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328 lines
14 KiB
Markdown
328 lines
14 KiB
Markdown
---
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title: Tok2Vec
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source: spacy/pipeline/tok2vec.py
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new: 3
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teaser: null
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api_base_class: /api/pipe
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api_string_name: tok2vec
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api_trainable: true
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---
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Apply a "token-to-vector" model and set its outputs in the `Doc.tensor`
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attribute. This is mostly useful to **share a single subnetwork** between
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multiple components, e.g. to have one embedding and CNN network shared between a
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[`DependencyParser`](/api/dependencyparser), [`Tagger`](/api/tagger) and
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[`EntityRecognizer`](/api/entityrecognizer).
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In order to use the `Tok2Vec` predictions, subsequent components should use the
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[Tok2VecListener](/api/architectures#Tok2VecListener) layer as the `tok2vec`
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subnetwork of their model. This layer will read data from the `doc.tensor`
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attribute during prediction. During training, the `Tok2Vec` component will save
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its prediction and backprop callback for each batch, so that the subsequent
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components can backpropagate to the shared weights. This implementation is used
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because it allows us to avoid relying on object identity within the models to
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achieve the parameter sharing.
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## Config and implementation {#config}
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The default config is defined by the pipeline component factory and describes
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how the component should be configured. You can override its settings via the
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`config` argument on [`nlp.add_pipe`](/api/language#add_pipe) or in your
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[`config.cfg` for training](/usage/training#config). See the
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[model architectures](/api/architectures) documentation for details on the
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architectures and their arguments and hyperparameters.
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> #### Example
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>
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> ```python
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> from spacy.pipeline.tok2vec import DEFAULT_TOK2VEC_MODEL
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> config = {"model": DEFAULT_TOK2VEC_MODEL}
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> nlp.add_pipe("tok2vec", config=config)
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> ```
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| Setting | Description |
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| ------- | ------------------------------------------------------------------------------------------------------------------ |
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| `model` | The model to use. Defaults to [HashEmbedCNN](/api/architectures#HashEmbedCNN). ~~Model[List[Doc], List[Floats2d]~~ |
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```python
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%%GITHUB_SPACY/spacy/pipeline/tok2vec.py
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```
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## Tok2Vec.\_\_init\_\_ {#init tag="method"}
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> #### Example
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>
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> ```python
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> # Construction via add_pipe with default model
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> tok2vec = nlp.add_pipe("tok2vec")
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>
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> # Construction via add_pipe with custom model
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> config = {"model": {"@architectures": "my_tok2vec"}}
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> parser = nlp.add_pipe("tok2vec", config=config)
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>
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> # Construction from class
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> from spacy.pipeline import Tok2Vec
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> tok2vec = Tok2Vec(nlp.vocab, model)
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> ```
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Create a new pipeline instance. In your application, you would normally use a
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shortcut for this and instantiate the component using its string name and
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[`nlp.add_pipe`](/api/language#create_pipe).
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| Name | Description |
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| ------- | ------------------------------------------------------------------------------------------------------------------------- |
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| `vocab` | The shared vocabulary. ~~Vocab~~ |
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| `model` | The Thinc [`Model`](https://thinc.ai/docs/api-model) powering the pipeline component. ~~Model[List[Doc], List[Floats2d]~~ |
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| `name` | String name of the component instance. Used to add entries to the `losses` during training. ~~str~~ |
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## Tok2Vec.\_\_call\_\_ {#call tag="method"}
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Apply the pipe to one document and add context-sensitive embeddings to the
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`Doc.tensor` attribute, allowing them to be used as features by downstream
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components. The document is modified in place, and returned. This usually
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happens under the hood when the `nlp` object is called on a text and all
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pipeline components are applied to the `Doc` in order. Both
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[`__call__`](/api/tok2vec#call) and [`pipe`](/api/tok2vec#pipe) delegate to the
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[`predict`](/api/tok2vec#predict) and
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[`set_annotations`](/api/tok2vec#set_annotations) methods.
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> #### Example
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>
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> ```python
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> doc = nlp("This is a sentence.")
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> tok2vec = nlp.add_pipe("tok2vec")
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> # This usually happens under the hood
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> processed = tok2vec(doc)
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> ```
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| Name | Description |
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| ----------- | -------------------------------- |
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| `doc` | The document to process. ~~Doc~~ |
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| **RETURNS** | The processed document. ~~Doc~~ |
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## Tok2Vec.pipe {#pipe tag="method"}
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Apply the pipe to a stream of documents. This usually happens under the hood
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when the `nlp` object is called on a text and all pipeline components are
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applied to the `Doc` in order. Both [`__call__`](/api/tok2vec#call) and
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[`pipe`](/api/tok2vec#pipe) delegate to the [`predict`](/api/tok2vec#predict)
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and [`set_annotations`](/api/tok2vec#set_annotations) methods.
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> #### Example
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>
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> ```python
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> tok2vec = nlp.add_pipe("tok2vec")
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> for doc in tok2vec.pipe(docs, batch_size=50):
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> pass
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> ```
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| Name | Description |
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| -------------- | ------------------------------------------------------------- |
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| `stream` | A stream of documents. ~~Iterable[Doc]~~ |
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| _keyword-only_ | |
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| `batch_size` | The number of documents to buffer. Defaults to `128`. ~~int~~ |
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| **YIELDS** | The processed documents in order. ~~Doc~~ |
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## Tok2Vec.initialize {#initialize tag="method"}
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Initialize the component for training and return an
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[`Optimizer`](https://thinc.ai/docs/api-optimizers). `get_examples` should be a
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function that returns an iterable of [`Example`](/api/example) objects. The data
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examples are used to **initialize the model** of the component and can either be
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the full training data or a representative sample. Initialization includes
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validating the network,
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[inferring missing shapes](https://thinc.ai/docs/usage-models#validation) and
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setting up the label scheme based on the data. This method is typically called
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by [`Language.initialize`](/api/language#initialize).
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> #### Example
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>
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> ```python
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> tok2vec = nlp.add_pipe("tok2vec")
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> tok2vec.initialize(lambda: [], nlp=nlp)
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> ```
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| Name | Description |
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| -------------- | ------------------------------------------------------------------------------------------------------------------------------------- |
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| `get_examples` | Function that returns gold-standard annotations in the form of [`Example`](/api/example) objects. ~~Callable[[], Iterable[Example]]~~ |
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| _keyword-only_ | |
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| `nlp` | The current `nlp` object. Defaults to `None`. ~~Optional[Language]~~ |
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## Tok2Vec.predict {#predict tag="method"}
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Apply the component's model to a batch of [`Doc`](/api/doc) objects without
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modifying them.
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> #### Example
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>
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> ```python
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> tok2vec = nlp.add_pipe("tok2vec")
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> scores = tok2vec.predict([doc1, doc2])
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> ```
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| Name | Description |
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| ----------- | ------------------------------------------- |
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| `docs` | The documents to predict. ~~Iterable[Doc]~~ |
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| **RETURNS** | The model's prediction for each document. |
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## Tok2Vec.set_annotations {#set_annotations tag="method"}
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Modify a batch of [`Doc`](/api/doc) objects, using pre-computed scores.
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> #### Example
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>
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> ```python
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> tok2vec = nlp.add_pipe("tok2vec")
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> scores = tok2vec.predict(docs)
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> tok2vec.set_annotations(docs, scores)
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> ```
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| Name | Description |
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| -------- | ------------------------------------------------- |
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| `docs` | The documents to modify. ~~Iterable[Doc]~~ |
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| `scores` | The scores to set, produced by `Tok2Vec.predict`. |
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## Tok2Vec.update {#update tag="method"}
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Learn from a batch of [`Example`](/api/example) objects containing the
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predictions and gold-standard annotations, and update the component's model.
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Delegates to [`predict`](/api/tok2vec#predict).
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> #### Example
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>
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> ```python
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> tok2vec = nlp.add_pipe("tok2vec")
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> optimizer = nlp.initialize()
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> losses = tok2vec.update(examples, sgd=optimizer)
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> ```
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| Name | Description |
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| -------------- | ------------------------------------------------------------------------------------------------------------------------ |
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| `examples` | A batch of [`Example`](/api/example) objects to learn from. ~~Iterable[Example]~~ |
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| _keyword-only_ | |
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| `drop` | The dropout rate. ~~float~~ |
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| `sgd` | An optimizer. Will be created via [`create_optimizer`](#create_optimizer) if not set. ~~Optional[Optimizer]~~ |
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| `losses` | Optional record of the loss during training. Updated using the component name as the key. ~~Optional[Dict[str, float]]~~ |
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| **RETURNS** | The updated `losses` dictionary. ~~Dict[str, float]~~ |
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## Tok2Vec.create_optimizer {#create_optimizer tag="method"}
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Create an optimizer for the pipeline component.
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> #### Example
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>
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> ```python
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> tok2vec = nlp.add_pipe("tok2vec")
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> optimizer = tok2vec.create_optimizer()
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> ```
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| Name | Description |
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| ----------- | ---------------------------- |
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| **RETURNS** | The optimizer. ~~Optimizer~~ |
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## Tok2Vec.use_params {#use_params tag="method, contextmanager"}
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Modify the pipe's model to use the given parameter values. At the end of the
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context, the original parameters are restored.
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> #### Example
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>
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> ```python
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> tok2vec = nlp.add_pipe("tok2vec")
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> with tok2vec.use_params(optimizer.averages):
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> tok2vec.to_disk("/best_model")
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> ```
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| Name | Description |
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| -------- | -------------------------------------------------- |
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| `params` | The parameter values to use in the model. ~~dict~~ |
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## Tok2Vec.to_disk {#to_disk tag="method"}
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Serialize the pipe to disk.
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> #### Example
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>
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> ```python
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> tok2vec = nlp.add_pipe("tok2vec")
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> tok2vec.to_disk("/path/to/tok2vec")
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> ```
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| Name | Description |
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| -------------- | ------------------------------------------------------------------------------------------------------------------------------------------ |
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| `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]~~ |
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| _keyword-only_ | |
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| `exclude` | String names of [serialization fields](#serialization-fields) to exclude. ~~Iterable[str]~~ |
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## Tok2Vec.from_disk {#from_disk tag="method"}
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Load the pipe from disk. Modifies the object in place and returns it.
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> #### Example
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>
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> ```python
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> tok2vec = nlp.add_pipe("tok2vec")
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> tok2vec.from_disk("/path/to/tok2vec")
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> ```
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| Name | Description |
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| -------------- | ----------------------------------------------------------------------------------------------- |
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| `path` | A path to a directory. Paths may be either strings or `Path`-like objects. ~~Union[str, Path]~~ |
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| _keyword-only_ | |
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| `exclude` | String names of [serialization fields](#serialization-fields) to exclude. ~~Iterable[str]~~ |
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| **RETURNS** | The modified `Tok2Vec` object. ~~Tok2Vec~~ |
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## Tok2Vec.to_bytes {#to_bytes tag="method"}
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> #### Example
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>
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> ```python
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> tok2vec = nlp.add_pipe("tok2vec")
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> tok2vec_bytes = tok2vec.to_bytes()
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> ```
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Serialize the pipe to a bytestring.
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| Name | Description |
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| -------------- | ------------------------------------------------------------------------------------------- |
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| _keyword-only_ | |
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| `exclude` | String names of [serialization fields](#serialization-fields) to exclude. ~~Iterable[str]~~ |
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| **RETURNS** | The serialized form of the `Tok2Vec` object. ~~bytes~~ |
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## Tok2Vec.from_bytes {#from_bytes tag="method"}
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Load the pipe from a bytestring. Modifies the object in place and returns it.
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> #### Example
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>
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> ```python
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> tok2vec_bytes = tok2vec.to_bytes()
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> tok2vec = nlp.add_pipe("tok2vec")
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> tok2vec.from_bytes(tok2vec_bytes)
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> ```
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| Name | Description |
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| -------------- | ------------------------------------------------------------------------------------------- |
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| `bytes_data` | The data to load from. ~~bytes~~ |
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| _keyword-only_ | |
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| `exclude` | String names of [serialization fields](#serialization-fields) to exclude. ~~Iterable[str]~~ |
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| **RETURNS** | The `Tok2Vec` object. ~~Tok2Vec~~ |
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## Serialization fields {#serialization-fields}
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During serialization, spaCy will export several data fields used to restore
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different aspects of the object. If needed, you can exclude them from
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serialization by passing in the string names via the `exclude` argument.
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> #### Example
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>
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> ```python
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> data = tok2vec.to_disk("/path", exclude=["vocab"])
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> ```
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| Name | Description |
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| ------- | -------------------------------------------------------------- |
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| `vocab` | The shared [`Vocab`](/api/vocab). |
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| `cfg` | The config file. You usually don't want to exclude this. |
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| `model` | The binary model data. You usually don't want to exclude this. |
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