---
title: Tok2Vec
source: spacy/pipeline/tok2vec.py
version: 3
teaser: null
api_base_class: /api/pipe
api_string_name: tok2vec
api_trainable: 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`](/api/dependencyparser), [`Tagger`](/api/tagger) and
[`EntityRecognizer`](/api/entityrecognizer).

In order to use the `Tok2Vec` predictions, subsequent components should use the
[Tok2VecListener](/api/architectures#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 {id="config"}

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`](/api/language#add_pipe) or in your
[`config.cfg` for training](/usage/training#config). See the
[model architectures](/api/architectures) documentation for details on the
architectures and their arguments and hyperparameters.

> #### Example
>
> ```python
> 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](/api/architectures#HashEmbedCNN). ~~Model[List[Doc], List[Floats2d]~~ |

```python
%%GITHUB_SPACY/spacy/pipeline/tok2vec.py
```

## Tok2Vec.\_\_init\_\_ {id="init",tag="method"}

> #### Example
>
> ```python
> # 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`](/api/language#create_pipe).

| Name    | Description                                                                                                               |
| ------- | ------------------------------------------------------------------------------------------------------------------------- |
| `vocab` | The shared vocabulary. ~~Vocab~~                                                                                          |
| `model` | The Thinc [`Model`](https://thinc.ai/docs/api-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\_\_ {id="call",tag="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__`](/api/tok2vec#call) and [`pipe`](/api/tok2vec#pipe) delegate to the
[`predict`](/api/tok2vec#predict) and
[`set_annotations`](/api/tok2vec#set_annotations) methods.

> #### Example
>
> ```python
> 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.distill {id="distill", tag="method,experimental", version="4"}

Performs an update of the student pipe's model using the student's distillation 
examples and sets the annotations of the teacher's distillation examples using 
the teacher pipe. 

Unlike other trainable pipes, the student pipe doesn't directly learn its 
representations from the teacher. However, since downstream pipes that do 
perform distillation expect the tok2vec annotations to be present on the 
correct distillation examples, we need to ensure that they are set beforehand.

The distillation is performed on ~~Example~~ objects. The `Example.reference`
and `Example.predicted` ~~Doc~~s must have the same number of tokens and the
same orthography. Even though the reference does not need have to have gold
annotations, the teacher could adds its own annotations when necessary.

This feature is experimental.

> #### Example
>
> ```python
> teacher_pipe = teacher.add_pipe("tok2vec")
> student_pipe = student.add_pipe("tok2vec")
> optimizer = nlp.resume_training()
> losses = student.distill(teacher_pipe, examples, sgd=optimizer)
> ```

| Name           | Description                                                                                                                                 |
| -------------- | ------------------------------------------------------------------------------------------------------------------------------------------- |
| `teacher_pipe` | The teacher pipe to use for prediction. ~~Optional[TrainablePipe]~~                                                                                 |
| `examples`     | Distillation examples. The reference (teacher) and predicted (student) docs must have the same number of tokens and the same orthography. ~~Iterable[Example]~~ |
| _keyword-only_ |                                                                                                                                             |
| `drop`         | Dropout rate. ~~float~~                                                                                                                     |
| `sgd`          | An optimizer. Will be created via [`create_optimizer`](#create_optimizer) if not set. ~~Optional[Optimizer]~~                               |
| `losses`       | Optional record of the loss during distillation. Updated using the component name as the key. ~~Optional[Dict[str, float]]~~                |
| **RETURNS**    | The updated `losses` dictionary. ~~Dict[str, float]~~                                                                                       |

## Tok2Vec.pipe {id="pipe",tag="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__`](/api/tok2vec#call) and
[`pipe`](/api/tok2vec#pipe) delegate to the [`predict`](/api/tok2vec#predict)
and [`set_annotations`](/api/tok2vec#set_annotations) methods.

> #### Example
>
> ```python
> 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 {id="initialize",tag="method"}

Initialize the component for training and return an
[`Optimizer`](https://thinc.ai/docs/api-optimizers). `get_examples` should be a
function that returns an iterable of [`Example`](/api/example) objects. **At
least one example should be supplied.** 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](https://thinc.ai/docs/usage-models#validation) and
setting up the label scheme based on the data. This method is typically called
by [`Language.initialize`](/api/language#initialize).

> #### Example
>
> ```python
> tok2vec = nlp.add_pipe("tok2vec")
> tok2vec.initialize(lambda: examples, nlp=nlp)
> ```

| Name           | Description                                                                                                                                                                |
| -------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `get_examples` | Function that returns gold-standard annotations in the form of [`Example`](/api/example) objects. Must contain at least one `Example`. ~~Callable[[], Iterable[Example]]~~ |
| _keyword-only_ |                                                                                                                                                                            |
| `nlp`          | The current `nlp` object. Defaults to `None`. ~~Optional[Language]~~                                                                                                       |

## Tok2Vec.predict {id="predict",tag="method"}

Apply the component's model to a batch of [`Doc`](/api/doc) objects without
modifying them.

> #### Example
>
> ```python
> 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 {id="set_annotations",tag="method"}

Modify a batch of [`Doc`](/api/doc) objects, using pre-computed scores.

> #### Example
>
> ```python
> 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 {id="update",tag="method"}

Learn from a batch of [`Example`](/api/example) objects containing the
predictions and gold-standard annotations, and update the component's model.
Delegates to [`predict`](/api/tok2vec#predict).

> #### Example
>
> ```python
> tok2vec = nlp.add_pipe("tok2vec")
> optimizer = nlp.initialize()
> losses = tok2vec.update(examples, sgd=optimizer)
> ```

| Name           | Description                                                                                                              |
| -------------- | ------------------------------------------------------------------------------------------------------------------------ |
| `examples`     | A batch of [`Example`](/api/example) objects to learn from. ~~Iterable[Example]~~                                        |
| _keyword-only_ |                                                                                                                          |
| `drop`         | The dropout rate. ~~float~~                                                                                              |
| `sgd`          | An optimizer. Will be created via [`create_optimizer`](#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 {id="create_optimizer",tag="method"}

Create an optimizer for the pipeline component.

> #### Example
>
> ```python
> tok2vec = nlp.add_pipe("tok2vec")
> optimizer = tok2vec.create_optimizer()
> ```

| Name        | Description                  |
| ----------- | ---------------------------- |
| **RETURNS** | The optimizer. ~~Optimizer~~ |

## Tok2Vec.use_params {id="use_params",tag="method, contextmanager"}

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

> #### Example
>
> ```python
> 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 {id="to_disk",tag="method"}

Serialize the pipe to disk.

> #### Example
>
> ```python
> 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](#serialization-fields) to exclude. ~~Iterable[str]~~                                                |

## Tok2Vec.from_disk {id="from_disk",tag="method"}

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

> #### Example
>
> ```python
> 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](#serialization-fields) to exclude. ~~Iterable[str]~~     |
| **RETURNS**    | The modified `Tok2Vec` object. ~~Tok2Vec~~                                                      |

## Tok2Vec.to_bytes {id="to_bytes",tag="method"}

> #### Example
>
> ```python
> 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](#serialization-fields) to exclude. ~~Iterable[str]~~ |
| **RETURNS**    | The serialized form of the `Tok2Vec` object. ~~bytes~~                                      |

## Tok2Vec.from_bytes {id="from_bytes",tag="method"}

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

> #### Example
>
> ```python
> 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](#serialization-fields) to exclude. ~~Iterable[str]~~ |
| **RETURNS**    | The `Tok2Vec` object. ~~Tok2Vec~~                                                           |

## Serialization fields {id="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
>
> ```python
> data = tok2vec.to_disk("/path", exclude=["vocab"])
> ```

| Name    | Description                                                    |
| ------- | -------------------------------------------------------------- |
| `vocab` | The shared [`Vocab`](/api/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. |