spaCy/website/docs/api/transformer.md

title	teaser	tag	source	new	api_base_class	api_string_name
Transformer	Pipeline component for multi-task learning with transformer models	class	github.com/explosion/spacy-transformers/blob/master/spacy_transformers/pipeline_component.py	3	/api/pipe	transformer

Installation

$ pip install spacy-transformers

This component is available via the extension package spacy-transformers. It exposes the component via entry points, so if you have the package installed, using factory = "transformer" in your training config or nlp.add_pipe("transformer") will work out-of-the-box.

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, you can use the custom Doc._.trf_data attribute. The package also adds the function registries @span_getters and @annotation_setters with several built-in registered functions. For more details, see the usage documentation.

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.

Example

from spacy_transformers import Transformer, DEFAULT_CONFIG

nlp.add_pipe("transformer", config=DEFAULT_CONFIG)

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 and can set additional annotations on the Doc.	null_annotation_setter
model	Model	The model to use.	TransformerModel

https://github.com/explosion/spacy-transformers/blob/master/spacy_transformers/pipeline_component.py

Transformer.__init__

Example

# Construction via add_pipe with default model
trf = nlp.add_pipe("transformer")

# Construction via add_pipe with custom config
config = {
    "model": {
        "@architectures": "spacy-transformers.TransformerModel.v1",
        "name": "bert-base-uncased",
        "tokenizer_config": {"use_fast": True}
    }
}
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.

Name	Type	Description
vocab	Vocab	The shared vocabulary.
model	Model	The Thinc Model powering the pipeline component.
annotation_setter	Callable	Function that takes a batch of Doc objects and a 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.

Transformer.__call__

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__ and pipe delegate to the predict and set_annotations methods.

Example

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

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.

Example

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

Initialize the pipe for training, using data examples if available. Returns an Optimizer object.

Example

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 objects.
keyword-only
pipeline	List[Tuple[str, Callable]]	Optional list of pipeline components that this component is part of.
sgd	Optimizer	An optional optimizer. Will be created via create_optimizer if not set.
RETURNS	Optimizer	The optimizer.

Transformer.predict

Apply the pipeline's model to a batch of docs, without modifying them.

Example

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

Modify a batch of documents, using pre-computed scores.

Example

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

Learn from a batch of documents and gold-standard information, updating the pipe's model. Delegates to predict.

Example

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 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.
sgd	Optimizer	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 an optimizer for the pipeline component.

Example

trf = nlp.add_pipe("transformer")
optimizer = trf.create_optimizer()

Name	Type	Description
RETURNS	Optimizer	The optimizer.

Transformer.use_params

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

Example

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

Serialize the pipe to disk.

Example

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 to exclude.

Transformer.from_disk

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

Example

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 to exclude.
RETURNS	Tok2Vec	The modified Tok2Vec object.

Transformer.to_bytes

Example

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 to exclude.
RETURNS	bytes	The serialized form of the Tok2Vec object.

Transformer.from_bytes

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

Example

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 to exclude.
RETURNS	Tok2Vec	The Tok2Vec object.

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

data = trf.to_disk("/path", exclude=["vocab"])

Name	Description
vocab	The shared 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.

TransformerData

Transformer tokens and outputs for one Doc object.

Name	Type	Description
tokens	Dict
tensors	List[FloatsXd]
align	Ragged
width	int

TransformerData.empty

Name	Type	Description
RETURNS	TransformerData

FullTransformerBatch

Name	Type	Description
spans	List[List[Span]]
tokens	transformers.BatchEncoding
tensors	List[torch.Tensor]
align	Ragged
doc_data	List[TransformerData]

FullTransformerBatch.unsplit_by_doc

Name	Type	Description
arrays	List[List[Floats3d]]
RETURNS	FullTransformerBatch

FullTransformerBatch.split_by_doc

Split a TransformerData object that represents a batch into a list with one TransformerData per Doc.

Name	Type	Description
RETURNS	List[TransformerData]

Span getters

Span getters are functions that take a batch of Doc objects and return a lists of Span objects for each doc, to be processed by 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 getters using the @registry.span_getters decorator.

Example

@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

Name	Type	Description
docs	Iterable[Doc]	A batch of Doc objects.
RETURNS	List[List[Span]]	The spans to process by the transformer, one list per Doc.

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

Annotation setters

Annotation setters are functions that that take a batch of Doc objects and a 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

@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.

Custom attributes

The component sets the following custom extension attributes:

Name	Type	Description
Doc.trf_data	TransformerData	Transformer tokens and outputs for the Doc object.