spaCy/website/docs/api/dependencyparser.md

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DependencyParser class spacy/pipeline.pyx

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

DependencyParser.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.

DependencyParser.__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
parser = nlp.create_pipe("parser")

# Construction from class
from spacy.pipeline import DependencyParser
parser = DependencyParser(nlp.vocab)
parser.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.
**cfg - Configuration parameters.
RETURNS DependencyParser The newly constructed object.

DependencyParser.__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

parser = DependencyParser(nlp.vocab)
doc = nlp(u"This is a sentence.")
# This usually happens under the hood
processed = parser(doc)
Name Type Description
doc Doc The document to process.
RETURNS Doc The processed document.

DependencyParser.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

parser = DependencyParser(nlp.vocab)
for doc in parser.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.

DependencyParser.predict

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

Example

parser = DependencyParser(nlp.vocab)
scores = parser.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.

DependencyParser.set_annotations

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

Example

parser = DependencyParser(nlp.vocab)
scores = parser.predict([doc1, doc2])
parser.set_annotations([doc1, doc2], scores)
Name Type Description
docs iterable The documents to modify.
scores - The scores to set, produced by DependencyParser.predict.

DependencyParser.update

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

Example

parser = DependencyParser(nlp.vocab)
losses = {}
optimizer = nlp.begin_training()
parser.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.

DependencyParser.get_loss

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

Example

parser = DependencyParser(nlp.vocab)
scores = parser.predict([doc1, doc2])
loss, d_loss = parser.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).

DependencyParser.begin_training

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

Example

parser = DependencyParser(nlp.vocab)
nlp.pipeline.append(parser)
optimizer = parser.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 DependencyParser if not set.
RETURNS callable An optimizer.

DependencyParser.create_optimizer

Create an optimizer for the pipeline component.

Example

parser = DependencyParser(nlp.vocab)
optimizer = parser.create_optimizer()
Name Type Description
RETURNS callable The optimizer.

DependencyParser.use_params

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

Example

parser = DependencyParser(nlp.vocab)
with parser.use_params():
    parser.to_disk('/best_model')
Name Type Description
params - The parameter values to use in the model. At the end of the context, the original parameters are restored.

DependencyParser.add_label

Add a new label to the pipe.

Example

parser = DependencyParser(nlp.vocab)
parser.add_label('MY_LABEL')
Name Type Description
label unicode The label to add.

DependencyParser.to_disk

Serialize the pipe to disk.

Example

parser = DependencyParser(nlp.vocab)
parser.to_disk('/path/to/parser')
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.

DependencyParser.from_disk

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

Example

parser = DependencyParser(nlp.vocab)
parser.from_disk('/path/to/parser')
Name Type Description
path unicode / Path A path to a directory. Paths may be either strings or Path-like objects.
RETURNS DependencyParser The modified DependencyParser object.

DependencyParser.to_bytes

example

parser = DependencyParser(nlp.vocab)
parser_bytes = parser.to_bytes()

Serialize the pipe to a bytestring.

Name Type Description
**exclude - Named attributes to prevent from being serialized.
RETURNS bytes The serialized form of the DependencyParser object.

DependencyParser.from_bytes

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

Example

parser_bytes = parser.to_bytes()
parser = DependencyParser(nlp.vocab)
parser.from_bytes(parser_bytes)
Name Type Description
bytes_data bytes The data to load from.
**exclude - Named attributes to prevent from being loaded.
RETURNS DependencyParser The DependencyParser object.

DependencyParser.labels

The labels currently added to the component.

Example

parser.add_label("MY_LABEL")
assert "MY_LABEL" in parser.labels
Name Type Description
RETURNS tuple The labels added to the component.