EntityRecognizer

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.

Assigned Attributes {id="assigned-attributes"}

Predictions will be saved to Doc.ents as a tuple. Each label will also be reflected to each underlying token, where it is saved in the Token.ent_type and Token.ent_iob fields. Note that by definition each token can only have one label.

When setting Doc.ents to create training data, all the spans must be valid and non-overlapping, or an error will be thrown.

Location	Value
`Doc.ents`	The annotated spans. ~~Tuple[Span]~~
`Token.ent_iob`	An enum encoding of the IOB part of the named entity tag. ~~int~~
`Token.ent_iob_`	The IOB part of the named entity tag. ~~str~~
`Token.ent_type`	The label part of the named entity tag (hash). ~~int~~
`Token.ent_type_`	The label part of the named entity tag. ~~str~~

Config and implementation {id="config"}

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

python

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[TransitionSystem]~~
`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 will learn not to predict (exactly) those spans. Defaults to `None`. ~~Optional[str]~~
`scorer`	The scoring method. Defaults to `spacy.scorer.get_ner_prf`. ~~Optional[Callable]~~

python

%%GITHUB_SPACY/spacy/pipeline/ner.pyx

EntityRecognizer.init {id="init",tag="method"}

Example

python

# 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[TransitionSystem]~~
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 in `Doc.spans`, under this key. Defaults to `None`. ~~Optional[str]~~

EntityRecognizer.call {id="call",tag="method"}

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

python

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 {id="pipe",tag="method"}

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

python

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 {id="initialize",tag="method",version="3"}

Initialize the component for training. get_examples should be a function that returns an iterable of Example objects. At least one example should be supplied. 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.

</Infobox>

Example

python

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

ini

### 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. Must contain at least one `Example`. ~~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 {id="predict",tag="method"}

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

Example

python

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 {id="set_annotations",tag="method"}

Modify a batch of Doc objects, using pre-computed scores.

Example

python

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 {id="update",tag="method"}

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

Example

python

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 {id="get_loss",tag="method"}

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

Example

python

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.create_optimizer {id="create_optimizer",tag="method"}

Create an optimizer for the pipeline component.

Example

python

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

Name	Description
RETURNS	The optimizer. ~~Optimizer~~

EntityRecognizer.use_params {id="use_params",tag="method, contextmanager"}

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

Example

python

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 {id="add_label",tag="method"}

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

python

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 {id="set_output",tag="method"}

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

python

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

Name	Description
`nO`	The new output dimension. ~~int~~

EntityRecognizer.to_disk {id="to_disk",tag="method"}

Serialize the pipe to disk.

Example

python

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 {id="from_disk",tag="method"}

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

Example

python

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 {id="to_bytes",tag="method"}

Example

python

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 {id="from_bytes",tag="method"}

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

Example

python

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 {id="labels",tag="property"}

The labels currently added to the component.

Example

python

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 {id="label_data",tag="property",version="3"}

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

python

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 {id="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

python

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.

Assigned Attributes {id="assigned-attributes"}

Config and implementation {id="config"}

Example

EntityRecognizer.__init__ {id="init",tag="method"}

Example

EntityRecognizer.__call__ {id="call",tag="method"}

Example

EntityRecognizer.pipe {id="pipe",tag="method"}

Example

EntityRecognizer.initialize {id="initialize",tag="method",version="3"}

Example

EntityRecognizer.predict {id="predict",tag="method"}

Example

EntityRecognizer.set_annotations {id="set_annotations",tag="method"}

Example

EntityRecognizer.update {id="update",tag="method"}

Example

EntityRecognizer.get_loss {id="get_loss",tag="method"}

Example

EntityRecognizer.create_optimizer {id="create_optimizer",tag="method"}

Example

EntityRecognizer.use_params {id="use_params",tag="method, contextmanager"}

Example

EntityRecognizer.add_label {id="add_label",tag="method"}

Example

EntityRecognizer.set_output {id="set_output",tag="method"}

Example

EntityRecognizer.to_disk {id="to_disk",tag="method"}

Example

EntityRecognizer.from_disk {id="from_disk",tag="method"}

Example

EntityRecognizer.to_bytes {id="to_bytes",tag="method"}

Example

EntityRecognizer.from_bytes {id="from_bytes",tag="method"}

Example

EntityRecognizer.labels {id="labels",tag="property"}

Example

EntityRecognizer.label_data {id="label_data",tag="property",version="3"}

Example

Serialization fields {id="serialization-fields"}

Example

EntityRecognizer.init {id="init",tag="method"}

EntityRecognizer.call {id="call",tag="method"}