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---
title: Transformer
teaser: Pipeline component for multi-task learning with transformer models
tag: class
source: github.com/explosion/spacy-transformers/blob/master/spacy_transformers/pipeline_component.py
new: 3
api_base_class: /api/pipe
api_string_name: transformer
---
> #### Installation
>
> ```bash
> $ pip install spacy-transformers
> ```
<Infobox title="Important note" variant="warning">
This component is available via the extension package
[`spacy-transformers`](https://github.com/explosion/spacy-transformers). It
exposes the component via entry points, so if you have the package installed,
using `factory = "transformer"` in your
[training config](/usage/training#config) or `nlp.add_pipe("transformer")` will
work out-of-the-box.
</Infobox>
This pipeline component lets you use transformer models in your pipeline. The
component assigns the output of the transformer to the Doc's extension
attributes. We also calculate an alignment between the word-piece tokens and the
spaCy tokenization, so that we can use the last hidden states to set the
`Doc.tensor` attribute. When multiple word-piece tokens align to the same spaCy
token, the spaCy token receives the sum of their values. To access the values,
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you can use the custom [`Doc._.trf_data`](#custom-attributes) attribute. The
package also adds the function registries [`@span_getters`](#span_getters) and
[`@annotation_setters`](#annotation_setters) with several built-in registered
functions. For more details, see the [usage documentation](/usage/transformers).
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## Config and implementation {#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_transformers import Transformer, DEFAULT_CONFIG
>
> nlp.add_pipe("transformer", config=DEFAULT_CONFIG)
> ```
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| Setting | Type | Description | Default |
| ------------------- | ------------------------------------------ | ------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------- |
| `max_batch_items` | int | Maximum size of a padded batch. | `4096` |
| `annotation_setter` | Callable | Function that takes a batch of `Doc` objects and a [`FullTransformerBatch`](/api/transformer#fulltransformerbatch) and can set additional annotations on the `Doc`. | `null_annotation_setter` |
| `model` | [`Model`](https://thinc.ai/docs/api-model) | The model to use. | [TransformerModel](/api/architectures#TransformerModel) |
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```python
https://github.com/explosion/spacy-transformers/blob/master/spacy_transformers/pipeline_component.py
```
## Transformer.\_\_init\_\_ {#init tag="method"}
> #### Example
>
> ```python
> # Construction via add_pipe with default model
> trf = nlp.add_pipe("transformer")
>
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> # Construction via add_pipe with custom config
> config = {
> "model": {
> "@architectures": "spacy-transformers.TransformerModel.v1",
> "name": "bert-base-uncased",
> "tokenizer_config": {"use_fast": True}
> }
> }
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> trf = nlp.add_pipe("transformer", config=config)
>
> # Construction from class
> from spacy_transformers import Transformer
> trf = Transformer(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).
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| Name | Type | Description |
| ------------------- | ------------------------------------------ | --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `vocab` | `Vocab` | The shared vocabulary. |
| `model` | [`Model`](https://thinc.ai/docs/api-model) | The Thinc [`Model`](https://thinc.ai/docs/api-model) powering the pipeline component. |
| `annotation_setter` | `Callable` | Function that takes a batch of `Doc` objects and a [`FullTransformerBatch`](/api/transformer#fulltransformerbatch) and can set additional annotations on the `Doc`. Defaults to `null_annotation_setter`, a function that does nothing. |
| _keyword-only_ | | |
| `name` | str | String name of the component instance. Used to add entries to the `losses` during training. |
| `max_batch_items` | int | Maximum size of a padded batch. Defaults to `128*32`. |
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## Transformer.\_\_call\_\_ {#call tag="method"}
Apply the pipe to one document. 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/transformer#call) and [`pipe`](/api/transformer#pipe) delegate
to the [`predict`](/api/transformer#predict) and
[`set_annotations`](/api/transformer#set_annotations) methods.
> #### Example
>
> ```python
> doc = nlp("This is a sentence.")
> trf = nlp.add_pipe("transformer")
> # This usually happens under the hood
> processed = transformer(doc)
> ```
| Name | Type | Description |
| ----------- | ----- | ------------------------ |
| `doc` | `Doc` | The document to process. |
| **RETURNS** | `Doc` | The processed document. |
## Transformer.pipe {#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/transformer#call) and
[`pipe`](/api/transformer#pipe) delegate to the
[`predict`](/api/transformer#predict) and
[`set_annotations`](/api/transformer#set_annotations) methods.
> #### Example
>
> ```python
> trf = nlp.add_pipe("transformer")
> for doc in trf.pipe(docs, batch_size=50):
> pass
> ```
| Name | Type | Description |
| -------------- | --------------- | ----------------------------------------------------- |
| `stream` | `Iterable[Doc]` | A stream of documents. |
| _keyword-only_ | | |
| `batch_size` | int | The number of documents to buffer. Defaults to `128`. |
| **YIELDS** | `Doc` | The processed documents in order. |
## Transformer.begin_training {#begin_training tag="method"}
Initialize the pipe for training, using data examples if available. Returns an
[`Optimizer`](https://thinc.ai/docs/api-optimizers) object.
> #### Example
>
> ```python
> trf = nlp.add_pipe("transformer")
> optimizer = trf.begin_training(pipeline=nlp.pipeline)
> ```
| Name | Type | Description |
| -------------- | --------------------------------------------------- | -------------------------------------------------------------------------------------------------------------- |
| `get_examples` | `Callable[[], Iterable[Example]]` | Optional function that returns gold-standard annotations in the form of [`Example`](/api/example) objects. |
| _keyword-only_ | | |
| `pipeline` | `List[Tuple[str, Callable]]` | Optional list of pipeline components that this component is part of. |
| `sgd` | [`Optimizer`](https://thinc.ai/docs/api-optimizers) | An optional optimizer. Will be created via [`create_optimizer`](/api/transformer#create_optimizer) if not set. |
| **RETURNS** | [`Optimizer`](https://thinc.ai/docs/api-optimizers) | The optimizer. |
## Transformer.predict {#predict tag="method"}
Apply the pipeline's model to a batch of docs, without modifying them.
> #### Example
>
> ```python
> trf = nlp.add_pipe("transformer")
> scores = trf.predict([doc1, doc2])
> ```
| Name | Type | Description |
| ----------- | --------------- | ----------------------------------------- |
| `docs` | `Iterable[Doc]` | The documents to predict. |
| **RETURNS** | - | The model's prediction for each document. |
## Transformer.set_annotations {#set_annotations tag="method"}
Modify a batch of documents, using pre-computed scores.
> #### Example
>
> ```python
> trf = nlp.add_pipe("transformer")
> scores = trf.predict(docs)
> trf.set_annotations(docs, scores)
> ```
| Name | Type | Description |
| -------- | --------------- | ----------------------------------------------------- |
| `docs` | `Iterable[Doc]` | The documents to modify. |
| `scores` | - | The scores to set, produced by `Transformer.predict`. |
## Transformer.update {#update tag="method"}
Learn from a batch of documents and gold-standard information, updating the
pipe's model. Delegates to [`predict`](/api/transformer#predict).
> #### Example
>
> ```python
> trf = nlp.add_pipe("transformer")
> optimizer = nlp.begin_training()
> losses = trf.update(examples, sgd=optimizer)
> ```
| Name | Type | Description |
| ----------------- | --------------------------------------------------- | ----------------------------------------------------------------------------------------------------------------------------------------- |
| `examples` | `Iterable[Example]` | A batch of [`Example`](/api/example) objects to learn from. |
| _keyword-only_ | | |
| `drop` | float | The dropout rate. |
| `set_annotations` | bool | Whether or not to update the `Example` objects with the predictions, delegating to [`set_annotations`](/api/transformer#set_annotations). |
| `sgd` | [`Optimizer`](https://thinc.ai/docs/api-optimizers) | The optimizer. |
| `losses` | `Dict[str, float]` | Optional record of the loss during training. Updated using the component name as the key. |
| **RETURNS** | `Dict[str, float]` | The updated `losses` dictionary. |
## Transformer.create_optimizer {#create_optimizer tag="method"}
Create an optimizer for the pipeline component.
> #### Example
>
> ```python
> trf = nlp.add_pipe("transformer")
> optimizer = trf.create_optimizer()
> ```
| Name | Type | Description |
| ----------- | --------------------------------------------------- | -------------- |
| **RETURNS** | [`Optimizer`](https://thinc.ai/docs/api-optimizers) | The optimizer. |
## Transformer.use_params {#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
> trf = nlp.add_pipe("transformer")
> with trf.use_params(optimizer.averages):
> trf.to_disk("/best_model")
> ```
| Name | Type | Description |
| -------- | ---- | ----------------------------------------- |
| `params` | dict | The parameter values to use in the model. |
## Transformer.to_disk {#to_disk tag="method"}
Serialize the pipe to disk.
> #### Example
>
> ```python
> trf = nlp.add_pipe("transformer")
> trf.to_disk("/path/to/transformer")
> ```
| Name | Type | Description |
| -------------- | --------------- | --------------------------------------------------------------------------------------------------------------------- |
| `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. |
| _keyword-only_ | | |
| `exclude` | `Iterable[str]` | String names of [serialization fields](#serialization-fields) to exclude. |
## Transformer.from_disk {#from_disk tag="method"}
Load the pipe from disk. Modifies the object in place and returns it.
> #### Example
>
> ```python
> trf = nlp.add_pipe("transformer")
> trf.from_disk("/path/to/transformer")
> ```
| Name | Type | Description |
| -------------- | --------------- | -------------------------------------------------------------------------- |
| `path` | str / `Path` | A path to a directory. Paths may be either strings or `Path`-like objects. |
| _keyword-only_ | | |
| `exclude` | `Iterable[str]` | String names of [serialization fields](#serialization-fields) to exclude. |
| **RETURNS** | `Tok2Vec` | The modified `Tok2Vec` object. |
## Transformer.to_bytes {#to_bytes tag="method"}
> #### Example
>
> ```python
> trf = nlp.add_pipe("transformer")
> trf_bytes = trf.to_bytes()
> ```
Serialize the pipe to a bytestring.
| Name | Type | Description |
| -------------- | --------------- | ------------------------------------------------------------------------- |
| _keyword-only_ | | |
| `exclude` | `Iterable[str]` | String names of [serialization fields](#serialization-fields) to exclude. |
| **RETURNS** | bytes | The serialized form of the `Tok2Vec` object. |
## Transformer.from_bytes {#from_bytes tag="method"}
Load the pipe from a bytestring. Modifies the object in place and returns it.
> #### Example
>
> ```python
> trf_bytes = trf.to_bytes()
> trf = nlp.add_pipe("transformer")
> trf.from_bytes(trf_bytes)
> ```
| Name | Type | Description |
| -------------- | --------------- | ------------------------------------------------------------------------- |
| `bytes_data` | bytes | The data to load from. |
| _keyword-only_ | | |
| `exclude` | `Iterable[str]` | String names of [serialization fields](#serialization-fields) to exclude. |
| **RETURNS** | `Tok2Vec` | The `Tok2Vec` object. |
## Serialization fields {#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 = trf.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. |
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## TransformerData {#transformerdata tag="dataclass"}
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Transformer tokens and outputs for one `Doc` object.
| Name | Type | Description |
| --------- | -------------------------------------------------- | ----------------------------------------- |
| `tokens` | `Dict` | <!-- TODO: --> |
| `tensors` | `List[FloatsXd]` | <!-- TODO: --> |
| `align` | [`Ragged`](https://thinc.ai/docs/api-types#ragged) | <!-- TODO: --> |
| `width` | int | <!-- TODO: also mention it's property --> |
### TransformerData.empty {#transformerdata-emoty tag="classmethod"}
<!-- TODO: -->
| Name | Type | Description |
| ----------- | ----------------- | -------------- |
| **RETURNS** | `TransformerData` | <!-- TODO: --> |
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## FullTransformerBatch {#fulltransformerbatch tag="dataclass"}
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<!-- TODO: -->
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| Name | Type | Description |
| ---------- | -------------------------------------------------------------------------------------------------------------------------- | ----------------------------------------- |
| `spans` | `List[List[Span]]` | <!-- TODO: --> |
| `tokens` | [`transformers.BatchEncoding`](https://huggingface.co/transformers/main_classes/tokenizer.html#transformers.BatchEncoding) | <!-- TODO: --> |
| `tensors` | `List[torch.Tensor]` | <!-- TODO: --> |
| `align` | [`Ragged`](https://thinc.ai/docs/api-types#ragged) | <!-- TODO: --> |
| `doc_data` | `List[TransformerData]` | <!-- TODO: also mention it's property --> |
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### FullTransformerBatch.unsplit_by_doc {#fulltransformerbatch-unsplit_by_doc tag="method"}
<!-- TODO: -->
| Name | Type | Description |
| ----------- | ---------------------- | -------------- |
| `arrays` | `List[List[Floats3d]]` | <!-- TODO: --> |
| **RETURNS** | `FullTransformerBatch` | <!-- TODO: --> |
### FullTransformerBatch.split_by_doc {#fulltransformerbatch-split_by_doc tag="method"}
Split a `TransformerData` object that represents a batch into a list with one
`TransformerData` per `Doc`.
| Name | Type | Description |
| ----------- | ----------------------- | -------------- |
| **RETURNS** | `List[TransformerData]` | <!-- TODO: --> |
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## Span getters {#span_getters tag="registered functions" source="github.com/explosion/spacy-transformers/blob/master/spacy_transformers/span_getters.py"}
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<!-- TODO: details on what this is for -->
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Span getters are functions that take a batch of [`Doc`](/api/doc) objects and
return a lists of [`Span`](/api/span) objects for each doc, to be processed by
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the transformer. The returned spans can overlap. Span getters can be referenced
in the config's `[components.transformer.model.get_spans]` block to customize
the sequences processed by the transformer. You can also register custom span
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getters using the `@registry.span_getters` decorator.
> #### Example
>
> ```python
> @registry.span_getters("sent_spans.v1")
> def configure_get_sent_spans() -> Callable:
> def get_sent_spans(docs: Iterable[Doc]) -> List[List[Span]]:
> return [list(doc.sents) for doc in docs]
>
> return get_sent_spans
> ```
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| Name | Type | Description |
| ----------- | ------------------ | ---------------------------------------- |
| `docs` | `Iterable[Doc]` | A batch of `Doc` objects. |
| **RETURNS** | `List[List[Span]]` | The spans to process by the transformer. |
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The following built-in functions are available:
| Name | Description |
| ------------------ | ------------------------------------------------------------------ |
| `doc_spans.v1` | Create a span for each doc (no transformation, process each text). |
| `sent_spans.v1` | Create a span for each sentence if sentence boundaries are set. |
| `strided_spans.v1` | <!-- TODO: --> |
## Annotation setters {#annotation_setters tag="registered functions" source="github.com/explosion/spacy-transformers/blob/master/spacy_transformers/annotation_setters.py"}
Annotation setters are functions that that take a batch of `Doc` objects and a
[`FullTransformerBatch`](/api/transformer#fulltransformerbatch) and can set
additional annotations on the `Doc`, e.g. to set custom or built-in attributes.
You can register custom annotation setters using the
`@registry.annotation_setters` decorator.
> #### Example
>
> ```python
> @registry.annotation_setters("spacy-transformer.null_annotation_setter.v1")
> def configure_null_annotation_setter() -> Callable:
> def setter(docs: List[Doc], trf_data: FullTransformerBatch) -> None:
> pass
>
> return setter
> ```
| Name | Type | Description |
| ---------- | ---------------------- | ------------------------------------ |
| `docs` | `List[Doc]` | A batch of `Doc` objects. |
| `trf_data` | `FullTransformerBatch` | The transformers data for the batch. |
The following built-in functions are available:
| Name | Description |
| --------------------------------------------- | ------------------------------------- |
| `spacy-transformer.null_annotation_setter.v1` | Don't set any additional annotations. |
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## Custom attributes {#custom-attributes}
The component sets the following
[custom extension attributes](/usage/processing-pipeline#custom-components-attributes):
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| Name | Type | Description |
| -------------- | ----------------------------------------------------- | ---------------------------------------------------- |
| `Doc.trf_data` | [`TransformerData`](/api/transformer#transformerdata) | Transformer tokens and outputs for the `Doc` object. |