Pipeline

EntityRecognizer

class
String name:nerBase class:TrainablePipeTrainable:
Pipeline component for named entity recognition

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

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.

LocationValue
Doc.entsThe annotated spans. Tuple[Span]
Token.ent_iobAn 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_typeThe label part of the named entity tag (hash). int
Token.ent_type_The label part of the named entity tag. str

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.

SettingDescription
movesA list of transition names. Inferred from the data if not provided. Defaults to None. Optional[TransitionSystem]
update_with_oracle_cut_sizeDuring 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
modelThe Model powering the pipeline component. Defaults to TransitionBasedParser. Model[List[Doc], List[Floats2d]]
incorrect_spans_keyThis 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]
scorerThe scoring method. Defaults to spacy.scorer.get_ner_prf. Optional[Callable]
explosion/spaCy/master/spacy/pipeline/ner.pyx
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EntityRecognizer.__init__ method

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.

NameDescription
vocabThe shared vocabulary. Vocab
modelThe Model powering the pipeline component. Model[List[Doc], List[Floats2d]]
nameString name of the component instance. Used to add entries to the losses during training. str
movesA list of transition names. Inferred from the data if set to None, which is the default. Optional[TransitionSystem]
keyword-only
update_with_oracle_cut_sizeDuring 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_keyIdentifies 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__ 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.

NameDescription
docThe document to process. Doc

EntityRecognizer.pipe 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.

NameDescription
docsA stream of documents. Iterable[Doc]
keyword-only
batch_sizeThe number of documents to buffer. Defaults to 128. int

EntityRecognizer.initialize methodv3.0

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.

NameDescription
get_examplesFunction that returns gold-standard annotations in the form of Example objects. Callable[[], Iterable[Example]]
keyword-only
nlpThe current nlp object. Defaults to None. Optional[Language]
labelsThe 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 method

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

NameDescription
docsThe documents to predict. Iterable[Doc]

EntityRecognizer.set_annotations method

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

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

EntityRecognizer.update method

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

NameDescription
examplesA batch of Example objects to learn from. Iterable[Example]
keyword-only
dropThe dropout rate. float
sgdAn optimizer. Will be created via create_optimizer if not set. Optional[Optimizer]
lossesOptional record of the loss during training. Updated using the component name as the key. Optional[Dict[str, float]]

EntityRecognizer.get_loss method

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

NameDescription
examplesThe batch of examples. Iterable[Example]
scoresScores representing the model’s predictions. StateClass

EntityRecognizer.create_optimizer method

Create an optimizer for the pipeline component.

NameDescription

EntityRecognizer.use_params methodcontextmanager

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

NameDescription
paramsThe parameter values to use in the model. dict

EntityRecognizer.add_label 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.

NameDescription
labelThe label to add. str

EntityRecognizer.set_output 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.

NameDescription
nOThe new output dimension. int

EntityRecognizer.to_disk method

Serialize the pipe to disk.

NameDescription
pathA 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
excludeString names of serialization fields to exclude. Iterable[str]

EntityRecognizer.from_disk method

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

NameDescription
pathA path to a directory. Paths may be either strings or Path-like objects. Union[str, Path]
keyword-only
excludeString names of serialization fields to exclude. Iterable[str]

EntityRecognizer.to_bytes method

Serialize the pipe to a bytestring.

NameDescription
keyword-only
excludeString names of serialization fields to exclude. Iterable[str]

EntityRecognizer.from_bytes method

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

NameDescription
bytes_dataThe data to load from. bytes
keyword-only
excludeString names of serialization fields to exclude. Iterable[str]

EntityRecognizer.labels property

The labels currently added to the component.

NameDescription

EntityRecognizer.label_data propertyv3.0

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.

NameDescription

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.

NameDescription
vocabThe shared Vocab.
cfgThe config file. You usually don’t want to exclude this.
modelThe binary model data. You usually don’t want to exclude this.