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website/docs/usage/layers-architectures.md
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@ -373,7 +373,7 @@ gpu_allocator = "pytorch"
Of course it's also possible to define the `Model` from the previous section
entirely in Thinc. The Thinc documentation provides details on the
[various layers](https://thinc.ai/docs/api-layers) and helper functions
available. Combinators can also be used to
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:
@ -494,13 +494,34 @@ from scratch. This can be done by creating a new class inheriting from
### Example: Pipeline component for relation extraction {#component-rel}
This section will run through an example of implementing a novel relation
extraction component from scratch. As a first step, we need a method that will
This section outlines an example use-case of implementing a novel relation
extraction component from scratch. We assume we want to implement a binary
relation extraction method that determines whether two entities in a document
are related or not, and if so, with what type of relation. We'll allow multiple
types of relations between two such entities - i.e. it is a multi-label setting.
We'll need to implement a [`Model`](https://thinc.ai/docs/api-model) that takes
a list of documents as input, and outputs a two-dimensional matrix of scores:
```python
@registry.architectures.register("rel_model.v1")
def create_relation_model(...) -> Model[List[Doc], Floats2d]:
model = _create_my_model()
return model
```
The first layer in this model will typically be an
[embedding layer](/usage/embeddings-transformers) such as a
[`Tok2Vec`](/api/tok2vec) component or [`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.
Next, we need a method that will
generate pairs of entities that we want to classify as being related or not.
These candidate pairs are typically formed within one document, which means
we'll have a function that takes a `Doc` as input and outputs a `List` of `Span`
tuples. In this example, we will focus on binary relation extraction, i.e. the
tuple will be of length 2. For instance, a very straightforward implementation
tuples. For instance, a very straightforward implementation
would be to just take any two entities from the same document:
```python
@ -512,18 +533,24 @@ def get_candidates(doc: "Doc") -> List[Tuple[Span, Span]]:
return candidates
```
But we could also refine this further by excluding relations of an entity with
itself, and posing a maximum distance (in number of tokens) between two
entities. We'll also register this function in 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.
> ```
> [get_candidates]
> [model]
> @architectures = "rel_model.v1"
>
> [model.tok2vec]
> ...
>
> [model.get_candidates]
> @misc = "rel_cand_generator.v2"
> max_length = 6
> ```
But we could also refine this further by excluding relations of an entity with
itself, and posing a maximum distance (in number of tokens) between two
entities. We'll register this function in 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.
```python
### {highlight="1,2,7,8"}
@registry.misc.register("rel_cand_generator.v2")
@ -539,32 +566,33 @@ def create_candidate_indices(max_length: int) -> Callable[[Doc], List[Tuple[Span
return get_candidates
```
Finally, we'll require a method that transforms the candidate pairs of entities into
a 2D tensor using the specified Tok2Vec function, and this `Floats2d` object will then be
processed by a final `output_layer` of the network. Taking all this together, we can define
our relation model like this in the config:
> ```
> [tok2vec]
> @architectures = "spacy.HashEmbedCNN.v1"
> pretrained_vectors = null
> width = 96
> depth = 2
> embed_size = 300
> window_size = 1
> maxout_pieces = 3
> subword_features = true
> [model]
> @architectures = "rel_model.v1"
> nO = null
>
> [model.tok2vec]
> ...
>
> [model.get_candidates]
> @misc = "rel_cand_generator.v2"
> max_length = 6
>
> [components.relation_extractor.model.create_candidate_tensor]
> @misc = "rel_cand_tensor.v1"
>
> [components.relation_extractor.model.output_layer]
> @architectures = "rel_output_layer.v1"
> nI = null
> nO = null
> ```
Next, we'll assume we have access to an
[embedding layer](/usage/embeddings-transformers) such as a
[`Tok2Vec`](/api/tok2vec) component or [`Transformer`](/api/transformer). This
layer is assumed to be of type `Model[List["Doc"], List[Floats2d]]` as it
transforms a list of documents into a list of 2D vectors. Further, this
`tok2vec` component will be trainable, which means that, following the Thinc
paradigm, we'll apply it to some input, and receive the predicted results as
well as a callback to perform backpropagation:
```python
tok2vec = model.get_ref("tok2vec")
tokvecs, bp_tokvecs = tok2vec(docs, is_train=True)
```
<!-- Link to project for implementation details -->