Pipeline

SpanFinder

class,experimentalv3.6
String name:span_finderTrainable:
Pipeline component for identifying potentially overlapping spans of text

The span finder identifies potentially overlapping, unlabeled spans. It identifies tokens that start or end spans and annotates unlabeled spans between starts and ends, with optional filters for min and max span length. It is intended for use in combination with a component like SpanCategorizer that may further filter or label the spans. Predicted spans will be saved in a SpanGroup on the doc under doc.spans[spans_key], where spans_key is a component config setting.

Assigned Attributes

Predictions will be saved to Doc.spans[spans_key] as a SpanGroup.

spans_key defaults to "sc", but can be passed as a parameter. The span_finder component will overwrite any existing spans under the spans key doc.spans[spans_key].

LocationValue
Doc.spans[spans_key]The unlabeled spans. SpanGroup

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
modelA model instance that is given a list of documents and predicts a probability for each token. Model[List[Doc],Floats2d]
spans_keyKey of the Doc.spans dict to save the spans under. During initialization and training, the component will look for spans on the reference document under the same key. Defaults to "sc". str
thresholdMinimum probability to consider a prediction positive. Defaults to 0.5. float
max_lengthMaximum length of the produced spans, defaults to 25. Optional[int]
min_lengthMinimum length of the produced spans, defaults to None meaning shortest span length is 1. Optional[int]
scorerThe scoring method. Defaults to Scorer.score_spans for Doc.spans[spans_key] with overlapping spans allowed. Optional[Callable]
explosion/spaCy/master/spacy/pipeline/span_finder.py

SpanFinder.__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
modelA model instance that is given a list of documents and predicts a probability for each token. Model[List[Doc],Floats2d]
nameString name of the component instance. Used to add entries to the losses during training. str
keyword-only
spans_keyKey of the Doc.spans dict to save the spans under. During initialization and training, the component will look for spans on the reference document under the same key. Defaults to "sc". str
thresholdMinimum probability to consider a prediction positive. Defaults to 0.5. float
max_lengthMaximum length of the produced spans, defaults to None meaning unlimited length. Optional[int]
min_lengthMinimum length of the produced spans, defaults to None meaning shortest span length is 1. Optional[int]
scorerThe scoring method. Defaults to Scorer.score_spans for Doc.spans[spans_key] with overlapping spans allowed. Optional[Callable]

SpanFinder.__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

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

SpanFinder.initialize method

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 and inferring missing shapes 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. Must contain at least one Example. Callable[[], Iterable[Example]]
keyword-only
nlpThe current nlp object. Defaults to None. Optional[Language]

SpanFinder.predict method

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

NameDescription
docsThe documents to predict. Iterable[Doc]

SpanFinder.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 SpanFinder.predict.

SpanFinder.update method

Learn from a batch of Example objects containing the predictions and gold-standard annotations, and update the component’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]]

SpanFinder.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]
spans_scoresScores representing the model’s predictions. Tuple[Ragged,Floats2d]

SpanFinder.create_optimizer method

Create an optimizer for the pipeline component.

NameDescription

SpanFinder.use_params methodcontextmanager

Modify the pipe’s model to use the given parameter values.

NameDescription
paramsThe parameter values to use in the model. dict

SpanFinder.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]

SpanFinder.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]

SpanFinder.to_bytes method

Serialize the pipe to a bytestring.

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

SpanFinder.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]

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.