spaCy/website/docs/api/entitylinker.md
2020-07-31 17:02:54 +02:00

16 KiB

title tag source new teaser api_base_class api_string_name api_trainable
EntityLinker class spacy/pipeline/entity_linker.py 2.2 Pipeline component for named entity linking and disambiguation /api/pipe entity_linker true

Config and implementation

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 or in your config.cfg for training. See the model architectures documentation for details on the architectures and their arguments and hyperparameters.

Example

from spacy.pipeline.entity_linker import DEFAULT_NEL_MODEL
config = {
   "kb": None,
   "labels_discard": [],
   "incl_prior": True,
   "incl_context": True,
   "model": DEFAULT_NEL_MODEL,
}
nlp.add_pipe("entity_linker", config=config)
Setting Type Description Default
kb KnowledgeBase None
labels_discard Iterable[str] []
incl_prior bool  True
incl_context bool True
model Model The model to use. EntityLinker
https://github.com/explosion/spaCy/blob/develop/spacy/pipeline/entity_linker.py

EntityLinker.__init__

Example

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

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

# Construction from class
from spacy.pipeline import EntityLinker
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.add_pipe.

Name Type Description
vocab Vocab The shared vocabulary.
model Model The Model powering the pipeline component.
name str String name of the component instance. Used to add entries to the losses during training.
keyword-only
kb KnowlegeBase
labels_discard Iterable[str]
incl_prior bool
incl_context bool

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

doc = nlp("This is a sentence.")
entity_linker = nlp.add_pipe("entity_linker")
# 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 = nlp.add_pipe("entity_linker")
for doc in entity_linker.pipe(docs, batch_size=50):
    pass
Name Type Description
stream Iterable[Doc] A stream of documents.
keyword-only
batch_size int The number of texts to buffer. Defaults to 128.
YIELDS Doc Processed documents in the order of the original text.

EntityLinker.begin_training

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

Example

entity_linker = nlp.add_pipe("entity_linker", last=True)
entity_linker.set_kb(kb)
optimizer = entity_linker.begin_training(pipeline=nlp.pipeline)
Name Type Description
get_examples Callable[[], Iterable[Example]] Optional function that returns gold-standard annotations in the form of Example objects.
keyword-only
pipeline List[Tuple[str, Callable]] Optional list of pipeline components that this component is part of.
sgd Optimizer An optional optimizer. Will be created via create_optimizer if not set.
RETURNS Optimizer The optimizer.

EntityLinker.predict

Apply the pipeline's model to a batch of docs, without modifying them. Returns the KB IDs for each entity in each doc, including NIL if there is no prediction.

Example

entity_linker = nlp.add_pipe("entity_linker")
kb_ids = entity_linker.predict([doc1, doc2])
Name Type Description
docs Iterable[Doc] The documents to predict.
RETURNS List[str] The predicted KB identifiers for the entities in the docs.

EntityLinker.set_annotations

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

Example

entity_linker = nlp.add_pipe("entity_linker")
kb_ids = entity_linker.predict([doc1, doc2])
entity_linker.set_annotations([doc1, doc2], kb_ids)
Name Type Description
docs Iterable[Doc] The documents to modify.
kb_ids List[str] The knowledge base identifiers for the entities in the docs, predicted by EntityLinker.predict.

EntityLinker.update

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

Example

entity_linker = nlp.add_pipe("entity_linker")
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 The optimizer.
losses Dict[str, float] Optional record of the loss during training. Updated using the component name as the key.
RETURNS Dict[str, float] The updated losses dictionary.

EntityLinker.set_kb

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

Example

entity_linker = nlp.add_pipe("entity_linker")
entity_linker.set_kb(kb)
Name Type Description
kb KnowledgeBase The KnowledgeBase.

EntityLinker.create_optimizer

Create an optimizer for the pipeline component.

Example

entity_linker = nlp.add_pipe("entity_linker")
optimizer = entity_linker.create_optimizer()
Name Type Description
RETURNS Optimizer The optimizer.

EntityLinker.use_params

Modify the pipe's model, to use the given parameter values. At the end of the context, the original parameters are restored.

Example

entity_linker = nlp.add_pipe("entity_linker")
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.

EntityLinker.to_disk

Serialize the pipe to disk.

Example

entity_linker = nlp.add_pipe("entity_linker")
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.
keyword-only
exclude Iterable[str] 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 = nlp.add_pipe("entity_linker")
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.
keyword-only
exclude Iterable[str] 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.