spaCy/website/docs/api/tok2vec.md

title	source	new	teaser	api_base_class	api_string_name	api_trainable
Tok2Vec	spacy/pipeline/tok2vec.py	3	null	/api/pipe	tok2vec	true

Apply a "token-to-vector" model and set its outputs in the Doc.tensor attribute. This is mostly useful to share a single subnetwork between multiple components, e.g. to have one embedding and CNN network shared between a DependencyParser, Tagger and EntityRecognizer.

In order to use the Tok2Vec predictions, subsequent components should use the Tok2VecListener layer as the tok2vec subnetwork of their model. This layer will read data from the doc.tensor attribute during prediction. During training, the Tok2Vec component will save its prediction and backprop callback for each batch, so that the subsequent components can backpropagate to the shared weights. This implementation is used because it allows us to avoid relying on object identity within the models to achieve the parameter sharing.

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.tok2vec import DEFAULT_TOK2VEC_MODEL
config = {"model": DEFAULT_TOK2VEC_MODEL}
nlp.add_pipe("tok2vec", config=config)

Setting	Type	Description	Default
model	Model	Input: List[Doc]. Output: List[Floats2d]. The model to use.	HashEmbedCNN

https://github.com/explosion/spaCy/blob/develop/spacy/pipeline/tok2vec.py

Tok2Vec.__init__

Example

# Construction via add_pipe with default model
tok2vec = nlp.add_pipe("tok2vec")

# Construction via add_pipe with custom model
config = {"model": {"@architectures": "my_tok2vec"}}
parser = nlp.add_pipe("tok2vec", config=config)

# Construction from class
from spacy.pipeline import Tok2Vec
tok2vec = Tok2Vec(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.

Tok2Vec.__call__

Apply the pipe to one document and add context-sensitive embeddings to the Doc.tensor attribute, allowing them to be used as features by downstream components. 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.")
tok2vec = nlp.add_pipe("tok2vec")
# This usually happens under the hood
processed = tok2vec(doc)

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

Tok2Vec.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

tok2vec = nlp.add_pipe("tok2vec")
for doc in tok2vec.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.

Tok2Vec.begin_training

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

Example

tok2vec = nlp.add_pipe("tok2vec")
optimizer = tok2vec.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.

Tok2Vec.predict

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

Example

tok2vec = nlp.add_pipe("tok2vec")
scores = tok2vec.predict([doc1, doc2])

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

Tok2Vec.set_annotations

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

Example

tok2vec = nlp.add_pipe("tok2vec")
scores = tok2vec.predict(docs)
tok2vec.set_annotations(docs, scores)

Name	Type	Description
docs	Iterable[Doc]	The documents to modify.
scores	-	The scores to set, produced by Tok2Vec.predict.

Tok2Vec.update

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

Example

tok2vec = nlp.add_pipe("tok2vec")
optimizer = nlp.begin_training()
losses = tok2vec.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.

Tok2Vec.create_optimizer

Create an optimizer for the pipeline component.

Example

tok2vec = nlp.add_pipe("tok2vec")
optimizer = tok2vec.create_optimizer()

Name	Type	Description
RETURNS	Optimizer	The optimizer.

Tok2Vec.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

tok2vec = nlp.add_pipe("tok2vec")
with tok2vec.use_params(optimizer.averages):
    tok2vec.to_disk("/best_model")

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

Tok2Vec.to_disk

Serialize the pipe to disk.

Example

tok2vec = nlp.add_pipe("tok2vec")
tok2vec.to_disk("/path/to/tok2vec")

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.

Tok2Vec.from_disk

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

Example

tok2vec = nlp.add_pipe("tok2vec")
tok2vec.from_disk("/path/to/tok2vec")

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	Tok2Vec	The modified Tok2Vec object.

Tok2Vec.to_bytes

Example

tok2vec = nlp.add_pipe("tok2vec")
tok2vec_bytes = tok2vec.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 Tok2Vec object.

Tok2Vec.from_bytes

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

Example

tok2vec_bytes = tok2vec.to_bytes()
tok2vec = nlp.add_pipe("tok2vec")
tok2vec.from_bytes(tok2vec_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	Tok2Vec	The Tok2Vec object.

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 = tok2vec.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.