spaCy/website/docs/api/pipe.md

title	tag	teaser
Pipe	class	Base class for trainable pipeline components

This class is a base class and not instantiated directly. Trainable pipeline components like the EntityRecognizer or TextCategorizer inherit from it and it defines the interface that components should follow to function as trainable components in a spaCy pipeline. See the docs on writing trainable components for how to use the Pipe base class to implement custom components.

Why is Pipe implemented in Cython?

The Pipe class is implemented in a .pyx module, the extension used by Cython. This is needed so that other Cython classes, like the EntityRecognizer can inherit from it. But it doesn't mean you have to implement trainable components in Cython – pure Python components like the TextCategorizer can also inherit from Pipe.

https://github.com/explosion/spaCy/blob/develop/spacy/pipeline/pipe.pyx

Pipe.__init__

Example

from spacy.pipeline import Pipe
from spacy.language import Language

class CustomPipe(Pipe):
    ...

@Language.factory("your_custom_pipe", default_config={"model": MODEL})
def make_custom_pipe(nlp, name, model):
    return CustomPipe(nlp.vocab, model, name)

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.
**cfg		Additional config parameters and settings. Will be available as the dictionary Pipe.cfg and is serialized with the component.

Pipe.__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.")
pipe = nlp.add_pipe("your_custom_pipe")
# This usually happens under the hood
processed = pipe(doc)

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

Pipe.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

pipe = nlp.add_pipe("your_custom_pipe")
for doc in pipe.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.

Pipe.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

pipe = nlp.add_pipe("your_custom_pipe")
optimizer = pipe.begin_training(lambda: [], 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.

Pipe.predict

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

This method needs to be overwritten with your own custom predict method.

Example

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

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

Pipe.set_annotations

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

This method needs to be overwritten with your own custom set_annotations method.

Example

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

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

Pipe.update

Learn from a batch of Example objects containing the predictions and gold-standard annotations, and update the component's model.

Example

pipe = nlp.add_pipe("your_custom_pipe")
optimizer = nlp.begin_training()
losses = pipe.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.

Pipe.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

pipe = nlp.add_pipe("your_custom_pipe")
optimizer = nlp.resume_training()
losses = pipe.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.

Pipe.get_loss

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

Example

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

Pipe.score

Score a batch of examples.

Example

scores = pipe.score(examples)

Name	Type	Description
examples	Iterable[Example]	The examples to score.
RETURNS	Dict[str, Any]	The scores, e.g. produced by the Scorer.

Pipe.create_optimizer

Create an optimizer for the pipeline component. Defaults to Adam with default settings.

Example

pipe = nlp.add_pipe("your_custom_pipe")
optimizer = pipe.create_optimizer()

Name	Type	Description
RETURNS	Optimizer	The optimizer.

Pipe.add_label

Add a new label to the pipe. It's possible to extend pretrained models with new labels, but care should be taken to avoid the "catastrophic forgetting" problem.

Example

pipe = nlp.add_pipe("your_custom_pipe")
pipe.add_label("MY_LABEL")

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

Pipe.use_params

Modify the pipe's model, to use the given parameter values. At the end of the context, the original parameters are restored.

Example

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

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

Pipe.to_disk

Serialize the pipe to disk.

Example

pipe = nlp.add_pipe("your_custom_pipe")
pipe.to_disk("/path/to/pipe")

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.

Pipe.from_disk

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

Example

pipe = nlp.add_pipe("your_custom_pipe")
pipe.from_disk("/path/to/pipe")

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	Pipe	The modified pipe.

Pipe.to_bytes

Example

pipe = nlp.add_pipe("your_custom_pipe")
pipe_bytes = pipe.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 pipe.

Pipe.from_bytes

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

Example

pipe_bytes = pipe.to_bytes()
pipe = nlp.add_pipe("your_custom_pipe")
pipe.from_bytes(pipe_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	Pipe	The pipe.

Attributes

Name	Type	Description
vocab	Vocab	The shared vocabulary that's passed in on initialization.
model	Model	The model powering the component.
name	str	The name of the component instance in the pipeline. Can be used in the losses.
cfg	dict	Keyword arguments passed to Pipe.__init__. Will be serialized with 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 = pipe.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.