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Update docs [ci skip]
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Ines Montani
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@ -11,9 +11,17 @@ menu:
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- ['Entity Linking', 'entitylinker']
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---
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TODO: intro and how architectures work, link to
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[`registry`](/api/top-level#registry),
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[custom functions](/usage/training#custom-functions) usage etc.
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A **model architecture** is a function that wires up a
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[`Model`](https://thinc.ai/docs/api-model) instance, which you can then use in a
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pipeline component or as a layer of a larger network. This page documents
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spaCy's built-in architectures that are used for different NLP tasks. All
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trainable [built-in components](/api#architecture-pipeline) expect a `model`
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argument defined in the config and document their the default architecture.
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Custom architectures can be registered using the
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[`@spacy.registry.architectures`](/api/top-level#regsitry) decorator and used as
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part of the [training config](/usage/training#custom-functions). Also see the
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usage documentation on
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[layers and model architectures](/usage/layers-architectures).
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## Tok2Vec architectures {#tok2vec-arch source="spacy/ml/models/tok2vec.py"}
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@ -284,8 +292,18 @@ on [static vectors](/usage/embeddings-transformers#static-vectors) for details.
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The following architectures are provided by the package
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[`spacy-transformers`](https://github.com/explosion/spacy-transformers). See the
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[usage documentation](/usage/embeddings-transformers) for how to integrate the
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architectures into your training config.
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[usage documentation](/usage/embeddings-transformers#transformers) for how to
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integrate the architectures into your training config.
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<Infobox variant="warning">
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Note that in order to use these architectures in your config, you need to
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install the
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[`spacy-transformers`](https://github.com/explosion/spacy-transformers). See the
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[installation docs](/usage/embeddings-transformers#transformers-installation)
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for details and system requirements.
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</Infobox>
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### spacy-transformers.TransformerModel.v1 {#TransformerModel}
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@ -9,7 +9,7 @@ menu:
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next: /usage/projects
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---
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A **model architecture** is a function that wires up a
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A **model architecture** is a function that wires up a
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[Thinc `Model`](https://thinc.ai/docs/api-model) instance, which you can then
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use in a component or as a layer of a larger network. You can use Thinc as a
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thin wrapper around frameworks such as PyTorch, TensorFlow or MXNet, or you can
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@ -6,8 +6,7 @@ menu:
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- ['Quickstart', 'quickstart']
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- ['Config System', 'config']
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- ['Custom Functions', 'custom-functions']
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- ['Transfer Learning', 'transfer-learning']
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- ['Parallel Training', 'parallel-training']
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# - ['Parallel Training', 'parallel-training']
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- ['Internal API', 'api']
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---
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@ -92,16 +91,6 @@ spaCy's binary `.spacy` format. You can either include the data paths in the
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$ python -m spacy train config.cfg --output ./output --paths.train ./train.spacy --paths.dev ./dev.spacy
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```
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<!-- TODO:
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<Project id="some_example_project">
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The easiest way to get started with an end-to-end training process is to clone a
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[project](/usage/projects) template. Projects let you manage multi-step
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workflows, from data preprocessing to training and packaging your model.
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</Project>
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-->
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## Training config {#config}
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Training config files include all **settings and hyperparameters** for training
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@ -400,13 +389,11 @@ recipe once the dish has already been prepared. You have to make a new one.
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spaCy includes a variety of built-in [architectures](/api/architectures) for
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different tasks. For example:
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<!-- TODO: model return types -->
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| Architecture | Description |
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| ----------------------------------------------------------------- | --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
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| [HashEmbedCNN](/api/architectures#HashEmbedCNN) | Build spaCy’s "standard" embedding layer, which uses hash embedding with subword features and a CNN with layer-normalized maxout. ~~Model[List[Doc], List[Floats2d]]~~ |
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| [TransitionBasedParser](/api/architectures#TransitionBasedParser) | Build a [transition-based parser](https://explosion.ai/blog/parsing-english-in-python) model used in the default [`EntityRecognizer`](/api/entityrecognizer) and [`DependencyParser`](/api/dependencyparser). ~~Model[List[Docs], List[List[Floats2d]]]~~ |
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| [TextCatEnsemble](/api/architectures#TextCatEnsemble) | Stacked ensemble of a bag-of-words model and a neural network model with an internal CNN embedding layer. Used in the default [`TextCategorizer`](/api/textcategorizer). ~~Model~~ |
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| [TextCatEnsemble](/api/architectures#TextCatEnsemble) | Stacked ensemble of a bag-of-words model and a neural network model with an internal CNN embedding layer. Used in the default [`TextCategorizer`](/api/textcategorizer). ~~Model[List[Doc], Floats2d]~~ |
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<!-- TODO: link to not yet existing usage page on custom architectures etc. -->
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@ -755,71 +742,10 @@ def filter_batch(size: int) -> Callable[[Iterable[Example]], Iterator[List[Examp
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return create_filtered_batches
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```
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<!-- TODO:
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<Project id="example_pytorch_model">
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Lorem ipsum dolor sit amet, consectetur adipiscing elit. Phasellus interdum
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sodales lectus, ut sodales orci ullamcorper id. Sed condimentum neque ut erat
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mattis pretium.
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</Project>
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-->
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### Defining custom architectures {#custom-architectures}
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<!-- TODO: this should probably move to new section on models -->
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## Transfer learning {#transfer-learning}
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<!-- TODO: write something, link to embeddings and transformers page – should probably wait until transformers/embeddings/transfer learning docs are done -->
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### Using transformer models like BERT {#transformers}
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spaCy v3.0 lets you use almost any statistical model to power your pipeline. You
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can use models implemented in a variety of frameworks. A transformer model is
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just a statistical model, so the
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[`spacy-transformers`](https://github.com/explosion/spacy-transformers) package
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actually has very little work to do: it just has to provide a few functions that
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do the required plumbing. It also provides a pipeline component,
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[`Transformer`](/api/transformer), that lets you do multi-task learning and lets
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you save the transformer outputs for later use.
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<!-- TODO:
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<Project id="en_core_trf_lg">
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Try out a BERT-based model pipeline using this project template: swap in your
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data, edit the settings and hyperparameters and train, evaluate, package and
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visualize your model.
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</Project>
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-->
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For more details on how to integrate transformer models into your training
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config and customize the implementations, see the usage guide on
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[training transformers](/usage/embeddings-transformers#transformers-training).
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### Pretraining with spaCy {#pretraining}
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<!-- TODO: document spacy pretrain, objectives etc. – should probably wait until transformers/embeddings/transfer learning docs are done -->
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## Parallel Training with Ray {#parallel-training}
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<!-- TODO:
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<Project id="some_example_project">
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Lorem ipsum dolor sit amet, consectetur adipiscing elit. Phasellus interdum
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sodales lectus, ut sodales orci ullamcorper id. Sed condimentum neque ut erat
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mattis pretium.
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</Project>
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-->
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## Internal training API {#api}
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<Infobox variant="warning">
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@ -880,8 +806,8 @@ example = Example.from_dict(predicted, {"tags": tags})
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Here's another example that shows how to define gold-standard named entities.
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The letters added before the labels refer to the tags of the
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[BILUO scheme](/usage/linguistic-features#updating-biluo) – `O` is a token
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outside an entity, `U` a single entity unit, `B` the beginning of an entity,
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`I` a token inside an entity and `L` the last token of an entity.
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outside an entity, `U` a single entity unit, `B` the beginning of an entity, `I`
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a token inside an entity and `L` the last token of an entity.
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```python
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doc = Doc(nlp.vocab, words=["Facebook", "released", "React", "in", "2014"])
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@ -363,7 +363,7 @@ body [id]:target
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color: var(--color-red-medium)
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background: var(--color-red-transparent)
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&.italic, &.comment
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&.italic
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font-style: italic
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@ -384,11 +384,9 @@ body [id]:target
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// Settings for ini syntax (config files)
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[class*="language-ini"]
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color: var(--syntax-comment)
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font-style: italic !important
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.token
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color: var(--color-subtle)
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font-style: normal !important
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.gatsby-highlight-code-line
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@ -426,7 +424,6 @@ body [id]:target
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.cm-comment
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color: var(--syntax-comment)
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font-style: italic
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.cm-keyword
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color: var(--syntax-keyword)
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