spaCy/website/docs/api/entityrecognizer.md

title	tag	source	teaser	api_base_class	api_string_name	api_trainable
EntityRecognizer	class	spacy/pipeline/ner.pyx	Pipeline component for named entity recognition	/api/pipe	ner	true

A transition-based named entity recognition component. The entity recognizer identifies non-overlapping labelled spans of tokens. The transition-based algorithm used encodes certain assumptions that are effective for "traditional" named entity recognition tasks, but may not be a good fit for every span identification problem. Specifically, the loss function optimizes for whole entity accuracy, so if your inter-annotator agreement on boundary tokens is low, the component will likely perform poorly on your problem. The transition-based algorithm also assumes that the most decisive information about your entities will be close to their initial tokens. If your entities are long and characterized by tokens in their middle, the component will likely not be a good fit for your task.

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.ner import DEFAULT_NER_MODEL
config = {
   "moves": None,
   "update_with_oracle_cut_size": 100,
   "model": DEFAULT_NER_MODEL,
}
nlp.add_pipe("ner", config=config)

Setting	Type	Description	Default
moves	List[str]	A list of transition names. Inferred from the data if not provided.
update_with_oracle_cut_size	int	During training, cut long sequences into shorter segments by creating intermediate states based on the gold-standard history. The model is not very sensitive to this parameter, so you usually won't need to change it.	100
model	Model	The model to use.	TransitionBasedParser

https://github.com/explosion/spaCy/blob/develop/spacy/pipeline/ner.pyx

EntityRecognizer.__init__

Example

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

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

# Construction from class
from spacy.pipeline import EntityRecognizer
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.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.
moves	List[str]	A list of transition names. Inferred from the data if not provided.
keyword-only
update_with_oracle_cut_size	int	During training, cut long sequences into shorter segments by creating intermediate states based on the gold-standard history. The model is not very sensitive to this parameter, so you usually won't need to change it. 100 is a good default.

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

doc = nlp("This is a sentence.")
ner = nlp.add_pipe("ner")
# 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 = nlp.add_pipe("ner")
for doc in ner.pipe(docs, batch_size=50):
    pass

Name	Type	Description
docs	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.

EntityRecognizer.begin_training

Initialize the component for training and return an Optimizer. get_examples should be a function that returns an iterable of Example objects. 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 and setting up the label scheme based on the data.

Example

ner = nlp.add_pipe("ner")
optimizer = ner.begin_training(lambda: [], 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.

EntityRecognizer.predict

Apply the component's model to a batch of Doc objects, without modifying them.

Example

ner = nlp.add_pipe("ner")
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 Doc objects, using pre-computed scores.

Example

ner = nlp.add_pipe("ner")
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 = nlp.add_pipe("ner")
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.
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.

EntityRecognizer.get_loss

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

Example

ner = nlp.add_pipe("ner")
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[float, float]	The loss and the gradient, i.e. (loss, gradient).

EntityRecognizer.score

Score a batch of examples.

Example

scores = ner.score(examples)

Name	Type	Description
examples	Iterable[Example]	The examples to score.
RETURNS	Dict[str, Any]	The scores, produced by Scorer.score_spans.

EntityRecognizer.create_optimizer

Create an optimizer for the pipeline component.

Example

ner = nlp.add_pipe("ner")
optimizer = ner.create_optimizer()

Name	Type	Description
RETURNS	Optimizer	The optimizer.

EntityRecognizer.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

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.

EntityRecognizer.add_label

Add a new label to the pipe.

Example

ner = nlp.add_pipe("ner")
ner.add_label("MY_LABEL")

Name	Type	Description
label	str	The label to add.
RETURNS	int	0 if the label is already present, otherwise 1.

EntityRecognizer.to_disk

Serialize the pipe to disk.

Example

ner = nlp.add_pipe("ner")
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.
keyword-only
exclude	Iterable[str]	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 = nlp.add_pipe("ner")
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.
keyword-only
exclude	Iterable[str]	String names of serialization fields to exclude.
RETURNS	EntityRecognizer	The modified EntityRecognizer object.

EntityRecognizer.to_bytes

Example

ner = nlp.add_pipe("ner")
ner_bytes = ner.to_bytes()

Serialize the pipe to a bytestring.

Name	Type	Description
keyword-only
exclude	Iterable[str]	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 = nlp.add_pipe("ner")
ner.from_bytes(ner_bytes)

Name	Type	Description
bytes_data	bytes	The data to load from.
keyword-only
exclude	Iterable[str]	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.