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,
   "incorrect_spans_key": "incorrect_spans",
}
nlp.add_pipe("ner", config=config)

Setting	Description
moves	A list of transition names. Inferred from the data if not provided. Defaults to None. Optional[List[str]]
update_with_oracle_cut_size	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. Defaults to 100. int
model	The Model powering the pipeline component. Defaults to TransitionBasedParser. Model[List[Doc], List[Floats2d]]
incorrect_spans_key	This key refers to a SpanGroup in doc.spans that specifies incorrect spans. The NER wiill learn not to predict (exactly) those spans. Defaults to None. Optional[str]

%%GITHUB_SPACY/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	Description
vocab	The shared vocabulary. Vocab
model	The Model powering the pipeline component. Model[List[Doc], List[Floats2d]]
name	String name of the component instance. Used to add entries to the losses during training. str
moves	A list of transition names. Inferred from the data if set to None, which is the default. Optional[List[str]]
keyword-only
update_with_oracle_cut_size	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. Defaults to 100. int
incorrect_spans_key	Identifies spans that are known to be incorrect entity annotations. The incorrect entity annotations can be stored in the span group, under this key. Defaults to None. Optional[str]

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

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	Description
docs	A stream of documents. Iterable[Doc]
keyword-only
batch_size	The number of documents to buffer. Defaults to 128. int
YIELDS	The processed documents in order. Doc

EntityRecognizer.initialize

Initialize the component for training. 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. This method is typically called by Language.initialize and lets you customize arguments it receives via the [initialize.components] block in the config.

This method was previously called begin_training.

Example

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

### config.cfg
[initialize.components.ner]

[initialize.components.ner.labels]
@readers = "spacy.read_labels.v1"
path = "corpus/labels/ner.json

Name	Description
get_examples	Function that returns gold-standard annotations in the form of Example objects. Callable], Iterable[Example
keyword-only
nlp	The current nlp object. Defaults to None. Optional[Language]
labels	The label information to add to the component, as provided by the label_data property after initialization. To generate a reusable JSON file from your data, you should run the init labels command. If no labels are provided, the get_examples callback is used to extract the labels from the data, which may be a lot slower. Optional[Dict[str, Dict[str, int]]]

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	Description
docs	The documents to predict. Iterable[Doc]
RETURNS	A helper class for the parse state (internal). StateClass

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	Description
docs	The documents to modify. Iterable[Doc]
scores	The scores to set, produced by EntityRecognizer.predict. Returns an internal helper class for the parse state. List[StateClass]

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.initialize()
losses = ner.update(examples, sgd=optimizer)

Name	Description
examples	A batch of Example objects to learn from. Iterable[Example]
keyword-only
drop	The dropout rate. float
sgd	An optimizer. Will be created via create_optimizer if not set. Optional[Optimizer]
losses	Optional record of the loss during training. Updated using the component name as the key. Optional[Dict[str, float]]
RETURNS	The updated losses dictionary. Dict[str, float]

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

EntityRecognizer.score

Score a batch of examples.

Example

scores = ner.score(examples)

Name	Description
examples	The examples to score. Iterable[Example]
RETURNS	The scores. Dict[str, Union[float, Dict[str, float]]]

EntityRecognizer.create_optimizer

Create an optimizer for the pipeline component.

Example

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

Name	Description
RETURNS	The optimizer. 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	Description
params	The parameter values to use in the model. dict

EntityRecognizer.add_label

Add a new label to the pipe. Note that you don't have to call this method if you provide a representative data sample to the initialize method. In this case, all labels found in the sample will be automatically added to the model, and the output dimension will be inferred automatically.

Example

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

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

EntityRecognizer.set_output

Change the output dimension of the component's model by calling the model's attribute resize_output. This is a function that takes the original model and the new output dimension nO, and changes the model in place. When resizing an already trained model, care should be taken to avoid the "catastrophic forgetting" problem.

Example

ner = nlp.add_pipe("ner")
ner.set_output(512)

Name	Description
nO	The new output dimension. int

EntityRecognizer.to_disk

Serialize the pipe to disk.

Example

ner = nlp.add_pipe("ner")
ner.to_disk("/path/to/ner")

Name	Description
path	A path to a directory, which will be created if it doesn't exist. Paths may be either strings or Path-like objects. Union[str, Path]
keyword-only
exclude	String names of serialization fields to exclude. Iterable[str]

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	Description
path	A path to a directory. Paths may be either strings or Path-like objects. Union[str, Path]
keyword-only
exclude	String names of serialization fields to exclude. Iterable[str]
RETURNS	The modified EntityRecognizer object. EntityRecognizer

EntityRecognizer.to_bytes

Example

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

Serialize the pipe to a bytestring.

Name	Description
keyword-only
exclude	String names of serialization fields to exclude. Iterable[str]
RETURNS	The serialized form of the EntityRecognizer object. bytes

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	Description
bytes_data	The data to load from. bytes
keyword-only
exclude	String names of serialization fields to exclude. Iterable[str]
RETURNS	The EntityRecognizer object. EntityRecognizer

EntityRecognizer.labels

The labels currently added to the component.

Example

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

Name	Description
RETURNS	The labels added to the component. Tuple[str, ...]

EntityRecognizer.label_data

The labels currently added to the component and their internal meta information. This is the data generated by init labels and used by EntityRecognizer.initialize to initialize the model with a pre-defined label set.

Example

labels = ner.label_data
ner.initialize(lambda: [], nlp=nlp, labels=labels)

Name	Description
RETURNS	The label data added to the component. Dict[str, Dict[str, Dict[str, int]]]

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.