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Update docs [ci skip]
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Ines Montani
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@ -26,6 +26,8 @@ TODO: intro and how architectures work, link to
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### spacy-transformers.TransformerModel.v1 {#TransformerModel}
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### spacy-transformers.Tok2VecListener.v1 {#spacy-transformers.Tok2VecListener.v1}
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## Parser & NER architectures {#parser source="spacy/ml/models/parser.py"}
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### spacy.TransitionBasedParser.v1 {#TransitionBasedParser}
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@ -304,6 +304,31 @@ factories.
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| `losses` | Registry for functions that create [losses](https://thinc.ai/docs/api-loss). |
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| `initializers` | Registry for functions that create [initializers](https://thinc.ai/docs/api-initializers). |
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### spacy-transformers registry {#registry-transformers}
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The following registries are added by the
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[`spacy-transformers`](https://github.com/explosion/spacy-transformers) package.
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See the [`Transformer`](/api/transformer) API reference and
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[usage docs](/usage/transformers) for details.
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> #### Example
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>
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> ```python
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> import spacy_transformers
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>
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> @spacy_transformers.registry.annotation_setters("my_annotation_setter.v1")
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> def configure_custom_annotation_setter():
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> def annotation_setter(docs, trf_data) -> None:
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> # Set annotations on the docs
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>
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> return annotation_sette
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> ```
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| Registry name | Description |
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| ------------------------------------------------------------ | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
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| [`span_getters`](/api/transformer#span_getters) | Registry for functions that take a batch of `Doc` objects and return a list of `Span` objects to process by the transformer, e.g. sentences. |
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| [`annotation_setters`](/api/transformers#annotation_setters) | Registry for functions that create annotation setters. Annotation setters are functions that take a batch of `Doc` objects and a [`FullTransformerBatch`](/api/transformer#fulltransformerbatch) and can set additional annotations on the `Doc`. |
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## Training data and alignment {#gold source="spacy/gold"}
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### gold.docs_to_json {#docs_to_json tag="function"}
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@ -31,8 +31,10 @@ attributes. We also calculate an alignment between the word-piece tokens and the
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spaCy tokenization, so that we can use the last hidden states to set the
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`Doc.tensor` attribute. When multiple word-piece tokens align to the same spaCy
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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. For
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more details, see the [usage documentation](/usage/transformers).
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you can use the custom [`Doc._.trf_data`](#custom-attributes) attribute. The
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package also adds the function registries [`@span_getters`](#span_getters) and
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[`@annotation_setters`](#annotation_setters) with several built-in registered
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functions. For more details, see the [usage documentation](/usage/transformers).
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## Config and implementation {#config}
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@ -52,9 +54,9 @@ architectures and their arguments and hyperparameters.
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> ```
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| Setting | Type | Description | Default |
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| ------------------- | ------------------------------------------ | --------------------------------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------- |
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| ------------------- | ------------------------------------------ | ------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------- |
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| `max_batch_items` | int | Maximum size of a padded batch. | `4096` |
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| `annotation_setter` | Callable | Function that takes a batch of `Doc` objects and a [`FullTransformerBatch`](#fulltransformerbatch) and can set additional annotations on the `Doc`. | `null_annotation_setter` |
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| `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` |
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| `model` | [`Model`](https://thinc.ai/docs/api-model) | The model to use. | [TransformerModel](/api/architectures#TransformerModel) |
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```python
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@ -390,6 +392,72 @@ Split a `TransformerData` object that represents a batch into a list with one
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| ----------- | ----------------------- | -------------- |
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| **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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Span getters are functions that take a batch of [`Doc`](/api/doc) objects and
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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.
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<!-- TODO: details on what this is for --> Span getters can be referenced in the
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config's `[components.transformer.model.get_spans]` block to customize the
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sequences processed by the transformer. You can also register custom span
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getters using the `@registry.span_getters` decorator.
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> #### Example
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>
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> ```python
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> @registry.span_getters("sent_spans.v1")
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> def configure_get_sent_spans() -> Callable:
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> def get_sent_spans(docs: Iterable[Doc]) -> List[List[Span]]:
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> return [list(doc.sents) for doc in docs]
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>
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> return get_sent_spans
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> ```
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| Name | Type | Description |
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| ----------- | ------------------ | ------------------------------------------------------------ |
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| `docs` | `Iterable[Doc]` | A batch of `Doc` objects. |
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| **RETURNS** | `List[List[Span]]` | The spans to process by the transformer, one list per `Doc`. |
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The following built-in functions are available:
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| Name | Description |
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| ------------------ | ------------------------------------------------------------------ |
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| `doc_spans.v1` | Create a span for each doc (no transformation, process each text). |
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| `sent_spans.v1` | Create a span for each sentence if sentence boundaries are set. |
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| `strided_spans.v1` | <!-- TODO: --> |
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## Annotation setters {#annotation_setters tag="registered functions" source="github.com/explosion/spacy-transformers/blob/master/spacy_transformers/annotation_setters.py"}
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Annotation setters are functions that that take a batch of `Doc` objects and a
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[`FullTransformerBatch`](/api/transformer#fulltransformerbatch) and can set
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additional annotations on the `Doc`, e.g. to set custom or built-in attributes.
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You can register custom annotation setters using the
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`@registry.annotation_setters` decorator.
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> #### Example
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>
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> ```python
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> @registry.annotation_setters("spacy-transformer.null_annotation_setter.v1")
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> def configure_null_annotation_setter() -> Callable:
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> def setter(docs: List[Doc], trf_data: FullTransformerBatch) -> None:
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> pass
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>
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> return setter
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> ```
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| Name | Type | Description |
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| ---------- | ---------------------- | ------------------------------------ |
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| `docs` | `List[Doc]` | A batch of `Doc` objects. |
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| `trf_data` | `FullTransformerBatch` | The transformers data for the batch. |
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The following built-in functions are available:
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| Name | Description |
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| --------------------------------------------- | ------------------------------------- |
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| `spacy-transformer.null_annotation_setter.v1` | Don't set any additional annotations. |
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## Custom attributes {#custom-attributes}
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The component sets the following
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||||
</svg>
|
||||
|
After Width: | Height: | Size: 14 KiB |
|
|
@ -1,10 +1,17 @@
|
|||
---
|
||||
title: Transformers
|
||||
teaser: Using transformer models like BERT in spaCy
|
||||
menu:
|
||||
- ['Installation', 'install']
|
||||
- ['Runtime Usage', 'runtime']
|
||||
- ['Training Usage', 'training']
|
||||
---
|
||||
|
||||
## Installation {#install hidden="true"}
|
||||
|
||||
spaCy v3.0 lets you use almost **any statistical model** to power your pipeline.
|
||||
You can use models implemented in a variety of frameworks, including TensorFlow,
|
||||
You can use models implemented in a variety of
|
||||
[frameworks](https://thinc.ai/docs/usage-frameworks), including TensorFlow,
|
||||
PyTorch and MXNet. To keep things sane, spaCy expects models from these
|
||||
frameworks to be wrapped with a common interface, using our machine learning
|
||||
library [Thinc](https://thinc.ai). A transformer model is just a statistical
|
||||
|
|
@ -15,34 +22,110 @@ that do the required plumbing. We also provide a pipeline component,
|
|||
[`Transformer`](/api/transformer), that lets you do multi-task learning and lets
|
||||
you save the transformer outputs for later use.
|
||||
|
||||
<Project id="en_core_bert">
|
||||
To use transformers with spaCy, you need the
|
||||
[`spacy-transformers`](https://github.com/explosion/spacy-transformers) package
|
||||
installed. It takes care of all the setup behind the scenes, and makes sure the
|
||||
transformer pipeline component is available to spaCy.
|
||||
|
||||
Try out a BERT-based model pipeline using this project template: swap in your
|
||||
data, edit the settings and hyperparameters and train, evaluate, package and
|
||||
visualize your model.
|
||||
```bash
|
||||
$ pip install spacy-transformers
|
||||
```
|
||||
|
||||
</Project>
|
||||
<!-- TODO: the text below has been copied from the spacy-transformers repo and needs to be updated and adjusted -->
|
||||
|
||||
<!-- TODO: the text below has been copied from the spacy-transformers repo and needs to be updated and adjusted
|
||||
## Runtime usage {#runtime}
|
||||
|
||||
### Training usage
|
||||
Transformer models can be used as **drop-in replacements** for other types of
|
||||
neural networks, so your spaCy pipeline can include them in a way that's
|
||||
completely invisible to the user. Users will download, load and use the model in
|
||||
the standard way, like any other spaCy pipeline. Instead of using the
|
||||
transformers as subnetworks directly, you can also use them via the
|
||||
[`Transformer`](/api/transformer) pipeline component.
|
||||
|
||||
|
||||
|
||||
The `Transformer` component sets the
|
||||
[`Doc._.trf_data`](/api/transformer#custom_attributes) extension attribute,
|
||||
which lets you access the transformers outputs at runtime.
|
||||
|
||||
```bash
|
||||
$ python -m spacy download en_core_trf_lg
|
||||
```
|
||||
|
||||
```python
|
||||
### Example
|
||||
import spacy
|
||||
|
||||
nlp = spacy.load("en_core_trf_lg")
|
||||
for doc in nlp.pipe(["some text", "some other text"]):
|
||||
tokvecs = doc._.trf_data.tensors[-1]
|
||||
```
|
||||
|
||||
You can also customize how the [`Transformer`](/api/transformer) component sets
|
||||
annotations onto the [`Doc`](/api/doc), by customizing the `annotation_setter`.
|
||||
This callback will be called with the raw input and output data for the whole
|
||||
batch, along with the batch of `Doc` objects, allowing you to implement whatever
|
||||
you need. The annotation setter is called with a batch of [`Doc`](/api/doc)
|
||||
objects and a [`FullTransformerBatch`](/api/transformer#fulltransformerbatch)
|
||||
containing the transformers data for the batch.
|
||||
|
||||
```python
|
||||
def custom_annotation_setter(docs, trf_data):
|
||||
# TODO:
|
||||
...
|
||||
|
||||
nlp = spacy.load("en_core_trf_lg")
|
||||
nlp.get_pipe("transformer").annotation_setter = custom_annotation_setter
|
||||
doc = nlp("This is a text")
|
||||
print() # TODO:
|
||||
```
|
||||
|
||||
## Training usage {#training}
|
||||
|
||||
The recommended workflow for training is to use spaCy's
|
||||
[config system](/usage/training#config), usually via the
|
||||
[`spacy train`](/api/cli#train) command. The config system lets you describe a
|
||||
tree of objects by referring to creation functions, including functions you
|
||||
register yourself. Here's a config snippet for the `Transformer` component,
|
||||
along with matching Python code.
|
||||
[`spacy train`](/api/cli#train) command. The training config defines all
|
||||
component settings and hyperparameters in one place and lets you describe a tree
|
||||
of objects by referring to creation functions, including functions you register
|
||||
yourself.
|
||||
|
||||
<Project id="en_core_bert">
|
||||
|
||||
The easiest way to get started is to clone a transformers-based project
|
||||
template. Swap in your data, edit the settings and hyperparameters and train,
|
||||
evaluate, package and visualize your model.
|
||||
|
||||
</Project>
|
||||
|
||||
The `[components]` section in the [`config.cfg`](#TODO:) describes the pipeline
|
||||
components and the settings used to construct them, including their model
|
||||
implementation. Here's a config snippet for the
|
||||
[`Transformer`](/api/transformer) component, along with matching Python code:
|
||||
|
||||
> #### Python equivalent
|
||||
>
|
||||
> ```python
|
||||
> from spacy_transformers import Transformer, TransformerModel
|
||||
> from spacy_transformers.annotation_setters import null_annotation_setter
|
||||
> from spacy_transformers.span_getters import get_doc_spans
|
||||
>
|
||||
> trf = Transformer(
|
||||
> nlp.vocab,
|
||||
> TransformerModel(
|
||||
> "bert-base-cased",
|
||||
> get_spans=get_doc_spans,
|
||||
> tokenizer_config={"use_fast": True},
|
||||
> ),
|
||||
> annotation_setter=null_annotation_setter,
|
||||
> max_batch_items=4096,
|
||||
> )
|
||||
> ```
|
||||
|
||||
```ini
|
||||
[nlp]
|
||||
lang = "en"
|
||||
pipeline = ["transformer"]
|
||||
|
||||
### config.cfg (excerpt)
|
||||
[components.transformer]
|
||||
factory = "transformer"
|
||||
extra_annotation_setter = null
|
||||
max_batch_size = 32
|
||||
max_batch_items = 4096
|
||||
|
||||
[components.transformer.model]
|
||||
@architectures = "spacy-transformers.TransformerModel.v1"
|
||||
|
|
@ -50,46 +133,110 @@ name = "bert-base-cased"
|
|||
tokenizer_config = {"use_fast": true}
|
||||
|
||||
[components.transformer.model.get_spans]
|
||||
@span_getters = "get_doc_spans.v1"
|
||||
@span_getters = "doc_spans.v1"
|
||||
|
||||
[components.transformer.annotation_setter]
|
||||
@annotation_setters = "spacy-transformer.null_annotation_setter.v1"
|
||||
|
||||
```
|
||||
|
||||
The `[components.transformer.model]` block describes the `model` argument passed
|
||||
to the transformer component. It's a Thinc
|
||||
[`Model`](https://thinc.ai/docs/api-model) object that will be passed into the
|
||||
component. Here, it references the function
|
||||
[spacy-transformers.TransformerModel.v1](/api/architectures#TransformerModel)
|
||||
registered in the [`architectures` registry](/api/top-level#registry). If a key
|
||||
in a block starts with `@`, it's **resolved to a function** and all other
|
||||
settings are passed to the function as arguments. In this case, `name`,
|
||||
`tokenizer_config` and `get_spans`.
|
||||
|
||||
`get_spans` is a function that takes a batch of `Doc` object and returns lists
|
||||
of potentially overlapping `Span` objects to process by the transformer. Several
|
||||
[built-in functions](/api/transformer#span-getters) are available – for example,
|
||||
to process the whole document or individual sentences. When the config is
|
||||
resolved, the function is created and passed into the model as an argument.
|
||||
|
||||
<Infobox variant="warning">
|
||||
|
||||
Remember that the `config.cfg` used for training should contain **no missing
|
||||
values** and requires all settings to be defined. You don't want any hidden
|
||||
defaults creeping in and changing your results! spaCy will tell you if settings
|
||||
are missing, and you can run [`spacy debug config`](/api/cli#debug-config) with
|
||||
`--auto-fill` to automatically fill in all defaults.
|
||||
|
||||
<!-- TODO: update with details on getting started with a config -->
|
||||
|
||||
</Infobox>
|
||||
|
||||
### Customizing the settings {#training-custom-settings}
|
||||
|
||||
To change any of the settings, you can edit the `config.cfg` and re-run the
|
||||
training. To change any of the functions, like the span getter, you can replace
|
||||
the name of the referenced function – e.g. `@span_getters = "sent_spans.v1"` to
|
||||
process sentences. You can also register your own functions using the
|
||||
`span_getters` registry:
|
||||
|
||||
> #### config.cfg
|
||||
>
|
||||
> ```ini
|
||||
> [components.transformer.model.get_spans]
|
||||
> @span_getters = "custom_sent_spans"
|
||||
> ```
|
||||
|
||||
```python
|
||||
from spacy_transformers import Transformer
|
||||
### code.py
|
||||
import spacy_transformers
|
||||
|
||||
trf = Transformer(
|
||||
nlp.vocab,
|
||||
TransformerModel(
|
||||
"bert-base-cased",
|
||||
get_spans=get_doc_spans,
|
||||
tokenizer_config={"use_fast": True},
|
||||
),
|
||||
annotation_setter=null_annotation_setter,
|
||||
max_batch_size=32,
|
||||
)
|
||||
@spacy_transformers.registry.span_getters("custom_sent_spans")
|
||||
def configure_custom_sent_spans():
|
||||
# TODO: write custom example
|
||||
def get_sent_spans(docs):
|
||||
return [list(doc.sents) for doc in docs]
|
||||
|
||||
return get_sent_spans
|
||||
```
|
||||
|
||||
The `components.transformer` block adds the `transformer` component to the
|
||||
pipeline, and the `components.transformer.model` block describes the creation of
|
||||
a Thinc [`Model`](https://thinc.ai/docs/api-model) object that will be passed
|
||||
into the component. The block names a function registered in the
|
||||
`@architectures` registry. This function will be looked up and called using the
|
||||
provided arguments. You're not limited to just that function --- you can write
|
||||
your own or use someone else's. The only limitation is that it must return an
|
||||
object of type `Model[List[Doc], FullTransformerBatch]`: that is, a Thinc model
|
||||
that takes a list of `Doc` objects, and returns a `FullTransformerBatch` object
|
||||
with the transformer data.
|
||||
To resolve the config during training, spaCy needs to know about your custom
|
||||
function. You can make it available via the `--code` argument that can point to
|
||||
a Python file:
|
||||
|
||||
The same idea applies to task models that power the downstream components. Most
|
||||
of spaCy's built-in model creation functions support a `tok2vec` argument, which
|
||||
should be a Thinc layer of type `Model[List[Doc], List[Floats2d]]`. This is
|
||||
where we'll plug in our transformer model, using the `Tok2VecTransformer` layer,
|
||||
which sneakily delegates to the `Transformer` pipeline component.
|
||||
```bash
|
||||
$ python -m spacy train ./train.spacy ./dev.spacy ./config.cfg --code ./code.py
|
||||
```
|
||||
|
||||
### Customizing the model implementations {#training-custom-model}
|
||||
|
||||
The [`Transformer`](/api/transformer) component expects a Thinc
|
||||
[`Model`](https://thinc.ai/docs/api-model) object to be passed in as its `model`
|
||||
argument. You're not limited to the implementation provided by
|
||||
`spacy-transformers` – the only requirement is that your registered function
|
||||
must return an object of type `Model[List[Doc], FullTransformerBatch]`: that is,
|
||||
a Thinc model that takes a list of [`Doc`](/api/doc) objects, and returns a
|
||||
[`FullTransformerBatch`](/api/transformer#fulltransformerbatch) object with the
|
||||
transformer data.
|
||||
|
||||
> #### Model type annotations
|
||||
>
|
||||
> In the documentation and code base, you may come across type annotations and
|
||||
> descriptions of [Thinc](https://thinc.ai) model types, like
|
||||
> `Model[List[Doc], List[Floats2d]]`. This so-called generic type describes the
|
||||
> layer and its input and output type – in this case, it takes a list of `Doc`
|
||||
> objects as the input and list of 2-dimensional arrays of floats as the output.
|
||||
> You can read more about defining Thinc
|
||||
> models [here](https://thinc.ai/docs/usage-models). Also see the
|
||||
> [type checking](https://thinc.ai/docs/usage-type-checking) for how to enable
|
||||
> linting in your editor to see live feedback if your inputs and outputs don't
|
||||
> match.
|
||||
|
||||
The same idea applies to task models that power the **downstream components**.
|
||||
Most of spaCy's built-in model creation functions support a `tok2vec` argument,
|
||||
which should be a Thinc layer of type `Model[List[Doc], List[Floats2d]]`. This
|
||||
is where we'll plug in our transformer model, using the
|
||||
[Tok2VecListener](/api/architectures#Tok2VecListener) layer, which sneakily
|
||||
delegates to the `Transformer` pipeline component.
|
||||
|
||||
```ini
|
||||
[nlp]
|
||||
lang = "en"
|
||||
pipeline = ["ner"]
|
||||
|
||||
### config.cfg (excerpt) {highlight="12"}
|
||||
[components.ner]
|
||||
factory = "ner"
|
||||
|
||||
|
|
@ -108,49 +255,24 @@ grad_factor = 1.0
|
|||
@layers = "reduce_mean.v1"
|
||||
```
|
||||
|
||||
The `Tok2VecListener` layer expects a `pooling` layer, which needs to be of type
|
||||
`Model[Ragged, Floats2d]`. This layer determines how the vector for each spaCy
|
||||
token will be computed from the zero or more source rows the token is aligned
|
||||
against. Here we use the `reduce_mean` layer, which averages the wordpiece rows.
|
||||
We could instead use `reduce_last`, `reduce_max`, or a custom function you write
|
||||
yourself.
|
||||
The [Tok2VecListener](/api/architectures#Tok2VecListener) layer expects a
|
||||
[pooling layer](https://thinc.ai/docs/api-layers#reduction-ops), which needs to
|
||||
be of type `Model[Ragged, Floats2d]`. This layer determines how the vector for
|
||||
each spaCy token will be computed from the zero or more source rows the token is
|
||||
aligned against. Here we use the
|
||||
[`reduce_mean`](https://thinc.ai/docs/api-layers#reduce_mean) layer, which
|
||||
averages the wordpiece rows. We could instead use `reduce_last`,
|
||||
[`reduce_max`](https://thinc.ai/docs/api-layers#reduce_max), or a custom
|
||||
function you write yourself.
|
||||
|
||||
<!--TODO: reduce_last: undocumented? -->
|
||||
|
||||
You can have multiple components all listening to the same transformer model,
|
||||
and all passing gradients back to it. By default, all of the gradients will be
|
||||
equally weighted. You can control this with the `grad_factor` setting, which
|
||||
**equally weighted**. You can control this with the `grad_factor` setting, which
|
||||
lets you reweight the gradients from the different listeners. For instance,
|
||||
setting `grad_factor = 0` would disable gradients from one of the listeners,
|
||||
while `grad_factor = 2.0` would multiply them by 2. This is similar to having a
|
||||
custom learning rate for each component. Instead of a constant, you can also
|
||||
provide a schedule, allowing you to freeze the shared parameters at the start of
|
||||
training.
|
||||
|
||||
### Runtime usage
|
||||
|
||||
Transformer models can be used as drop-in replacements for other types of neural
|
||||
networks, so your spaCy pipeline can include them in a way that's completely
|
||||
invisible to the user. Users will download, load and use the model in the
|
||||
standard way, like any other spaCy pipeline.
|
||||
|
||||
Instead of using the transformers as subnetworks directly, you can also use them
|
||||
via the [`Transformer`](/api/transformer) pipeline component. This sets the
|
||||
[`Doc._.trf_data`](/api/transformer#custom_attributes) extension attribute,
|
||||
which lets you access the transformers outputs at runtime via the
|
||||
`doc._.trf_data` extension attribute. You can also customize how the
|
||||
`Transformer` object sets annotations onto the `Doc`, by customizing the
|
||||
`Transformer.annotation_setter` object. This callback will be called with the
|
||||
raw input and output data for the whole batch, along with the batch of `Doc`
|
||||
objects, allowing you to implement whatever you need.
|
||||
|
||||
```python
|
||||
import spacy
|
||||
|
||||
nlp = spacy.load("en_core_trf_lg")
|
||||
for doc in nlp.pipe(["some text", "some other text"]):
|
||||
doc._.trf_data.tensors
|
||||
tokvecs = doc._.trf_data.tensors[-1]
|
||||
```
|
||||
|
||||
The `nlp` object in this example is just like any other spaCy pipeline
|
||||
|
||||
-->
|
||||
|
|
|
|||
Loading…
Reference in New Issue
Block a user