24 KiB
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 . |
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. |
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" . |
threshold |
Minimum probability to consider a prediction positive. Spans with a positive prediction will be saved on the Doc. Defaults to 0.5 . |
max_positive |
Maximum number of labels to consider positive per span. Defaults to None , indicating no limit. |
%%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. |
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. |
suggester |
A function that suggests spans. Spans are returned as a ragged array with two integer columns, for the start and end positions. |
name |
String name of the component instance. Used to add entries to the losses during training. |
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" . |
threshold |
Minimum probability to consider a prediction positive. Spans with a positive prediction will be saved on the Doc. Defaults to 0.5 . |
max_positive |
Maximum number of labels to consider positive per span. Defaults to None , indicating no limit. |
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. |
RETURNS | The processed document. |
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. |
keyword-only | |
batch_size |
The number of documents to buffer. Defaults to 128 . |
YIELDS | The processed documents in order. |
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. |
keyword-only | |
nlp |
The current nlp object. Defaults to None . |
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. |
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. |
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. |
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. |
keyword-only | |
drop |
The dropout rate. |
sgd |
An optimizer. Will be created via create_optimizer if not set. |
losses |
Optional record of the loss during training. Updated using the component name as the key. |
RETURNS | The updated losses dictionary. |
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. |
scores |
Scores representing the model's predictions. |
RETURNS | The loss and the gradient, i.e. (loss, gradient) . |
SpanCategorizer.score
Score a batch of examples.
Example
scores = spancat.score(examples)
Name | Description |
---|---|
examples |
The examples to score. |
keyword-only | |
RETURNS | The scores, produced by Scorer.score_spans . |
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. |
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. |
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. |
RETURNS | 0 if the label is already present, otherwise 1 . |
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. |
keyword-only | |
exclude |
String names of serialization fields to exclude. |
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. |
keyword-only | |
exclude |
String names of serialization fields to exclude. |
RETURNS | The modified SpanCategorizer object. |
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. |
RETURNS | The serialized form of the SpanCategorizer object. |
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. |
keyword-only | |
exclude |
String names of serialization fields to exclude. |
RETURNS | The SpanCategorizer object. |
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. |
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. |
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. |
CREATES | The suggester function. |