Pipeline

Tok2Vec

v3
String name:tok2vecBase class:TrainablePipeTrainable:

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.

SettingDescription
modelThe model to use. Defaults to HashEmbedCNN. Model[List[Doc], List[Floats2d]
explosion/spaCy/master/spacy/pipeline/tok2vec.py
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Tok2Vec.__init__ method

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.

NameDescription
vocabThe shared vocabulary. Vocab
modelThe Thinc Model powering the pipeline component. Model[List[Doc], List[Floats2d]
nameString name of the component instance. Used to add entries to the losses during training. str

Tok2Vec.__call__ method

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.

NameDescription
docThe document to process. Doc

Tok2Vec.pipe method

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.

NameDescription
streamA stream of documents. Iterable[Doc]
keyword-only
batch_sizeThe number of documents to buffer. Defaults to 128. int

Tok2Vec.initialize method

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.

NameDescription
get_examplesFunction that returns gold-standard annotations in the form of Example objects. Callable[[], Iterable[Example]]
keyword-only
nlpThe current nlp object. Defaults to None. Optional[Language]

Tok2Vec.predict method

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

NameDescription
docsThe documents to predict. Iterable[Doc]

Tok2Vec.set_annotations method

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

NameDescription
docsThe documents to modify. Iterable[Doc]
scoresThe scores to set, produced by Tok2Vec.predict.

Tok2Vec.update method

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

NameDescription
examplesA batch of Example objects to learn from. Iterable[Example]
keyword-only
dropThe dropout rate. float
sgdAn optimizer. Will be created via create_optimizer if not set. Optional[Optimizer]
lossesOptional record of the loss during training. Updated using the component name as the key. Optional[Dict[str, float]]

Tok2Vec.create_optimizer method

Create an optimizer for the pipeline component.

NameDescription

Tok2Vec.use_params methodcontextmanager

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

NameDescription
paramsThe parameter values to use in the model. dict

Tok2Vec.to_disk method

Serialize the pipe to disk.

NameDescription
pathA 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
excludeString names of serialization fields to exclude. Iterable[str]

Tok2Vec.from_disk method

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

NameDescription
pathA path to a directory. Paths may be either strings or Path-like objects. Union[str, Path]
keyword-only
excludeString names of serialization fields to exclude. Iterable[str]

Tok2Vec.to_bytes method

Serialize the pipe to a bytestring.

NameDescription
keyword-only
excludeString names of serialization fields to exclude. Iterable[str]

Tok2Vec.from_bytes method

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

NameDescription
bytes_dataThe data to load from. bytes
keyword-only
excludeString names of serialization fields to exclude. Iterable[str]

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.

NameDescription
vocabThe shared Vocab.
cfgThe config file. You usually don’t want to exclude this.
modelThe binary model data. You usually don’t want to exclude this.