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303 lines
14 KiB
Markdown
303 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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<!-- TODO: intro describing component -->
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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 | Type | Description | Default |
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| ------- | ------------------------------------------ | ----------------- | ----------------------------------------------- |
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| `model` | [`Model`](https://thinc.ai/docs/api-model) | The model to use. | [HashEmbedCNN](/api/architectures#HashEmbedCNN) |
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```python
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https://github.com/explosion/spaCy/blob/develop/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 | Type | Description |
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| ------- | ------------------------------------------ | ------------------------------------------------------------------------------------------- |
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| `vocab` | `Vocab` | The shared vocabulary. |
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| `model` | [`Model`](https://thinc.ai/docs/api-model) | The Thinc [`Model`](https://thinc.ai/docs/api-model) powering the pipeline component. |
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| `name` | str | String name of the component instance. Used to add entries to the `losses` during training. |
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## Tok2Vec.\_\_call\_\_ {#call tag="method"}
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Apply the pipe to one document. The document is modified in place, and returned.
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This usually happens under the hood when the `nlp` object is called on a text
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and all 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 | Type | Description |
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| ----------- | ----- | ------------------------ |
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| `doc` | `Doc` | The document to process. |
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| **RETURNS** | `Doc` | The processed document. |
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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 | Type | Description |
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| -------------- | --------------- | ----------------------------------------------------- |
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| `stream` | `Iterable[Doc]` | A stream of documents. |
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| _keyword-only_ | | |
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| `batch_size` | int | The number of documents to buffer. Defaults to `128`. |
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| **YIELDS** | `Doc` | The processed documents in order. |
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## Tok2Vec.begin_training {#begin_training tag="method"}
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Initialize the pipe for training, using data examples if available. Return an
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[`Optimizer`](https://thinc.ai/docs/api-optimizers) object.
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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.begin_training(pipeline=nlp.pipeline)
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> ```
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| Name | Type | Description |
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| -------------- | --------------------------------------------------- | ---------------------------------------------------------------------------------------------------------- |
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| `get_examples` | `Callable[[], Iterable[Example]]` | Optional function that returns gold-standard annotations in the form of [`Example`](/api/example) objects. |
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| _keyword-only_ | | |
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| `pipeline` | `List[Tuple[str, Callable]]` | Optional list of pipeline components that this component is part of. |
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| `sgd` | [`Optimizer`](https://thinc.ai/docs/api-optimizers) | An optional optimizer. Will be created via [`create_optimizer`](/api/tok2vec#create_optimizer) if not set. |
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| **RETURNS** | [`Optimizer`](https://thinc.ai/docs/api-optimizers) | The optimizer. |
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## Tok2Vec.predict {#predict tag="method"}
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Apply the pipeline's model to a batch of docs, without 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 | Type | Description |
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| ----------- | --------------- | ----------------------------------------- |
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| `docs` | `Iterable[Doc]` | The documents to predict. |
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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 documents, 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 | Type | Description |
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| -------- | --------------- | ------------------------------------------------- |
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| `docs` | `Iterable[Doc]` | The documents to modify. |
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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 documents and gold-standard information, updating the
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pipe's model. 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.begin_training()
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> losses = tok2vec.update(examples, sgd=optimizer)
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> ```
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| Name | Type | Description |
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| ----------------- | --------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------- |
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| `examples` | `Iterable[Example]` | A batch of [`Example`](/api/example) objects to learn from. |
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| _keyword-only_ | | |
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| `drop` | float | The dropout rate. |
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| `set_annotations` | bool | Whether or not to update the `Example` objects with the predictions, delegating to [`set_annotations`](/api/tok2vec#set_annotations). |
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| `sgd` | [`Optimizer`](https://thinc.ai/docs/api-optimizers) | The optimizer. |
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| `losses` | `Dict[str, float]` | Optional record of the loss during training. Updated using the component name as the key. |
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| **RETURNS** | `Dict[str, float]` | The updated `losses` dictionary. |
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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 | Type | Description |
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| ----------- | --------------------------------------------------- | -------------- |
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| **RETURNS** | [`Optimizer`](https://thinc.ai/docs/api-optimizers) | The 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 | Type | Description |
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| -------- | ---- | ----------------------------------------- |
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| `params` | dict | The parameter values to use in the model. |
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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 | Type | Description |
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| --------- | --------------- | --------------------------------------------------------------------------------------------------------------------- |
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| `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. |
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| `exclude` | `Iterable[str]` | String names of [serialization fields](#serialization-fields) to exclude. |
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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 | Type | Description |
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| ----------- | --------------- | -------------------------------------------------------------------------- |
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| `path` | str / `Path` | A path to a directory. Paths may be either strings or `Path`-like objects. |
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| `exclude` | `Iterable[str]` | String names of [serialization fields](#serialization-fields) to exclude. |
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| **RETURNS** | `Tok2Vec` | The modified `Tok2Vec` object. |
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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 | Type | Description |
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| ----------- | --------------- | ------------------------------------------------------------------------- |
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| `exclude` | `Iterable[str]` | String names of [serialization fields](#serialization-fields) to exclude. |
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| **RETURNS** | bytes | The serialized form of the `Tok2Vec` object. |
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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 | Type | Description |
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| ------------ | --------------- | ------------------------------------------------------------------------- |
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| `bytes_data` | bytes | The data to load from. |
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| `exclude` | `Iterable[str]` | String names of [serialization fields](#serialization-fields) to exclude. |
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| **RETURNS** | `Tok2Vec` | The `Tok2Vec` object. |
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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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