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edits and updates to implementing REL component docs

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website/docs/usage/layers-architectures.md
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@ -624,9 +624,9 @@ 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
that has the full implementation
[here](https://github.com/explosion/projects/tree/v3/tutorials/rel_component).
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)
>
@ -636,13 +636,13 @@ that has the full implementation
>
> [model.create_instance_tensor.tok2vec]
> @architectures = "spacy.HashEmbedCNN.v1"
> ...
> # ...
>
> [model.create_instance_tensor.pooling]
> @layers = "reduce_mean.v1"
>
> [model.create_instance_tensor.get_instances]
> ...
> # ...
> `
> ```
@ -658,10 +658,10 @@ def create_tensors(
return Model(
"instance_tensors",
instance_forward,
init=instance_init,
layers=[tok2vec, pooling],
refs={"tok2vec": tok2vec, "pooling": pooling},
attrs={"get_instances": get_instances},
init=instance_init,
)
@ -671,9 +671,11 @@ def instance_forward(
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]:
@ -744,14 +746,35 @@ 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
references, so we can access them easily:
#### Intermezzo: define how to store the relations data {#component-rel-attribute}
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`.
> #### 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}
> ```
```python
pooling = model.get_ref("pooling")
tok2vec = model.get_ref("tok2vec")
get_instances = model.attrs["get_instances"]
### Registering the extension attribute
from spacy.tokens import Doc
Doc.set_extension("rel", default={})
```
#### Step 2: Implementing the pipeline component {#component-rel-pipe}
@ -794,19 +817,43 @@ class RelationExtractor(TrainablePipe):
...
```
Before the model can be used, it needs to be
[initialized](/usage/training#initialization). This function receives a callback
to access 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 to `initialize`, or you can call
`RelationExtractor.add_label` directly. The number of labels defines the output
dimensionality of the network, and will be used to do
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 method to easily retrieve and
add to them.
```python
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
### The initialize method {highlight="12,18,22"}
### The initialize method {highlight="12,15,18,22"}
from itertools import islice
def initialize(
@ -837,7 +884,7 @@ 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 function [`update`](/api/pipe#update) is invoked which
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
@ -858,7 +905,7 @@ def update(
losses: Optional[Dict[str, float]] = None,
) -> Dict[str, float]:
# ...
docs = [ex.predicted for ex in examples]
docs = [eg.predicted for eg in examples]
predictions, backprop = self.model.begin_update(docs)
loss, gradient = self.get_loss(examples, predictions)
backprop(gradient)
@ -867,8 +914,8 @@ def update(
return losses
```
When the internal model is trained, the component can be used to make novel
**predictions**. The [`predict`](/api/pipe#predict) function needs to be
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
@ -884,42 +931,21 @@ def predict(self, docs: Iterable[Doc]) -> Floats2d:
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](/usage/processing-pipelines#custom-components-attributes)
`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.
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_candidates` function that defined which pairs
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:
> #### 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}
> ```
```python
### Registering the extension attribute
from spacy.tokens import Doc
Doc.set_extension("rel", default={})
```
```python
### The set_annotations method {highlight="5-6,10"}
def set_annotations(self, docs: Iterable[Doc], predictions: Floats2d):
c = 0
get_candidates = self.model.attrs["get_candidates"]
get_instances = self.model.attrs["get_instances"]
for doc in docs:
for (e1, e2) in get_candidates(doc):
for (e1, e2) in get_instances(doc):
offset = (e1.start, e2.start)
if offset not in doc._.rel:
doc._.rel[offset] = {}
@ -933,7 +959,7 @@ Under the hood, when the pipe is applied to a document, it delegates to the
```python
### The __call__ method
def __call__(self, Doc doc):
def __call__(self, doc: Doc):
predictions = self.predict([doc])
self.set_annotations([doc], predictions)
return doc
@ -957,8 +983,8 @@ 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 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
@ -975,13 +1001,8 @@ assigns it a name and lets you create the component with
>
> [components.relation_extractor.model]
> @architectures = "rel_model.v1"
>
> [components.relation_extractor.model.tok2vec]
> # ...
>
> [components.relation_extractor.model.get_candidates]
> @misc = "rel_cand_generator.v1"
> max_length = 20
>
> [training.score_weights]
> rel_micro_p = 0.0