spaCy/website/docs/api/spancategorizer.md

title	tag	source	new	teaser	api_base_class	api_string_name	api_trainable
SpanCategorizer	class,experimental	spacy/pipeline/spancat.py	3.1	Pipeline component for labeling potentially overlapping spans of text	/api/pipe	spancat	true

A span categorizer consists of two parts: a suggester function that proposes candidate spans, which may or may not overlap, and a labeler model that predicts zero or more labels for each candidate.

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.spancat import DEFAULT_SPANCAT_MODEL
config = {
    "threshold": 0.5,
    "spans_key": "labeled_spans",
    "max_positive": None,
    "model": DEFAULT_SPANCAT_MODEL,
    "suggester": {"@misc": "ngram_suggester.v1", "sizes": [1, 2, 3]},
}
nlp.add_pipe("spancat", config=config)

Setting	Description
suggester	A function that suggests spans. Spans are returned as a ragged array with two integer columns, for the start and end positions. Defaults to ngram_suggester. Callable[List[Doc], Ragged]
model	A model instance that is given a a list of documents and (start, end) indices representing candidate span offsets. The model predicts a probability for each category for each span. Defaults to SpanCategorizer. Model[Tuple[List[Doc], Ragged], Floats2d]
spans_key	Key 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 "spans". str
threshold	Minimum probability to consider a prediction positive. Spans with a positive prediction will be saved on the Doc. Defaults to 0.5. float
max_positive	Maximum number of labels to consider positive per span. Defaults to None, indicating no limit. Optional[int]

%%GITHUB_SPACY/spacy/pipeline/spancat.py

SpanCategorizer.__init__

Example

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

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

# Construction from class
from spacy.pipeline import SpanCategorizer
spancat = SpanCategorizer(nlp.vocab, model, suggester)

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	A model instance that is given a a list of documents and (start, end) indices representing candidate span offsets. The model predicts a probability for each category for each span. Model[Tuple[List[Doc], Ragged], Floats2d]
suggester	A function that suggests spans. Spans are returned as a ragged array with two integer columns, for the start and end positions. Callable[List[Doc], Ragged]
name	String name of the component instance. Used to add entries to the losses during training. str
keyword-only
spans_key	Key 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 "spans". str
threshold	Minimum probability to consider a prediction positive. Spans with a positive prediction will be saved on the Doc. Defaults to 0.5. float
max_positive	Maximum number of labels to consider positive per span. Defaults to None, indicating no limit. Optional[int]

SpanCategorizer.__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.")
spancat = nlp.add_pipe("spancat")
# This usually happens under the hood
processed = spancat(doc)

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

SpanCategorizer.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

spancat = nlp.add_pipe("spancat")
for doc in spancat.pipe(docs, batch_size=50):
    pass

Name	Description
stream	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

SpanCategorizer.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.

Example

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

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

[initialize.components.spancat.labels]
@readers = "spacy.read_labels.v1"
path = "corpus/labels/spancat.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[Iterable[str]]

SpanCategorizer.predict

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

Example

spancat = nlp.add_pipe("spancat")
scores = spancat.predict([doc1, doc2])

Name	Description
docs	The documents to predict. Iterable[Doc]
RETURNS	The model's prediction for each document.

SpanCategorizer.set_annotations

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

Example

spancat = nlp.add_pipe("spancat")
scores = spancat.predict(docs)
spancat.set_annotations(docs, scores)

Name	Description
docs	The documents to modify. Iterable[Doc]
scores	The scores to set, produced by SpanCategorizer.predict.

SpanCategorizer.update

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.

Example

spancat = nlp.add_pipe("spancat")
optimizer = nlp.initialize()
losses = spancat.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]

SpanCategorizer.get_loss

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

Example

spancat = nlp.add_pipe("spancat")
scores = spancat.predict([eg.predicted for eg in examples])
loss, d_loss = spancat.get_loss(examples, scores)

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

SpanCategorizer.score

Score a batch of examples.

Example

scores = spancat.score(examples)

Name	Description
examples	The examples to score. Iterable[Example]
keyword-only
RETURNS	The scores, produced by Scorer.score_spans. Dict[str, Union[float, Dict[str, float]]]

SpanCategorizer.create_optimizer

Create an optimizer for the pipeline component.

Example

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

Name	Description
RETURNS	The optimizer. Optimizer

SpanCategorizer.use_params

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

Example

spancat = nlp.add_pipe("spancat")
with spancat.use_params(optimizer.averages):
    spancat.to_disk("/best_model")

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

SpanCategorizer.add_label

Add a new label to the pipe. Raises an error if the output dimension is already set, or if the model has already been fully initialized. 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

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

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

SpanCategorizer.to_disk

Serialize the pipe to disk.

Example

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

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]

SpanCategorizer.from_disk

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

Example

spancat = nlp.add_pipe("spancat")
spancat.from_disk("/path/to/spancat")

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 SpanCategorizer object. SpanCategorizer

SpanCategorizer.to_bytes

Example

spancat = nlp.add_pipe("spancat")
spancat_bytes = spancat.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 SpanCategorizer object. bytes

SpanCategorizer.from_bytes

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

Example

spancat_bytes = spancat.to_bytes()
spancat = nlp.add_pipe("spancat")
spancat.from_bytes(spancat_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 SpanCategorizer object. SpanCategorizer

SpanCategorizer.labels

The labels currently added to the component.

Example

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

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

SpanCategorizer.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 SpanCategorizer.initialize to initialize the model with a pre-defined label set.

Example

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

Name	Description
RETURNS	The label data added to the component. Tuple[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.

Example

data = spancat.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.

Suggesters

spacy.ngram_suggester.v1

Example Config

[components.spancat.suggester]
@misc = "spacy.ngram_suggester.v1"
sizes = [1, 2, 3]

Suggest all spans of the given lengths. Spans are returned as a ragged array of integers. The array has two columns, indicating the start and end position.

Name	Description
sizes	The phrase lengths to suggest. For example, [1, 2] will suggest phrases consisting of 1 or 2 tokens. List[int]
CREATES	The suggester function. CallableList[Doc, Ragged]