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@ -502,7 +502,7 @@ with Model.define_operators({">>": chain}):
## Create new trainable components {#components}
In addition to [swapping out](#swap-architectures) default models in built-in
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
@ -523,25 +523,27 @@ overview of the `TrainablePipe` methods used by
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. We'll allow multiple types of
relations between two such entities (multi-label setting). There are two major
steps required:
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 candidates from a [`Doc`](/api/doc) and predict
a relation for the available candidate pairs.
2. Implement a custom [pipeline component](#component-rel-pipe) powered by the
machine learning model that sets annotations on the [`Doc`](/api/doc) passing
through the pipeline.
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 contains two config files to train the model:
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, but you can easily swap in your own dataset for your experiments
in any other domain.
interactions in a sample dataset, but you can easily swap in your own dataset
for your experiments in any other domain.
</Project>
#### Step 1: Implementing the Model {#component-rel-model}
@ -558,18 +560,17 @@ matrix** (~~Floats2d~~) of predictions:
> for details.
```python
### Register the model architecture
### The model architecture
@spacy.registry.architectures.register("rel_model.v1")
def create_relation_model(...) -> Model[List[Doc], Floats2d]:
model = ... # 👈 model will go here
return model
```
We will adapt a **modular approach** to the definition of this relation model, and
define it as chaining to layers together: the first layer that generates an
instance tensor from a given set of documents, and the second layer that
transforms this tensor into a final tensor holding the predictions:
We will 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)
>
@ -581,11 +582,11 @@ transforms this tensor into a final tensor holding the predictions:
> # ...
>
> [model.classification_layer]
> ...
> # ...
> ```
```python
### Implement the model architecture
### The model architecture
@spacy.registry.architectures.register("rel_model.v1")
def create_relation_model(
create_instance_tensor: Model[List[Doc], Floats2d],
@ -595,9 +596,8 @@ def create_relation_model(
return model
```
The `classification_layer` could be something simple like a Linear layer
followed by a logistic activation function:
The `classification_layer` could be something like a Linear layer followed by a
logistic activation function:
> #### config.cfg (excerpt)
>
@ -609,7 +609,7 @@ followed by a logistic activation function:
> ```
```python
### Implement the classification layer
### The classification layer
@spacy.registry.architectures.register("rel_classification_layer.v1")
def create_classification_layer(
nO: int = None, nI: int = None
@ -617,60 +617,16 @@ def create_classification_layer(
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)
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.
```python
### Implement 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)
relations = ...
def backprop(d_relations: Floats2d) -> List[Doc]:
d_tokvecs = ...
return bp_tokvecs(d_tokvecs)
return relations, backprop
### Implement 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
### Implement the layer that creates the instance tensor
@spacy.registry.architectures.register("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,
layers=[tok2vec, pooling],
refs={"tok2vec": tok2vec, "pooling": pooling},
attrs={"get_instances": get_instances},
init=instance_init,
)
```
We omit some of the implementation details here, and refer to the spaCy project
that has the full implementation
[here](https://github.com/explosion/projects/tree/v3/tutorials/rel_component).
> #### config.cfg (excerpt)
>
@ -688,19 +644,69 @@ def create_tensors(
> [model.create_instance_tensor.get_instances]
> ...
> `
> ```
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
```python
### The layer that creates the instance tensor
@spacy.registry.architectures.register("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,
layers=[tok2vec, pooling],
refs={"tok2vec": tok2vec, "pooling": pooling},
attrs={"get_instances": get_instances},
init=instance_init,
)
### 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)
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.
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
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.
> #### config.cfg (excerpt)
@ -712,15 +718,15 @@ this purpose.
> max_length = 100
> ```
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, this
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 eachother:
```python
### Simple candiate generation
### Candiate generation
@spacy.registry.misc.register("rel_instance_generator.v1")
def create_candidate_indices(max_length: int) -> Callable[[Doc], List[Tuple[Span, Span]]]:
def get_candidates(doc: "Doc") -> List[Tuple[Span, Span]]:
@ -733,10 +739,10 @@ def create_candidate_indices(max_length: int) -> Callable[[Doc], List[Tuple[Span
return candidates
return get_candidates
```
This function in 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.
This function in 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.
When creating this model, we store the custom functions as
[attributes](https://thinc.ai/docs/api-model#properties) and the sublayers as
@ -851,13 +857,13 @@ def update(
sgd: Optional[Optimizer] = None,
losses: Optional[Dict[str, float]] = None,
) -> Dict[str, float]:
...
# ...
docs = [ex.predicted for ex in examples]
predictions, backprop = self.model.begin_update(docs)
loss, gradient = self.get_loss(examples, predictions)
backprop(gradient)
losses[self.name] += loss
...
# ...
return losses
```
@ -933,9 +939,9 @@ def __call__(self, Doc doc):
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:
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
### The score method
@ -951,8 +957,8 @@ def score(self, examples: Iterable[Example]) -> Dict[str, Any]:
}
```
This is particularly useful to see the scores on the development corpus
when training the component with [`spacy train`](/api/cli#training).
This is particularly useful to see the 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
@ -976,7 +982,7 @@ assigns it a name and lets you create the component with
> [components.relation_extractor.model.get_candidates]
> @misc = "rel_cand_generator.v1"
> max_length = 20
>
>
> [training.score_weights]
> rel_micro_p = 0.0
> rel_micro_r = 0.0
@ -992,8 +998,8 @@ 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
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