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	Description
labels	A list of categories to learn. If empty, the model infers the categories from the data. Defaults to []. Iterable[str]
model	A model instance that predicts scores for each category. Defaults to TextCatEnsemble. Model[List[Doc], List[Floats2d]]

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	Description
vocab	The shared vocabulary. Vocab
model	The Thinc Model powering the pipeline component. Model[List[Doc], List[Floats2d]]
name	String name of the component instance. Used to add entries to the losses during training. str
keyword-only
labels	The labels to use. Iterable[str]

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

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	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

TextCategorizer.begin_training

Initialize the component for training and return an Optimizer. 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.

Example

textcat = nlp.add_pipe("textcat")
optimizer = textcat.begin_training(lambda: [], pipeline=nlp.pipeline)

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

TextCategorizer.predict

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

Example

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

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

TextCategorizer.set_annotations

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

Example

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

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

TextCategorizer.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

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

Name	Description
examples	A batch of Example objects to learn from. Iterable[Example]
keyword-only
drop	The dropout rate. float
set_annotations	Whether or not to update the Example objects with the predictions, delegating to set_annotations. bool
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]

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	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]

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	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]

TextCategorizer.score

Score a batch of examples.

Example

scores = textcat.score(examples)

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

TextCategorizer.create_optimizer

Create an optimizer for the pipeline component.

Example

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

Name	Description
RETURNS	The optimizer. Optimizer

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

TextCategorizer.add_label

Add a new label to the pipe.

Example

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

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

TextCategorizer.to_disk

Serialize the pipe to disk.

Example

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

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]

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	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 TextCategorizer object. TextCategorizer

TextCategorizer.to_bytes

Example

textcat = nlp.add_pipe("textcat")
textcat_bytes = textcat.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 TextCategorizer object. bytes

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

TextCategorizer.labels

The labels currently added to the component.

Example

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

Name	Description
RETURNS	The labels 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 = 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.