spaCy/website/docs/api/entityrecognizer.md

title	tag	source
EntityRecognizer	class	spacy/pipeline/pipes.pyx

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

Default config

This is the default configuration used to initialize the model powering the pipeline component. See the model architectures documentation for details on the architectures and their arguments and hyperparameters. To learn more about how to customize the config and train custom models, check out the training config docs.

https://github.com/explosion/spaCy/blob/develop/spacy/pipeline/defaults/ner_defaults.cfg

EntityRecognizer.__init__

Example

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

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

# Construction from class with custom model from file
from spacy.pipeline import EntityRecognizer
model = util.load_config("model.cfg", create_objects=True)["model"]
ner = EntityRecognizer(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	EntityRecognizer	The newly constructed object.

EntityRecognizer.__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

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

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

EntityRecognizer.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

ner = EntityRecognizer(nlp.vocab)
for doc in ner.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.

EntityRecognizer.predict

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

Example

ner = EntityRecognizer(nlp.vocab)
scores = ner.predict([doc1, doc2])

Name	Type	Description
docs	Iterable[Doc]	The documents to predict.
RETURNS	List[StateClass]	List of syntax.StateClass objects. syntax.StateClass is a helper class for the parse state (internal).

EntityRecognizer.set_annotations

Modify a batch of documents, using pre-computed scores.

Example

ner = EntityRecognizer(nlp.vocab)
scores = ner.predict([doc1, doc2])
ner.set_annotations([doc1, doc2], scores)

Name	Type	Description
docs	Iterable[Doc]	The documents to modify.
scores	List[StateClass]	The scores to set, produced by EntityRecognizer.predict.

EntityRecognizer.update

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

Example

ner = EntityRecognizer(nlp.vocab, ner_model)
optimizer = nlp.begin_training()
losses = ner.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 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.

EntityRecognizer.get_loss

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

Example

ner = EntityRecognizer(nlp.vocab)
scores = ner.predict([eg.predicted for eg in examples])
loss, d_loss = ner.get_loss(examples, scores)

Name	Type	Description
examples	Iterable[Example]	The batch of examples.
scores	List[StateClass]	Scores representing the model's predictions.
RETURNS	tuple	The loss and the gradient, i.e. (loss, gradient).

EntityRecognizer.begin_training

Initialize the pipe for training, using data examples if available. Return an Optimizer object.

Example

ner = EntityRecognizer(nlp.vocab)
nlp.pipeline.append(ner)
optimizer = ner.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.

EntityRecognizer.create_optimizer

Create an optimizer for the pipeline component.

Example

ner = EntityRecognizer(nlp.vocab)
optimizer = ner.create_optimizer()

Name	Type	Description
RETURNS	Optimizer	The Optimizer object.

EntityRecognizer.use_params

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

Example

ner = EntityRecognizer(nlp.vocab)
with ner.use_params(optimizer.averages):
    ner.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.

EntityRecognizer.add_label

Add a new label to the pipe.

Example

ner = EntityRecognizer(nlp.vocab)
ner.add_label("MY_LABEL")

Name	Type	Description
label	str	The label to add.

EntityRecognizer.to_disk

Serialize the pipe to disk.

Example

ner = EntityRecognizer(nlp.vocab)
ner.to_disk("/path/to/ner")

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.

EntityRecognizer.from_disk

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

Example

ner = EntityRecognizer(nlp.vocab)
ner.from_disk("/path/to/ner")

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	EntityRecognizer	The modified EntityRecognizer object.

EntityRecognizer.to_bytes

Example

ner = EntityRecognizer(nlp.vocab)
ner_bytes = ner.to_bytes()

Serialize the pipe to a bytestring.

Name	Type	Description
exclude	list	String names of serialization fields to exclude.
RETURNS	bytes	The serialized form of the EntityRecognizer object.

EntityRecognizer.from_bytes

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

Example

ner_bytes = ner.to_bytes()
ner = EntityRecognizer(nlp.vocab)
ner.from_bytes(ner_bytes)

Name	Type	Description
bytes_data	bytes	The data to load from.
exclude	list	String names of serialization fields to exclude.
RETURNS	EntityRecognizer	The EntityRecognizer object.

EntityRecognizer.labels

The labels currently added to the component.

Example

ner.add_label("MY_LABEL")
assert "MY_LABEL" in ner.labels

Name	Type	Description
RETURNS	tuple	The labels added to the component.

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 = ner.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.