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various small fixes

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website/docs/usage/layers-architectures.md
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@ -288,7 +288,7 @@ those parts of the network.
To use our custom model including the PyTorch subnetwork, all we need to do is
register the architecture using the
[`architectures` registry](/api/top-level#registry). This will assign the
[`architectures` registry](/api/top-level#registry). This assigns the
architecture a name so spaCy knows how to find it, and allows passing in
arguments like hyperparameters via the [config](/usage/training#config). The
full example then becomes:
@ -488,27 +488,27 @@ with Model.define_operators({">>": chain}):
In addition to [swapping out](#swap-architectures) default models in built-in
components, you can also implement an entirely new,
[trainable pipeline component](usage/processing-pipelines#trainable-components)
[trainable pipeline component](/usage/processing-pipelines#trainable-components)
from scratch. This can be done by creating a new class inheriting from
[`Pipe`](/api/pipe), and linking it up to your custom model implementation.
### Example: Pipeline component for relation extraction {#component-rel}
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.
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: first, we need to
[implement a machine learning model](#component-rel-model) specific to this
task, and then we'll use this model to
task, and subsequently we use this model to
[implement a custom pipeline component](#component-rel-pipe).
#### Step 1: Implementing the Model {#component-rel-model}
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:
We 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 predictions:
```python
@registry.architectures.register("rel_model.v1")
@ -519,17 +519,16 @@ def create_relation_model(...) -> Model[List[Doc], Floats2d]:
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
[`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.
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. For instance, a very
straightforward implementation would be to just take any two entities from the
same document:
Next, we need a 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` as input and outputs a `List` of
`Span` tuples. For instance, a very straightforward implementation would be to
just take any two entities from the same document:
```python
def get_candidates(doc: "Doc") -> List[Tuple[Span, Span]]:
@ -549,12 +548,12 @@ def get_candidates(doc: "Doc") -> List[Tuple[Span, Span]]:
>
> [model.get_candidates]
> @misc = "rel_cand_generator.v2"
> max_length = 6
> max_length = 20
> ```
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
entities. We 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.
@ -573,10 +572,10 @@ 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:
Finally, we require a method that transforms the candidate entity pairs into a
2D tensor using the specified `Tok2Vec` function. The resulting `Floats2d`
object will then be processed by a final `output_layer` of the network. Putting
all this together, we can define our relation model in a config file as such:
```
[model]
@ -588,7 +587,7 @@ all this together, we can define our relation model like this in the config:
[model.get_candidates]
@misc = "rel_cand_generator.v2"
max_length = 6
max_length = 20
[model.create_candidate_tensor]
@misc = "rel_cand_tensor.v1"
@ -600,7 +599,7 @@ max_length = 6
<!-- TODO: Link to project for implementation details -->
When creating this model, we'll store the custom functions as
When creating this model, we store the custom functions as
[attributes](https://thinc.ai/docs/api-model#properties) and the sublayers as
references, so we can access them easily:
@ -614,7 +613,7 @@ get_candidates = model.attrs["get_candidates"]
#### Step 2: Implementing the pipeline component {#component-rel-pipe}
To use our new relation extraction model as part of a custom component, we
create a subclass of [`Pipe`](/api/pipe) that will hold the model:
create a subclass of [`Pipe`](/api/pipe) that holds the model:
```python
from spacy.pipeline import Pipe
@ -624,6 +623,9 @@ class RelationExtractor(Pipe):
self.model = model
...
def update(self, examples, ...):
...
def predict(self, docs):
...
@ -631,18 +633,19 @@ class RelationExtractor(Pipe):
...
```
Before the model can be used however, it needs to be
[initialized](/api/pipe#initialize). This function recieves either the full
training data set, or a representative sample. The training data can be used
to deduce all relevant labels. Alternatively, a list of labels can be provided,
or a script can call `rel_component.add_label()` to add each label separately.
Before the model can be used, it needs to be
[initialized](/api/pipe#initialize). This function receives either the full
training data set, or a representative sample. This data set can be used to
deduce all relevant labels. Alternatively, a list of labels can be provided, or
a script can call `rel_component.add_label()` directly.
The number of labels will define 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 triggerd by calling `model.initialize`.
The 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`.
```python
### {highlight="12,18,22"}
from itertools import islice
def initialize(
@ -666,18 +669,21 @@ def initialize(
label_sample = self._examples_to_truth(subbatch)
self.model.initialize(X=doc_sample, Y=label_sample)
```
The `initialize` method will be triggered whenever this component is part of an
`nlp` pipeline, and `nlp.initialize()` is invoked. After doing so, the pipeline
component and its internal model can be trained and used to make predictions.
During training the function [`update`](/api/pipe#update) is invoked which delegates to
[`self.model.begin_update`](https://thinc.ai/docs/api-model#begin_update) and
needs a function [`get_loss`](/api/pipe#get_loss) that will calculate 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.
The `initialize` method is triggered whenever this component is part of an `nlp`
pipeline, and [`nlp.initialize()`](/api/language#initialize) is invoked. After
doing so, the pipeline component and its internal model can be trained and used
to make predictions.
During training, the function [`update`](/api/pipe#update) is invoked which
delegates to
[`self.model.begin_update`](https://thinc.ai/docs/api-model#begin_update) and a
[`get_loss`](/api/pipe#get_loss) function that calculate 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.
```python
### {highlight="12-14"}
def update(
self,
examples: Iterable[Example],
@ -697,13 +703,13 @@ def update(
return losses
```
Thinc provides some [loss functions](https://thinc.ai/docs/api-loss) that can be used
for the implementation of the `get_loss` function.
Thinc provides several [loss functions](https://thinc.ai/docs/api-loss) that can
be used for the implementation of the `get_loss` function.
When the internal model is trained, the component can be used to make novel predictions.
The [`predict`](/api/pipe#predict) function needs to be implemented for each
subclass of `Pipe`. In our case, we can simply delegate to the internal model's
[predict](https://thinc.ai/docs/api-model#predict) function:
When the internal model is trained, the component can be used to make novel
predictions. The [`predict`](/api/pipe#predict) function needs to be implemented
for each subclass of `Pipe`. In our case, we can simply delegate to the internal
model's [predict](https://thinc.ai/docs/api-model#predict) function:
```python
def predict(self, docs: Iterable[Doc]) -> Floats2d:
@ -711,24 +717,24 @@ def predict(self, docs: Iterable[Doc]) -> Floats2d:
return self.model.ops.asarray(predictions)
```
The other method that needs to be implemented, is
[`set_annotations`](/api/pipe#set_annotations). It takes the predicted scores,
and modifies the given `Doc` object in place to hold the predictions. For our
relation extraction component, we'll store the data as a dictionary in a custom
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 as a dictionary in a custom
extension attribute `doc._.rel`. As keys, we represent the candidate pair by the
start offsets of each entity, as this defines an entity pair uniquely within one
document.
To interpret the scores predicted by the REL model correctly, we need to
refer to the model's `get_candidates` function that originally defined which
pairs of entities would be run through the model, so that the scores can be
related to those exact entities:
To interpret the scores predicted by the REL model correctly, we need to refer
to the model's `get_candidates` function that defined which pairs of entities
were relevant candidates, so that the predictions can be linked to those exact
entities:
> #### Example output
>
> ```python
> doc = nlp("Amsterdam is the capital of the Netherlands.")
> print(f"spans: {[(e.start, e.text, e.label_) for e in doc.ents]}")
> print(f"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}")
> ```
@ -740,6 +746,7 @@ related to those exact entities:
> ```
```python
### {highlight="5-6,10"}
def set_annotations(self, docs: Iterable[Doc], predictions: Floats2d):
c = 0
get_candidates = self.model.attrs["get_candidates"]
@ -753,8 +760,8 @@ def set_annotations(self, docs: Iterable[Doc], predictions: Floats2d):
c += 1
```
Under the hood, when the pipe is applied to a document, it will delegate to these
two methods:
Under the hood, when the pipe is applied to a document, it delegates to the
`predict` and `set_annotations` functions:
```python
def __call__(self, Doc doc):
@ -763,18 +770,17 @@ def __call__(self, Doc doc):
return doc
```
Once our `Pipe` subclass is fully implemented, we can
[register](http://localhost:8000/usage/processing-pipelines#custom-components-factories)
the component with the
`Language.factory` decorator. This will enable the creation of the component with
`nlp.add_pipe`, or via the config.
Once our `Pipe` subclass is fully implemented, we can
[register](http://localhost:8000/usage/processing-pipelines#custom-components-factories)
the component with the `Language.factory` decorator. This enables the creation
of the component with `nlp.add_pipe`, or via the config.
> ```
>
>
> [components.relation_extractor]
> factory = "relation_extractor"
> labels = []
>
>
> [components.relation_extractor.model]
> @architectures = "rel_model.v1"
> ...