delegates to predict and get_loss.

Example

entity_linker = EntityLinker(nlp.vocab)
losses = {}
optimizer = nlp.begin_training()
entity_linker.update([doc1, doc2], [gold1, gold2], losses=losses, sgd=optimizer)

Name	Type	Description
docs	iterable	A batch of documents to learn from.
golds	iterable	The gold-standard data. Must have the same length as docs.
drop	float	The dropout rate, used both for the EL model and the context encoder.
sgd	callable	The optimizer for the EL model. Should take two arguments weights and gradient, and an optional ID.
losses	dict	Optional record of the loss during training. The value keyed by the model's name is updated.

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. If no model has been initialized yet, the model is added. 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
gold_tuples	iterable	Optional gold-standard annotations from which to construct GoldParse objects.
pipeline	list	Optional list of pipeline components that this component is part of.
sgd	callable	An optional optimizer. Should take two arguments weights and gradient, and an optional ID. Will be created via EntityLinker if not set.
RETURNS	callable	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.