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	Description
model	The model to use. Defaults to HashEmbedCNN. Model[List[Doc], List[Floats2d]

%%GITHUB_SPACY/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	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

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

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	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

Tok2Vec.initialize

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. This method is typically called by Language.initialize.

Example

tok2vec = nlp.add_pipe("tok2vec")
tok2vec.initialize(lambda: [], nlp=nlp)

Name	Description
get_examples	Function that returns gold-standard annotations in the form of Example objects. Callable], Iterable[Example
keyword-only
nlp	The current nlp object. Defaults to None. Optional[Language]

Tok2Vec.predict

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

Example

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

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

Tok2Vec.set_annotations

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

Example

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

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

Tok2Vec.update

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

Example

tok2vec = nlp.add_pipe("tok2vec")
optimizer = nlp.initialize()
losses = tok2vec.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]

Tok2Vec.create_optimizer

Create an optimizer for the pipeline component.

Example

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

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

Tok2Vec.to_disk

Serialize the pipe to disk.

Example

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

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]

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	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 Tok2Vec object. Tok2Vec

Tok2Vec.to_bytes

Example

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

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	Description
bytes_data	The data to load from. bytes
keyword-only
exclude	String names of serialization fields to exclude. Iterable[str]
RETURNS	The Tok2Vec object. Tok2Vec

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.