spaCy/website/docs/api/textcategorizer.md

title	tag	source	new	teaser	api_base_class	api_string_name	api_trainable
TextCategorizer	class	spacy/pipeline/textcat.py	2	Pipeline component for text classification	/api/pipe	textcat	true

The text categorizer predicts categories over a whole document. It can learn one or more labels, and the labels can be mutually exclusive (i.e. one true label per document) or non-mutually exclusive (i.e. zero or more labels may be true per document). The multi-label setting is controlled by the model instance that's provided.

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.textcat import DEFAULT_TEXTCAT_MODEL
config = {
   "labels": [],
   "model": DEFAULT_TEXTCAT_MODEL,
}
nlp.add_pipe("textcat", config=config)

Setting	Type	Description	Default
labels	List[str]	A list of categories to learn. If empty, the model infers the categories from the data.	[]
model	Model	A model instance that predicts scores for each category.	TextCatEnsemble

https://github.com/explosion/spaCy/blob/develop/spacy/pipeline/textcat.py

TextCategorizer.__init__

Example

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

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

# Construction from class
from spacy.pipeline import TextCategorizer
textcat = TextCategorizer(nlp.vocab, model)

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	Type	Description
vocab	Vocab	The shared vocabulary.
model	Model	The Thinc Model powering the pipeline component.
name	str	String name of the component instance. Used to add entries to the losses during training.
keyword-only
labels	Iterable[str]	The labels to use.

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

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

TextCategorizer.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

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

Name	Type	Description
stream	Iterable[Doc]	A stream of documents.
keyword-only
batch_size	int	The number of documents to buffer. Defaults to 128.
YIELDS	Doc	The processed documents in order.

TextCategorizer.begin_training

Initialize the pipe for training, using data examples if available. Returns an Optimizer object.

Example

textcat = nlp.add_pipe("textcat")
optimizer = textcat.begin_training(pipeline=nlp.pipeline)

Name	Type	Description
get_examples	Callable[[], Iterable[Example]]	Optional function that returns gold-standard annotations in the form of Example objects.
keyword-only
pipeline	List[Tuple[str, Callable]]	Optional list of pipeline components that this component is part of.
sgd	Optimizer	An optional optimizer. Will be created via create_optimizer if not set.
RETURNS	Optimizer	The optimizer.

TextCategorizer.predict

Apply the pipeline's model to a batch of docs, without modifying them.

Example

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

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

TextCategorizer.set_annotations

Modify a batch of documents, using pre-computed scores.

Example

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

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

TextCategorizer.update

Learn from a batch of documents and gold-standard information, updating the pipe's model. Delegates to predict and get_loss.

Example

textcat = nlp.add_pipe("textcat")
optimizer = nlp.begin_training()
losses = textcat.update(examples, sgd=optimizer)

Name	Type	Description
examples	Iterable[Example]	A batch of Example objects to learn from.
keyword-only
drop	float	The dropout rate.
set_annotations	bool	Whether or not to update the Example objects with the predictions, delegating to set_annotations.
sgd	Optimizer	The optimizer.
losses	Dict[str, float]	Optional record of the loss during training. Updated using the component name as the key.
RETURNS	Dict[str, float]	The updated losses dictionary.

TextCategorizer.rehearse

Perform a "rehearsal" update from a batch of data. Rehearsal updates teach the current model to make predictions similar to an initial model, to try to address the "catastrophic forgetting" problem. This feature is experimental.

Example

textcat = nlp.add_pipe("textcat")
optimizer = nlp.resume_training()
losses = textcat.rehearse(examples, sgd=optimizer)

Name	Type	Description
examples	Iterable[Example]	A batch of Example objects to learn from.
keyword-only
drop	float	The dropout rate.
sgd	Optimizer	The optimizer.
losses	Dict[str, float]	Optional record of the loss during training. Updated using the component name as the key.
RETURNS	Dict[str, float]	The updated losses dictionary.

TextCategorizer.get_loss

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

Example

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

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

TextCategorizer.score

Score a batch of examples.

Example

scores = textcat.score(examples)

Name	Type	Description
examples	Iterable[Example]	The examples to score.
keyword-only
positive_label	str	Optional positive label.
RETURNS	Dict[str, Any]	The scores, produced by Scorer.score_cats.

TextCategorizer.create_optimizer

Create an optimizer for the pipeline component.

Example

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

Name	Type	Description
RETURNS	Optimizer	The optimizer.

TextCategorizer.add_label

Add a new label to the pipe.

Example

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

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

TextCategorizer.use_params

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

Example

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

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

TextCategorizer.to_disk

Serialize the pipe to disk.

Example

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

Name	Type	Description
path	str / 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	Iterable[str]	String names of serialization fields to exclude.

TextCategorizer.from_disk

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

Example

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

Name	Type	Description
path	str / Path	A path to a directory. Paths may be either strings or Path-like objects.
keyword-only
exclude	Iterable[str]	String names of serialization fields to exclude.
RETURNS	TextCategorizer	The modified TextCategorizer object.

TextCategorizer.to_bytes

Example

textcat = nlp.add_pipe("textcat")
textcat_bytes = textcat.to_bytes()

Serialize the pipe to a bytestring.

Name	Type	Description
keyword-only
exclude	Iterable[str]	String names of serialization fields to exclude.
RETURNS	bytes	The serialized form of the TextCategorizer object.

TextCategorizer.from_bytes

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

Example

textcat_bytes = textcat.to_bytes()
textcat = nlp.add_pipe("textcat")
textcat.from_bytes(textcat_bytes)

Name	Type	Description
bytes_data	bytes	The data to load from.
keyword-only
exclude	Iterable[str]	String names of serialization fields to exclude.
RETURNS	TextCategorizer	The TextCategorizer object.

TextCategorizer.labels

The labels currently added to the component.

Example

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

Name	Type	Description
RETURNS	tuple	The labels 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 = textcat.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.