spaCy/website/docs/api/spancategorizer.md
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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.

Predicted spans will be saved in a SpanGroup on the doc. Individual span scores can be found in spangroup.attrs["scores"].

Assigned Attributes

Predictions will be saved to Doc.spans[spans_key] as a SpanGroup. The scores for the spans in the SpanGroup will be saved in SpanGroup.attrs["scores"].

spans_key defaults to "sc", but can be passed as a parameter.

Location Value
Doc.spans[spans_key] The annotated spans. SpanGroup
Doc.spans[spans_key].attrs["scores"] The score for each span in the SpanGroup. Floats1d

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": "spacy.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. CallableIterable[Doc], Optional[Ops, 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 "sc". 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]
scorer The scoring method. Defaults to Scorer.score_spans for Doc.spans[spans_key] with overlapping spans allowed. Optional[Callable]
%%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. CallableIterable[Doc], Optional[Ops, 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 "sc". 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. 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.

Example

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

Use the suggester to add a list of Span candidates to a list of Doc objects. This method is intended to be used for debugging purposes.

Example

spancat = nlp.add_pipe("spancat")
spancat.set_candidates(docs, "candidates")
Name Description
docs The documents to modify. Iterable[Doc]
candidates_key Key of the Doc.spans dict to save the candidate spans under. str

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]
spans_scores Scores representing the model's predictions. Tuple[Ragged, Floats2d]
RETURNS The loss and the gradient, i.e. (loss, gradient). Tuple[float, 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. CallableIterable[Doc], Optional[Ops, Ragged]

spacy.ngram_range_suggester.v1

Example Config

[components.spancat.suggester]
@misc = "spacy.ngram_range_suggester.v1"
min_size = 2
max_size = 4

Suggest all spans of at least length min_size and at most length max_size (both inclusive). Spans are returned as a ragged array of integers. The array has two columns, indicating the start and end position.

Name Description
min_size The minimal phrase lengths to suggest (inclusive). [int]
max_size The maximal phrase lengths to suggest (exclusive). [int]
CREATES The suggester function. CallableIterable[Doc], Optional[Ops, Ragged]