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---
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title: Layers and Model Architectures
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teaser: Power spaCy components with custom neural networks
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menu:
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- ['Type Signatures', 'type-sigs']
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- ['Swapping Architectures', 'swap-architectures']
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- ['PyTorch & TensorFlow', 'frameworks']
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- ['Custom Thinc Models', 'thinc']
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- ['Trainable Components', 'components']
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next: /usage/projects
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---
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> #### Example
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>
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> ```python
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> from thinc.api import Model, chain
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>
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> @spacy.registry.architectures("model.v1")
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> def build_model(width: int, classes: int) -> Model:
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> tok2vec = build_tok2vec(width)
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> output_layer = build_output_layer(width, classes)
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> model = chain(tok2vec, output_layer)
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> return model
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> ```
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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. It describes the
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neural network that is run internally as part of a component in a spaCy
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pipeline. To define the actual architecture, you can implement your logic in
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Thinc directly, or you can use Thinc as a thin wrapper around frameworks such as
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PyTorch, TensorFlow and MXNet. Each `Model` can also be used as a sublayer of a
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larger network, allowing you to freely combine implementations from different
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frameworks into a single model.
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spaCy's built-in components require a `Model` instance to be passed to them via
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the config system. To change the model architecture of an existing component,
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you just need to [**update the config**](#swap-architectures) so that it refers
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to a different registered function. Once the component has been created from
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this config, you won't be able to change it anymore. The architecture is like a
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recipe for the network, and you can't change the recipe once the dish has
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already been prepared. You have to make a new one.
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```ini {title="config.cfg (excerpt)"}
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[components.tagger]
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factory = "tagger"
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[components.tagger.model]
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@architectures = "model.v1"
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width = 512
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classes = 16
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```
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## Type signatures {id="type-sigs"}
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> #### Example
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>
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> ```python
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> from typing import List
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> from thinc.api import Model, chain
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> from thinc.types import Floats2d
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> def chain_model(
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> tok2vec: Model[List[Doc], List[Floats2d]],
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> layer1: Model[List[Floats2d], Floats2d],
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> layer2: Model[Floats2d, Floats2d]
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> ) -> Model[List[Doc], Floats2d]:
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> model = chain(tok2vec, layer1, layer2)
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> return model
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> ```
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The Thinc `Model` class is a **generic type** that can specify its input and
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output types. Python uses a square-bracket notation for this, so the type
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~~Model[List, Dict]~~ says that each batch of inputs to the model will be a
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list, and the outputs will be a dictionary. You can be even more specific and
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write for instance~~Model[List[Doc], Dict[str, float]]~~ to specify that the
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model expects a list of [`Doc`](/api/doc) objects as input, and returns a
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dictionary mapping of strings to floats. Some of the most common types you'll
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see are:
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| Type | Description |
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| ------------------ | ---------------------------------------------------------------------------------------------------- |
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| ~~List[Doc]~~ | A batch of [`Doc`](/api/doc) objects. Most components expect their models to take this as input. |
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| ~~Floats2d~~ | A two-dimensional `numpy` or `cupy` array of floats. Usually 32-bit. |
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| ~~Ints2d~~ | A two-dimensional `numpy` or `cupy` array of integers. Common dtypes include uint64, int32 and int8. |
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| ~~List[Floats2d]~~ | A list of two-dimensional arrays, generally with one array per `Doc` and one row per token. |
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| ~~Ragged~~ | A container to handle variable-length sequence data in an unpadded contiguous array. |
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| ~~Padded~~ | A container to handle variable-length sequence data in a padded contiguous array. |
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See the [Thinc type reference](https://thinc.ai/docs/api-types) for details. The
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model type signatures help you figure out which model architectures and
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components can **fit together**. For instance, the
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[`TextCategorizer`](/api/textcategorizer) class expects a model typed
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~~Model[List[Doc], Floats2d]~~, because the model will predict one row of
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category probabilities per [`Doc`](/api/doc). In contrast, the
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[`Tagger`](/api/tagger) class expects a model typed ~~Model[List[Doc],
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List[Floats2d]]~~, because it needs to predict one row of probabilities per
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token.
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There's no guarantee that two models with the same type signature can be used
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interchangeably. There are many other ways they could be incompatible. However,
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if the types don't match, they almost surely _won't_ be compatible. This little
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bit of validation goes a long way, especially if you
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[configure your editor](https://thinc.ai/docs/usage-type-checking) or other
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tools to highlight these errors early. The config file is also validated at the
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beginning of training, to verify that all the types match correctly.
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<Accordion title="Tip: Static type checking in your editor">
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If you're using a modern editor like Visual Studio Code, you can
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[set up `mypy`](https://thinc.ai/docs/usage-type-checking#install) with the
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custom Thinc plugin and get live feedback about mismatched types as you write
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code.
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<Image
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src="/images/thinc_mypy.jpg"
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href="https://thinc.ai/docs/usage-type-checking#linting"
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alt="Screenshot of Thinc type checking in VSCode with mypy"
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/>
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</Accordion>
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## Swapping model architectures {id="swap-architectures"}
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If no model is specified for the [`TextCategorizer`](/api/textcategorizer), the
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[TextCatEnsemble](/api/architectures#TextCatEnsemble) architecture is used by
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default. This architecture combines a simple bag-of-words model with a neural
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network, usually resulting in the most accurate results, but at the cost of
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speed. The config file for this model would look something like this:
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```ini {title="config.cfg (excerpt)"}
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[components.textcat]
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factory = "textcat"
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labels = []
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[components.textcat.model]
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@architectures = "spacy.TextCatEnsemble.v2"
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nO = null
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[components.textcat.model.tok2vec]
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@architectures = "spacy.Tok2Vec.v2"
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[components.textcat.model.tok2vec.embed]
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@architectures = "spacy.MultiHashEmbed.v2"
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width = 64
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rows = [2000, 2000, 1000, 1000, 1000, 1000]
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attrs = ["ORTH", "LOWER", "PREFIX", "SUFFIX", "SHAPE", "ID"]
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include_static_vectors = false
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[components.textcat.model.tok2vec.encode]
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@architectures = "spacy.MaxoutWindowEncoder.v2"
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width = ${components.textcat.model.tok2vec.embed.width}
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window_size = 1
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maxout_pieces = 3
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depth = 2
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[components.textcat.model.linear_model]
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@architectures = "spacy.TextCatBOW.v2"
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exclusive_classes = true
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ngram_size = 1
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no_output_layer = false
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```
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spaCy has two additional built-in `textcat` architectures, and you can easily
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use those by swapping out the definition of the textcat's model. For instance,
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to use the simple and fast bag-of-words model
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[TextCatBOW](/api/architectures#TextCatBOW), you can change the config to:
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```ini {title="config.cfg (excerpt)",highlight="6-10"}
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[components.textcat]
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factory = "textcat"
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labels = []
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[components.textcat.model]
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@architectures = "spacy.TextCatBOW.v2"
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exclusive_classes = true
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ngram_size = 1
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no_output_layer = false
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nO = null
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```
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For details on all pre-defined architectures shipped with spaCy and how to
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configure them, check out the [model architectures](/api/architectures)
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documentation.
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### Defining sublayers {id="sublayers"}
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Model architecture functions often accept **sublayers as arguments**, so that
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you can try **substituting a different layer** into the network. Depending on
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how the architecture function is structured, you might be able to define your
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network structure entirely through the [config system](/usage/training#config),
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using layers that have already been defined.
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In most neural network models for NLP, the most important parts of the network
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are what we refer to as the
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[embed and encode](https://explosion.ai/blog/deep-learning-formula-nlp) steps.
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These steps together compute dense, context-sensitive representations of the
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tokens, and their combination forms a typical
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[`Tok2Vec`](/api/architectures#Tok2Vec) layer:
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```ini {title="config.cfg (excerpt)"}
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[components.tok2vec]
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factory = "tok2vec"
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[components.tok2vec.model]
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@architectures = "spacy.Tok2Vec.v2"
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[components.tok2vec.model.embed]
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@architectures = "spacy.MultiHashEmbed.v2"
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# ...
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[components.tok2vec.model.encode]
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@architectures = "spacy.MaxoutWindowEncoder.v2"
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# ...
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```
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By defining these sublayers specifically, it becomes straightforward to swap out
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a sublayer for another one, for instance changing the first sublayer to a
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character embedding with the [CharacterEmbed](/api/architectures#CharacterEmbed)
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architecture:
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```ini {title="config.cfg (excerpt)"}
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[components.tok2vec.model.embed]
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@architectures = "spacy.CharacterEmbed.v2"
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# ...
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[components.tok2vec.model.encode]
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@architectures = "spacy.MaxoutWindowEncoder.v2"
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# ...
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```
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Most of spaCy's default architectures accept a `tok2vec` layer as a sublayer
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within the larger task-specific neural network. This makes it easy to **switch
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between** transformer, CNN, BiLSTM or other feature extraction approaches. The
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[transformers documentation](/usage/embeddings-transformers#training-custom-model)
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section shows an example of swapping out a model's standard `tok2vec` layer with
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a transformer. And if you want to define your own solution, all you need to do
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is register a ~~Model[List[Doc], List[Floats2d]]~~ architecture function, and
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you'll be able to try it out in any of the spaCy components.
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## Wrapping PyTorch, TensorFlow and other frameworks {id="frameworks"}
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Thinc allows you to [wrap models](https://thinc.ai/docs/usage-frameworks)
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written in other machine learning frameworks like PyTorch, TensorFlow and MXNet
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using a unified [`Model`](https://thinc.ai/docs/api-model) API. This makes it
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easy to use a model implemented in a different framework to power a component in
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your spaCy pipeline. For example, to wrap a PyTorch model as a Thinc `Model`,
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you can use Thinc's
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[`PyTorchWrapper`](https://thinc.ai/docs/api-layers#pytorchwrapper):
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```python
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from thinc.api import PyTorchWrapper
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wrapped_pt_model = PyTorchWrapper(torch_model)
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```
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Let's use PyTorch to define a very simple neural network consisting of two
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hidden `Linear` layers with `ReLU` activation and dropout, and a
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softmax-activated output layer:
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```python {title="PyTorch model"}
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from torch import nn
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torch_model = nn.Sequential(
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nn.Linear(width, hidden_width),
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nn.ReLU(),
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nn.Dropout2d(dropout),
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nn.Linear(hidden_width, nO),
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nn.ReLU(),
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nn.Dropout2d(dropout),
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nn.Softmax(dim=1)
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)
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```
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The resulting wrapped `Model` can be used as a **custom architecture** as such,
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or can be a **subcomponent of a larger model**. For instance, we can use Thinc's
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[`chain`](https://thinc.ai/docs/api-layers#chain) combinator, which works like
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`Sequential` in PyTorch, to combine the wrapped model with other components in a
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larger network. This effectively means that you can easily wrap different
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components from different frameworks, and "glue" them together with Thinc:
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```python
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from thinc.api import chain, with_array, PyTorchWrapper
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from spacy.ml import CharacterEmbed
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wrapped_pt_model = PyTorchWrapper(torch_model)
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char_embed = CharacterEmbed(width, embed_size, nM, nC)
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model = chain(char_embed, with_array(wrapped_pt_model))
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```
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In the above example, we have combined our custom PyTorch model with a character
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embedding layer defined by spaCy.
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[CharacterEmbed](/api/architectures#CharacterEmbed) returns a `Model` that takes
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a ~~List[Doc]~~ as input, and outputs a ~~List[Floats2d]~~. To make sure that
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the wrapped PyTorch model receives valid inputs, we use Thinc's
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[`with_array`](https://thinc.ai/docs/api-layers#with_array) helper.
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You could also implement a model that only uses PyTorch for the transformer
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layers, and "native" Thinc layers to do fiddly input and output transformations
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and add on task-specific "heads", as efficiency is less of a consideration for
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those parts of the network.
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### Using wrapped models {id="frameworks-usage"}
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To use our custom model including the PyTorch subnetwork, all we need to do is
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register the architecture using the
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[`architectures` registry](/api/top-level#registry). This assigns the
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architecture a name so spaCy knows how to find it, and allows passing in
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arguments like hyperparameters via the [config](/usage/training#config). The
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full example then becomes:
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```python {title="Registering the architecture",highlight="9"}
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from typing import List
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from thinc.types import Floats2d
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from thinc.api import Model, PyTorchWrapper, chain, with_array
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import spacy
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from spacy.tokens.doc import Doc
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from spacy.ml import CharacterEmbed
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from torch import nn
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@spacy.registry.architectures("CustomTorchModel.v1")
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def create_torch_model(
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nO: int,
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width: int,
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hidden_width: int,
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embed_size: int,
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nM: int,
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nC: int,
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dropout: float,
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) -> Model[List[Doc], List[Floats2d]]:
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char_embed = CharacterEmbed(width, embed_size, nM, nC)
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torch_model = nn.Sequential(
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nn.Linear(width, hidden_width),
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nn.ReLU(),
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nn.Dropout2d(dropout),
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nn.Linear(hidden_width, nO),
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nn.ReLU(),
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nn.Dropout2d(dropout),
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nn.Softmax(dim=1)
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)
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wrapped_pt_model = PyTorchWrapper(torch_model)
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model = chain(char_embed, with_array(wrapped_pt_model))
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return model
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```
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The model definition can now be used in any existing trainable spaCy component,
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by specifying it in the config file. In this configuration, all required
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parameters for the various subcomponents of the custom architecture are passed
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in as settings via the config.
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```ini {title="config.cfg (excerpt)",highlight="5-5"}
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[components.tagger]
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factory = "tagger"
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[components.tagger.model]
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@architectures = "CustomTorchModel.v1"
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nO = 50
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width = 96
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hidden_width = 48
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embed_size = 2000
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nM = 64
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nC = 8
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dropout = 0.2
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```
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<Infobox variant="warning">
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Remember that it is best not to rely on any (hidden) default values to ensure
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that training configs are complete and experiments fully reproducible.
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</Infobox>
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Note that when using a PyTorch or Tensorflow model, it is recommended to set the
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GPU memory allocator accordingly. When `gpu_allocator` is set to "pytorch" or
|
||
"tensorflow" in the training config, cupy will allocate memory via those
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respective libraries, preventing OOM errors when there's available memory
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||
sitting in the other library's pool.
|
||
|
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```ini {title="config.cfg (excerpt)"}
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[training]
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gpu_allocator = "pytorch"
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```
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## Custom models with Thinc {id="thinc"}
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Of course it's also possible to define the `Model` from the previous section
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entirely in Thinc. The Thinc documentation provides details on the
|
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[various layers](https://thinc.ai/docs/api-layers) and helper functions
|
||
available. Combinators can be used to
|
||
[overload operators](https://thinc.ai/docs/usage-models#operators) and a common
|
||
usage pattern is to bind `chain` to `>>`. The "native" Thinc version of our
|
||
simple neural network would then become:
|
||
|
||
```python
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||
from thinc.api import chain, with_array, Model, Relu, Dropout, Softmax
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from spacy.ml import CharacterEmbed
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|
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char_embed = CharacterEmbed(width, embed_size, nM, nC)
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with Model.define_operators({">>": chain}):
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layers = (
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Relu(hidden_width, width)
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>> Dropout(dropout)
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>> Relu(hidden_width, hidden_width)
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>> Dropout(dropout)
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>> Softmax(nO, hidden_width)
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)
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model = char_embed >> with_array(layers)
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```
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||
|
||
<Infobox variant="warning" title="Important note on inputs and outputs">
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||
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||
Note that Thinc layers define the output dimension (`nO`) as the first argument,
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||
followed (optionally) by the input dimension (`nI`). This is in contrast to how
|
||
the PyTorch layers are defined, where `in_features` precedes `out_features`.
|
||
|
||
</Infobox>
|
||
|
||
### Shape inference in Thinc {id="thinc-shape-inference"}
|
||
|
||
It is **not** strictly necessary to define all the input and output dimensions
|
||
for each layer, as Thinc can perform
|
||
[shape inference](https://thinc.ai/docs/usage-models#validation) between
|
||
sequential layers by matching up the output dimensionality of one layer to the
|
||
input dimensionality of the next. This means that we can simplify the `layers`
|
||
definition:
|
||
|
||
> #### Diff
|
||
>
|
||
> ```diff
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||
> layers = (
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||
> Relu(hidden_width, width)
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> >> Dropout(dropout)
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> - >> Relu(hidden_width, hidden_width)
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> + >> Relu(hidden_width)
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> >> Dropout(dropout)
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> - >> Softmax(nO, hidden_width)
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> + >> Softmax(nO)
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> )
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||
> ```
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||
|
||
```python
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with Model.define_operators({">>": chain}):
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||
layers = (
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Relu(hidden_width, width)
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>> Dropout(dropout)
|
||
>> Relu(hidden_width)
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||
>> Dropout(dropout)
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||
>> Softmax(nO)
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||
)
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||
```
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||
|
||
Thinc can even go one step further and **deduce the correct input dimension** of
|
||
the first layer, and output dimension of the last. To enable this functionality,
|
||
you have to call
|
||
[`Model.initialize`](https://thinc.ai/docs/api-model#initialize) with an **input
|
||
sample** `X` and an **output sample** `Y` with the correct dimensions:
|
||
|
||
```python {title="Shape inference with initialization",highlight="3,7,10"}
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||
with Model.define_operators({">>": chain}):
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||
layers = (
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||
Relu(hidden_width)
|
||
>> Dropout(dropout)
|
||
>> Relu(hidden_width)
|
||
>> Dropout(dropout)
|
||
>> Softmax()
|
||
)
|
||
model = char_embed >> with_array(layers)
|
||
model.initialize(X=input_sample, Y=output_sample)
|
||
```
|
||
|
||
The built-in [pipeline components](/usage/processing-pipelines) in spaCy ensure
|
||
that their internal models are **always initialized** with appropriate sample
|
||
data. In this case, `X` is typically a ~~List[Doc]~~, while `Y` is typically a
|
||
~~List[Array1d]~~ or ~~List[Array2d]~~, depending on the specific task. This
|
||
functionality is triggered when [`nlp.initialize`](/api/language#initialize) is
|
||
called.
|
||
|
||
### Dropout and normalization in Thinc {id="thinc-dropout-norm"}
|
||
|
||
Many of the available Thinc [layers](https://thinc.ai/docs/api-layers) allow you
|
||
to define a `dropout` argument that will result in "chaining" an additional
|
||
[`Dropout`](https://thinc.ai/docs/api-layers#dropout) layer. Optionally, you can
|
||
often specify whether or not you want to add layer normalization, which would
|
||
result in an additional
|
||
[`LayerNorm`](https://thinc.ai/docs/api-layers#layernorm) layer. That means that
|
||
the following `layers` definition is equivalent to the previous:
|
||
|
||
```python
|
||
with Model.define_operators({">>": chain}):
|
||
layers = (
|
||
Relu(hidden_width, dropout=dropout, normalize=False)
|
||
>> Relu(hidden_width, dropout=dropout, normalize=False)
|
||
>> Softmax()
|
||
)
|
||
model = char_embed >> with_array(layers)
|
||
model.initialize(X=input_sample, Y=output_sample)
|
||
```
|
||
|
||
## Create new trainable components {id="components"}
|
||
|
||
In addition to [swapping out](#swap-architectures) layers in existing
|
||
components, you can also implement an entirely new,
|
||
[trainable](/usage/processing-pipelines#trainable-components) pipeline component
|
||
from scratch. This can be done by creating a new class inheriting from
|
||
[`TrainablePipe`](/api/pipe), and linking it up to your custom model
|
||
implementation.
|
||
|
||
<Infobox title="Trainable component API" emoji="💡">
|
||
|
||
For details on how to implement pipeline components, check out the usage guide
|
||
on [custom components](/usage/processing-pipelines#custom-component) and the
|
||
overview of the `TrainablePipe` methods used by
|
||
[trainable components](/usage/processing-pipelines#trainable-components).
|
||
|
||
</Infobox>
|
||
|
||
### Example: Entity relation extraction component {id="component-rel"}
|
||
|
||
This section outlines an example use-case of implementing a **novel relation
|
||
extraction component** from scratch. We'll implement a binary relation
|
||
extraction method that determines whether or not **two entities** in a document
|
||
are related, and if so, what type of relation connects them. We allow multiple
|
||
types of relations between two such entities (a multi-label setting). There are
|
||
two major steps required:
|
||
|
||
1. Implement a [machine learning model](#component-rel-model) specific to this
|
||
task. It will have to extract candidate relation instances from a
|
||
[`Doc`](/api/doc) and predict the corresponding scores for each relation
|
||
label.
|
||
2. Implement a custom [pipeline component](#component-rel-pipe) - powered by the
|
||
machine learning model from step 1 - that translates the predicted scores
|
||
into annotations that are stored on the [`Doc`](/api/doc) objects as they
|
||
pass through the `nlp` pipeline.
|
||
|
||
<Project id="tutorials/rel_component">
|
||
Run this example use-case by using our project template. It includes all the
|
||
code to create the ML model and the pipeline component from scratch. It also
|
||
contains two config files to train the model: one to run on CPU with a Tok2Vec
|
||
layer, and one for the GPU using a transformer. The project applies the
|
||
relation extraction component to identify biomolecular interactions in a
|
||
sample dataset, but you can easily swap in your own dataset for your
|
||
experiments in any other domain.
|
||
</Project>
|
||
|
||
<YouTube id="8HL-Ap5_Axo"></YouTube>
|
||
|
||
#### Step 1: Implementing the Model {id="component-rel-model"}
|
||
|
||
We need to implement a [`Model`](https://thinc.ai/docs/api-model) that takes a
|
||
**list of documents** (~~List[Doc]~~) as input, and outputs a **two-dimensional
|
||
matrix** (~~Floats2d~~) of predictions:
|
||
|
||
> #### Model type annotations
|
||
>
|
||
> The `Model` class is a generic type that can specify its input and output
|
||
> types, e.g. ~~Model[List[Doc], Floats2d]~~. Type hints are used for static
|
||
> type checks and validation. See the section on [type signatures](#type-sigs)
|
||
> for details.
|
||
|
||
```python {title="The model architecture"}
|
||
@spacy.registry.architectures("rel_model.v1")
|
||
def create_relation_model(...) -> Model[List[Doc], Floats2d]:
|
||
model = ... # 👈 model will go here
|
||
return model
|
||
```
|
||
|
||
We adapt a **modular approach** to the definition of this relation model, and
|
||
define it as chaining two layers together: the first layer that generates an
|
||
instance tensor from a given set of documents, and the second layer that
|
||
transforms the instance tensor into a final tensor holding the predictions:
|
||
|
||
> #### config.cfg (excerpt)
|
||
>
|
||
> ```ini
|
||
> [model]
|
||
> @architectures = "rel_model.v1"
|
||
>
|
||
> [model.create_instance_tensor]
|
||
> # ...
|
||
>
|
||
> [model.classification_layer]
|
||
> # ...
|
||
> ```
|
||
|
||
```python {title="The model architecture",highlight="6"}
|
||
@spacy.registry.architectures("rel_model.v1")
|
||
def create_relation_model(
|
||
create_instance_tensor: Model[List[Doc], Floats2d],
|
||
classification_layer: Model[Floats2d, Floats2d],
|
||
) -> Model[List[Doc], Floats2d]:
|
||
model = chain(create_instance_tensor, classification_layer)
|
||
return model
|
||
```
|
||
|
||
The `classification_layer` could be something like a
|
||
[Linear](https://thinc.ai/docs/api-layers#linear) layer followed by a
|
||
[logistic](https://thinc.ai/docs/api-layers#logistic) activation function:
|
||
|
||
> #### config.cfg (excerpt)
|
||
>
|
||
> ```ini
|
||
> [model.classification_layer]
|
||
> @architectures = "rel_classification_layer.v1"
|
||
> nI = null
|
||
> nO = null
|
||
> ```
|
||
|
||
```python {title="The classification layer"}
|
||
@spacy.registry.architectures("rel_classification_layer.v1")
|
||
def create_classification_layer(
|
||
nO: int = None, nI: int = None
|
||
) -> Model[Floats2d, Floats2d]:
|
||
return chain(Linear(nO=nO, nI=nI), Logistic())
|
||
```
|
||
|
||
The first layer that **creates the instance tensor** can be defined by
|
||
implementing a
|
||
[custom forward function](https://thinc.ai/docs/usage-models#weights-layers-forward)
|
||
with an appropriate backpropagation callback. We also define an
|
||
[initialization method](https://thinc.ai/docs/usage-models#weights-layers-init)
|
||
that ensures that the layer is properly set up for training.
|
||
|
||
We omit some of the implementation details here, and refer to the
|
||
[spaCy project](https://github.com/explosion/projects/tree/v3/tutorials/rel_component)
|
||
that has the full implementation.
|
||
|
||
> #### config.cfg (excerpt)
|
||
>
|
||
> ```ini
|
||
> [model.create_instance_tensor]
|
||
> @architectures = "rel_instance_tensor.v1"
|
||
>
|
||
> [model.create_instance_tensor.tok2vec]
|
||
> @architectures = "spacy.HashEmbedCNN.v2"
|
||
> # ...
|
||
>
|
||
> [model.create_instance_tensor.pooling]
|
||
> @layers = "reduce_mean.v1"
|
||
>
|
||
> [model.create_instance_tensor.get_instances]
|
||
> # ...
|
||
> ```
|
||
|
||
```python {title="The layer that creates the instance tensor"}
|
||
@spacy.registry.architectures("rel_instance_tensor.v1")
|
||
def create_tensors(
|
||
tok2vec: Model[List[Doc], List[Floats2d]],
|
||
pooling: Model[Ragged, Floats2d],
|
||
get_instances: Callable[[Doc], List[Tuple[Span, Span]]],
|
||
) -> Model[List[Doc], Floats2d]:
|
||
|
||
return Model(
|
||
"instance_tensors",
|
||
instance_forward,
|
||
init=instance_init,
|
||
layers=[tok2vec, pooling],
|
||
refs={"tok2vec": tok2vec, "pooling": pooling},
|
||
attrs={"get_instances": get_instances},
|
||
)
|
||
|
||
|
||
# The custom forward function
|
||
def instance_forward(
|
||
model: Model[List[Doc], Floats2d],
|
||
docs: List[Doc],
|
||
is_train: bool,
|
||
) -> Tuple[Floats2d, Callable]:
|
||
tok2vec = model.get_ref("tok2vec")
|
||
tokvecs, bp_tokvecs = tok2vec(docs, is_train)
|
||
get_instances = model.attrs["get_instances"]
|
||
all_instances = [get_instances(doc) for doc in docs]
|
||
pooling = model.get_ref("pooling")
|
||
relations = ...
|
||
|
||
def backprop(d_relations: Floats2d) -> List[Doc]:
|
||
d_tokvecs = ...
|
||
return bp_tokvecs(d_tokvecs)
|
||
|
||
return relations, backprop
|
||
|
||
|
||
# The custom initialization method
|
||
def instance_init(
|
||
model: Model,
|
||
X: List[Doc] = None,
|
||
Y: Floats2d = None,
|
||
) -> Model:
|
||
tok2vec = model.get_ref("tok2vec")
|
||
tok2vec.initialize(X)
|
||
return model
|
||
|
||
```
|
||
|
||
This custom layer uses an [embedding layer](/usage/embeddings-transformers) such
|
||
as a [`Tok2Vec`](/api/tok2vec) component or a [`Transformer`](/api/transformer).
|
||
This layer is assumed to be of type ~~Model[List[Doc], List[Floats2d]]~~ as it
|
||
transforms each **document into a list of tokens**, with each token being
|
||
represented by its embedding in the vector space.
|
||
|
||
The `pooling` layer will be applied to summarize the token vectors into **entity
|
||
vectors**, as named entities (represented by ~~Span~~ objects) can consist of
|
||
one or multiple tokens. For instance, the pooling layer could resort to
|
||
calculating the average of all token vectors in an entity. Thinc provides
|
||
several
|
||
[built-in pooling operators](https://thinc.ai/docs/api-layers#reduction-ops) for
|
||
this purpose.
|
||
|
||
Finally, we need a `get_instances` method that **generates pairs of entities**
|
||
that we want to classify as being related or not. As these candidate pairs are
|
||
typically formed within one document, this function takes a [`Doc`](/api/doc) as
|
||
input and outputs a `List` of `Span` tuples. For instance, the following
|
||
implementation takes any two entities from the same document, as long as they
|
||
are within a **maximum distance** (in number of tokens) of each other:
|
||
|
||
> #### config.cfg (excerpt)
|
||
>
|
||
> ```ini
|
||
>
|
||
> [model.create_instance_tensor.get_instances]
|
||
> @misc = "rel_instance_generator.v1"
|
||
> max_length = 100
|
||
> ```
|
||
|
||
```python {title="Candidate generation"}
|
||
@spacy.registry.misc("rel_instance_generator.v1")
|
||
def create_instances(max_length: int) -> Callable[[Doc], List[Tuple[Span, Span]]]:
|
||
def get_candidates(doc: "Doc") -> List[Tuple[Span, Span]]:
|
||
candidates = []
|
||
for ent1 in doc.ents:
|
||
for ent2 in doc.ents:
|
||
if ent1 != ent2:
|
||
if max_length and abs(ent2.start - ent1.start) <= max_length:
|
||
candidates.append((ent1, ent2))
|
||
return candidates
|
||
return get_candidates
|
||
```
|
||
|
||
This function is added to the [`@misc` registry](/api/top-level#registry) so we
|
||
can refer to it from the config, and easily swap it out for any other candidate
|
||
generation function.
|
||
|
||
#### Intermezzo: define how to store the relations data {id="component-rel-attribute"}
|
||
|
||
> #### Example output
|
||
>
|
||
> ```python
|
||
> doc = nlp("Amsterdam is the capital of the Netherlands.")
|
||
> print("spans", [(e.start, e.text, e.label_) for e in doc.ents])
|
||
> for value, rel_dict in doc._.rel.items():
|
||
> print(f"{value}: {rel_dict}")
|
||
>
|
||
> # spans [(0, 'Amsterdam', 'LOC'), (6, 'Netherlands', 'LOC')]
|
||
> # (0, 6): {'CAPITAL_OF': 0.89, 'LOCATED_IN': 0.75, 'UNRELATED': 0.002}
|
||
> # (6, 0): {'CAPITAL_OF': 0.01, 'LOCATED_IN': 0.13, 'UNRELATED': 0.017}
|
||
> ```
|
||
|
||
For our new relation extraction component, we will use a custom
|
||
[extension attribute](/usage/processing-pipelines#custom-components-attributes)
|
||
`doc._.rel` in which we store relation data. The attribute refers to a
|
||
dictionary, keyed by the **start offsets of each entity** involved in the
|
||
candidate relation. The values in the dictionary refer to another dictionary
|
||
where relation labels are mapped to values between 0 and 1. We assume anything
|
||
above 0.5 to be a `True` relation. The ~~Example~~ instances that we'll use as
|
||
training data, will include their gold-standard relation annotations in
|
||
`example.reference._.rel`.
|
||
|
||
```python {title="Registering the extension attribute"}
|
||
from spacy.tokens import Doc
|
||
Doc.set_extension("rel", default={})
|
||
```
|
||
|
||
#### Step 2: Implementing the pipeline component {id="component-rel-pipe"}
|
||
|
||
To use our new relation extraction model as part of a custom
|
||
[trainable component](/usage/processing-pipelines#trainable-components), we
|
||
create a subclass of [`TrainablePipe`](/api/pipe) that holds the model.
|
||
|
||
|
||
|
||
```python {title="Pipeline component skeleton"}
|
||
from spacy.pipeline import TrainablePipe
|
||
|
||
class RelationExtractor(TrainablePipe):
|
||
def __init__(self, vocab, model, name="rel"):
|
||
"""Create a component instance."""
|
||
self.model = model
|
||
self.vocab = vocab
|
||
self.name = name
|
||
|
||
def update(self, examples, drop=0.0, sgd=None, losses=None):
|
||
"""Learn from a batch of Example objects."""
|
||
...
|
||
|
||
def predict(self, docs):
|
||
"""Apply the model to a batch of Doc objects."""
|
||
...
|
||
|
||
def set_annotations(self, docs, predictions):
|
||
"""Modify a batch of Doc objects using the predictions."""
|
||
...
|
||
|
||
def initialize(self, get_examples, nlp=None, labels=None):
|
||
"""Initialize the model before training."""
|
||
...
|
||
|
||
def add_label(self, label):
|
||
"""Add a label to the component."""
|
||
...
|
||
```
|
||
|
||
Typically, the **constructor** defines the vocab, the Machine Learning model,
|
||
and the name of this component. Additionally, this component, just like the
|
||
`textcat` and the `tagger`, stores an **internal list of labels**. The ML model
|
||
will predict scores for each label. We add convenience methods to easily
|
||
retrieve and add to them.
|
||
|
||
```python {title="The constructor (continued)"}
|
||
def __init__(self, vocab, model, name="rel"):
|
||
"""Create a component instance."""
|
||
# ...
|
||
self.cfg = {"labels": []}
|
||
|
||
@property
|
||
def labels(self) -> Tuple[str, ...]:
|
||
"""Returns the labels currently added to the component."""
|
||
return tuple(self.cfg["labels"])
|
||
|
||
def add_label(self, label: str):
|
||
"""Add a new label to the pipe."""
|
||
self.cfg["labels"] = list(self.labels) + [label]
|
||
```
|
||
|
||
After creation, the component needs to be
|
||
[initialized](/usage/training#initialization). This method can define the
|
||
relevant labels in two ways: explicitely by setting the `labels` argument in the
|
||
[`initialize` block](/api/data-formats#config-initialize) of the config, or
|
||
implicately by deducing them from the `get_examples` callback that generates the
|
||
full **training data set**, or a representative sample.
|
||
|
||
The final number of labels defines the output dimensionality of the network, and
|
||
will be used to do
|
||
[shape inference](https://thinc.ai/docs/usage-models#validation) throughout the
|
||
layers of the neural network. This is triggered by calling
|
||
[`Model.initialize`](https://thinc.ai/api/model#initialize).
|
||
|
||
```python {title="The initialize method",highlight="12,15,18,22"}
|
||
from itertools import islice
|
||
|
||
def initialize(
|
||
self,
|
||
get_examples: Callable[[], Iterable[Example]],
|
||
*,
|
||
nlp: Language = None,
|
||
labels: Optional[List[str]] = None,
|
||
):
|
||
if labels is not None:
|
||
for label in labels:
|
||
self.add_label(label)
|
||
else:
|
||
for example in get_examples():
|
||
relations = example.reference._.rel
|
||
for indices, label_dict in relations.items():
|
||
for label in label_dict.keys():
|
||
self.add_label(label)
|
||
subbatch = list(islice(get_examples(), 10))
|
||
doc_sample = [eg.reference for eg in subbatch]
|
||
label_sample = self._examples_to_truth(subbatch)
|
||
self.model.initialize(X=doc_sample, Y=label_sample)
|
||
```
|
||
|
||
The `initialize` method is triggered whenever this component is part of an `nlp`
|
||
pipeline, and [`nlp.initialize`](/api/language#initialize) is invoked.
|
||
Typically, this happens when the pipeline is set up before training in
|
||
[`spacy train`](/api/cli#training). After initialization, the pipeline component
|
||
and its internal model can be trained and used to make predictions.
|
||
|
||
During training, the method [`update`](/api/pipe#update) is invoked which
|
||
delegates to
|
||
[`Model.begin_update`](https://thinc.ai/docs/api-model#begin_update) and a
|
||
[`get_loss`](/api/pipe#get_loss) function that **calculates the loss** for a
|
||
batch of examples, as well as the **gradient** of loss that will be used to
|
||
update the weights of the model layers. Thinc provides several
|
||
[loss functions](https://thinc.ai/docs/api-loss) that can be used for the
|
||
implementation of the `get_loss` function.
|
||
|
||
```python {title="The update method",highlight="12-14"}
|
||
def update(
|
||
self,
|
||
examples: Iterable[Example],
|
||
*,
|
||
drop: float = 0.0,
|
||
sgd: Optional[Optimizer] = None,
|
||
losses: Optional[Dict[str, float]] = None,
|
||
) -> Dict[str, float]:
|
||
# ...
|
||
docs = [eg.predicted for eg in examples]
|
||
predictions, backprop = self.model.begin_update(docs)
|
||
loss, gradient = self.get_loss(examples, predictions)
|
||
backprop(gradient)
|
||
losses[self.name] += loss
|
||
# ...
|
||
return losses
|
||
```
|
||
|
||
After training the model, the component can be used to make novel
|
||
**predictions**. The [`predict`](/api/pipe#predict) method needs to be
|
||
implemented for each subclass of `TrainablePipe`. In our case, we can simply
|
||
delegate to the internal model's
|
||
[predict](https://thinc.ai/docs/api-model#predict) function that takes a batch
|
||
of `Doc` objects and returns a ~~Floats2d~~ array:
|
||
|
||
```python {title="The predict method"}
|
||
def predict(self, docs: Iterable[Doc]) -> Floats2d:
|
||
predictions = self.model.predict(docs)
|
||
return self.model.ops.asarray(predictions)
|
||
```
|
||
|
||
The final method that needs to be implemented, is
|
||
[`set_annotations`](/api/pipe#set_annotations). This function takes the
|
||
predictions, and modifies the given `Doc` object in place to store them. For our
|
||
relation extraction component, we store the data in the
|
||
[custom attribute](#component-rel-attribute)`doc._.rel`.
|
||
|
||
To interpret the scores predicted by the relation extraction model correctly, we
|
||
need to refer to the model's `get_instances` function that defined which pairs
|
||
of entities were relevant candidates, so that the predictions can be linked to
|
||
those exact entities:
|
||
|
||
```python {title="The set_annotations method",highlight="5-6,10"}
|
||
def set_annotations(self, docs: Iterable[Doc], predictions: Floats2d):
|
||
c = 0
|
||
get_instances = self.model.attrs["get_instances"]
|
||
for doc in docs:
|
||
for (e1, e2) in get_instances(doc):
|
||
offset = (e1.start, e2.start)
|
||
if offset not in doc._.rel:
|
||
doc._.rel[offset] = {}
|
||
for j, label in enumerate(self.labels):
|
||
doc._.rel[offset][label] = predictions[c, j]
|
||
c += 1
|
||
```
|
||
|
||
Under the hood, when the pipe is applied to a document, it delegates to the
|
||
`predict` and `set_annotations` methods:
|
||
|
||
```python {title="The __call__ method"}
|
||
def __call__(self, doc: Doc):
|
||
predictions = self.predict([doc])
|
||
self.set_annotations([doc], predictions)
|
||
return doc
|
||
```
|
||
|
||
There is one more optional method to implement: [`score`](/api/pipe#score)
|
||
calculates the performance of your component on a set of examples, and returns
|
||
the results as a dictionary:
|
||
|
||
```python {title="The score method"}
|
||
def score(self, examples: Iterable[Example]) -> Dict[str, Any]:
|
||
prf = PRFScore()
|
||
for example in examples:
|
||
...
|
||
|
||
return {
|
||
"rel_micro_p": prf.precision,
|
||
"rel_micro_r": prf.recall,
|
||
"rel_micro_f": prf.fscore,
|
||
}
|
||
```
|
||
|
||
This is particularly useful for calculating relevant scores on the development
|
||
corpus when training the component with [`spacy train`](/api/cli#training).
|
||
|
||
Once our `TrainablePipe` subclass is fully implemented, we can
|
||
[register](/usage/processing-pipelines#custom-components-factories) the
|
||
component with the [`@Language.factory`](/api/language#factory) decorator. This
|
||
assigns it a name and lets you create the component with
|
||
[`nlp.add_pipe`](/api/language#add_pipe) and via the
|
||
[config](/usage/training#config).
|
||
|
||
> #### config.cfg (excerpt)
|
||
>
|
||
> ```ini
|
||
> [components.relation_extractor]
|
||
> factory = "relation_extractor"
|
||
>
|
||
> [components.relation_extractor.model]
|
||
> @architectures = "rel_model.v1"
|
||
> # ...
|
||
>
|
||
> [training.score_weights]
|
||
> rel_micro_p = 0.0
|
||
> rel_micro_r = 0.0
|
||
> rel_micro_f = 1.0
|
||
> ```
|
||
|
||
```python {title="Registering the pipeline component"}
|
||
from spacy.language import Language
|
||
|
||
@Language.factory("relation_extractor")
|
||
def make_relation_extractor(nlp, name, model):
|
||
return RelationExtractor(nlp.vocab, model, name)
|
||
```
|
||
|
||
You can extend the decorator to include information such as the type of
|
||
annotations that are required for this component to run, the type of annotations
|
||
it produces, and the scores that can be calculated:
|
||
|
||
```python {title="Factory annotations",highlight="5-11"}
|
||
from spacy.language import Language
|
||
|
||
@Language.factory(
|
||
"relation_extractor",
|
||
requires=["doc.ents", "token.ent_iob", "token.ent_type"],
|
||
assigns=["doc._.rel"],
|
||
default_score_weights={
|
||
"rel_micro_p": None,
|
||
"rel_micro_r": None,
|
||
"rel_micro_f": None,
|
||
},
|
||
)
|
||
def make_relation_extractor(nlp, name, model):
|
||
return RelationExtractor(nlp.vocab, model, name)
|
||
```
|
||
|
||
<Project id="tutorials/rel_component">
|
||
Run this example use-case by using our project template. It includes all the
|
||
code to create the ML model and the pipeline component from scratch. It
|
||
contains two config files to train the model: one to run on CPU with a Tok2Vec
|
||
layer, and one for the GPU using a transformer. The project applies the
|
||
relation extraction component to identify biomolecular interactions, but you
|
||
can easily swap in your own dataset for your experiments in any other domain.
|
||
</Project>
|