spaCy/website/docs/api/textcategorizer.md

title	tag	source	new
TextCategorizer	class	spacy/pipeline/pipes.pyx	2

This class is a subclass of Pipe and follows the same API. The pipeline component is available in the processing pipeline via the ID "textcat".

TextCategorizer.Model

Initialize a model for the pipe. The model should implement the thinc.neural.Model API. Wrappers are under development for most major machine learning libraries.

Name	Type	Description
**kwargs	-	Parameters for initializing the model
RETURNS	object	The initialized model.

TextCategorizer.__init__

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.create_pipe.

Example

# Construction via create_pipe
textcat = nlp.create_pipe("textcat")
textcat = nlp.create_pipe("textcat", config={"exclusive_classes": True})

# Construction from class
from spacy.pipeline import TextCategorizer
textcat = TextCategorizer(nlp.vocab)
textcat.from_disk("/path/to/model")

Name	Type	Description
vocab	Vocab	The shared vocabulary.
model	thinc.neural.Model / True	The model powering the pipeline component. If no model is supplied, the model is created when you call begin_training, from_disk or from_bytes.
exclusive_classes	bool	Make categories mutually exclusive. Defaults to False.
architecture	unicode	Model architecture to use, see architectures for details. Defaults to "ensemble".
RETURNS	TextCategorizer	The newly constructed object.

Architectures

Text classification models can be used to solve a wide variety of problems. Differences in text length, number of labels, difficulty, and runtime performance constraints mean that no single algorithm performs well on all types of problems. To handle a wider variety of problems, the TextCategorizer object allows configuration of its model architecture, using the architecture keyword argument.

Name	Description
"ensemble"	Default: Stacked ensemble of a bag-of-words model and a neural network model. The neural network uses a CNN with mean pooling and attention. The "ngram_size" and "attr" arguments can be used to configure the feature extraction for the bag-of-words model.
"simple_cnn"	A neural network model where token vectors are calculated using a CNN. The vectors are mean pooled and used as features in a feed-forward network. This architecture is usually less accurate than the ensemble, but runs faster.
"bow"	An ngram "bag-of-words" model. This architecture should run much faster than the others, but may not be as accurate, especially if texts are short. The features extracted can be controlled using the keyword arguments ngram_size and attr. For instance, ngram_size=3 and attr="lower" would give lower-cased unigram, trigram and bigram features. 2, 3 or 4 are usually good choices of ngram size.

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

textcat = TextCategorizer(nlp.vocab)
doc = nlp(u"This is a sentence.")
# 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 = TextCategorizer(nlp.vocab)
for doc in textcat.pipe(docs, batch_size=50):
    pass

Name	Type	Description
stream	iterable	A stream of documents.
batch_size	int	The number of texts to buffer. Defaults to 128.
YIELDS	Doc	Processed documents in the order of the original text.

TextCategorizer.predict

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

Example

textcat = TextCategorizer(nlp.vocab)
scores, tensors = textcat.predict([doc1, doc2])

Name	Type	Description
docs	iterable	The documents to predict.
RETURNS	tuple	A (scores, tensors) tuple where scores is the model's prediction for each document and tensors is the token representations used to predict the scores. Each tensor is an array with one row for each token in the document.

TextCategorizer.set_annotations

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

Example

textcat = TextCategorizer(nlp.vocab)
scores, tensors = textcat.predict([doc1, doc2])
textcat.set_annotations([doc1, doc2], scores, tensors)

Name	Type	Description
docs	iterable	The documents to modify.
scores	-	The scores to set, produced by TextCategorizer.predict.
tensors	iterable	The token representations used to predict the scores.

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 = TextCategorizer(nlp.vocab)
losses = {}
optimizer = nlp.begin_training()
textcat.update([doc1, doc2], [gold1, gold2], losses=losses, sgd=optimizer)

Name	Type	Description
docs	iterable	A batch of documents to learn from.
golds	iterable	The gold-standard data. Must have the same length as docs.
drop	float	The dropout rate.
sgd	callable	The optimizer. Should take two arguments weights and gradient, and an optional ID.
losses	dict	Optional record of the loss during training. The value keyed by the model's name is updated.

TextCategorizer.get_loss

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

Example

textcat = TextCategorizer(nlp.vocab)
scores = textcat.predict([doc1, doc2])
loss, d_loss = textcat.get_loss([doc1, doc2], [gold1, gold2], scores)

Name	Type	Description
docs	iterable	The batch of documents.
golds	iterable	The gold-standard data. Must have the same length as docs.
scores	-	Scores representing the model's predictions.
RETURNS	tuple	The loss and the gradient, i.e. (loss, gradient).

TextCategorizer.begin_training

Initialize the pipe for training, using data examples if available. If no model has been initialized yet, the model is added.

Example

textcat = TextCategorizer(nlp.vocab)
nlp.pipeline.append(textcat)
optimizer = textcat.begin_training(pipeline=nlp.pipeline)

Name	Type	Description
gold_tuples	iterable	Optional gold-standard annotations from which to construct GoldParse objects.
pipeline	list	Optional list of pipeline components that this component is part of.
sgd	callable	An optional optimizer. Should take two arguments weights and gradient, and an optional ID. Will be created via TextCategorizer if not set.
RETURNS	callable	An optimizer.

TextCategorizer.create_optimizer

Create an optimizer for the pipeline component.

Example

textcat = TextCategorizer(nlp.vocab)
optimizer = textcat.create_optimizer()

Name	Type	Description
RETURNS	callable	The optimizer.

TextCategorizer.use_params

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

Example

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

Name	Type	Description
params	dict	The parameter values to use in the model. At the end of the context, the original parameters are restored.

TextCategorizer.add_label

Add a new label to the pipe.

Example

textcat = TextCategorizer(nlp.vocab)
textcat.add_label("MY_LABEL")

Name	Type	Description
label	unicode	The label to add.

TextCategorizer.to_disk

Serialize the pipe to disk.

Example

textcat = TextCategorizer(nlp.vocab)
textcat.to_disk("/path/to/textcat")

Name	Type	Description
path	unicode / Path	A path to a directory, which will be created if it doesn't exist. Paths may be either strings or Path-like objects.
exclude	list	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 = TextCategorizer(nlp.vocab)
textcat.from_disk("/path/to/textcat")

Name	Type	Description
path	unicode / Path	A path to a directory. Paths may be either strings or Path-like objects.
exclude	list	String names of serialization fields to exclude.
RETURNS	TextCategorizer	The modified TextCategorizer object.

TextCategorizer.to_bytes

Example

textcat = TextCategorizer(nlp.vocab)
textcat_bytes = textcat.to_bytes()

Serialize the pipe to a bytestring.

Name	Type	Description
exclude	list	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 = TextCategorizer(nlp.vocab)
textcat.from_bytes(textcat_bytes)

Name	Type	Description
bytes_data	bytes	The data to load from.
exclude	list	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.