Merge pull request #6252 from svlandeg/fix/docs

spacy/pipeline/tagger.pyx

@@ -195,7 +195,7 @@ class Tagger(TrainablePipe):
         validate_examples(examples, "Tagger.update")
         if not any(len(eg.predicted) if eg.predicted else 0 for eg in examples):
             # Handle cases where there are no tokens in any docs.
-            return
+            return losses
         set_dropout_rate(self.model, drop)
         tag_scores, bp_tag_scores = self.model.begin_update([eg.predicted for eg in examples])
         for sc in tag_scores:
@@ -227,22 +227,24 @@ class Tagger(TrainablePipe):
 
         DOCS: https://nightly.spacy.io/api/tagger#rehearse
         """
+        if losses is None:
+            losses = {}
+        losses.setdefault(self.name, 0.0)
         validate_examples(examples, "Tagger.rehearse")
         docs = [eg.predicted for eg in examples]
         if self._rehearsal_model is None:
-            return
+            return losses
         if not any(len(doc) for doc in docs):
             # Handle cases where there are no tokens in any docs.
-            return
+            return losses
         set_dropout_rate(self.model, drop)
         guesses, backprop = self.model.begin_update(docs)
         target = self._rehearsal_model(examples)
         gradient = guesses - target
         backprop(gradient)
         self.finish_update(sgd)
-        if losses is not None:
-            losses.setdefault(self.name, 0.0)
         losses[self.name] += (gradient**2).sum()
+        return losses
 
     def get_loss(self, examples, scores):
         """Find the loss and gradient of loss for the batch of documents and

spacy/pipeline/trainable_pipe.pyx

@@ -116,7 +116,7 @@ cdef class TrainablePipe(Pipe):
         validate_examples(examples, "TrainablePipe.update")
         if not any(len(eg.predicted) if eg.predicted else 0 for eg in examples):
             # Handle cases where there are no tokens in any docs.
-            return
+            return losses
         set_dropout_rate(self.model, drop)
         scores, bp_scores = self.model.begin_update([eg.predicted for eg in examples])
         loss, d_scores = self.get_loss(examples, scores)

website/docs/usage/layers-architectures.md

@@ -503,7 +503,7 @@ overview of the `TrainablePipe` methods used by
 
 </Infobox>
 
-### Example: Entity elation extraction component {#component-rel}
+### Example: Entity relation extraction component {#component-rel}
 
 This section outlines an example use-case of implementing a **novel relation
 extraction component** from scratch. We'll implement a binary relation
@@ -618,7 +618,7 @@ we can define our relation model in a config file as such:
 # ...
 
 [model.get_candidates]
-@misc = "rel_cand_generator.v2"
+@misc = "rel_cand_generator.v1"
 max_length = 20
 
 [model.create_candidate_tensor]
@@ -687,8 +687,8 @@ 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 the
+labels can be provided to `initialize`, or you can call 
-`RelationExtractoradd_label` directly. The number of labels defines the output
+`RelationExtractor.add_label` directly. 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
@@ -729,7 +729,7 @@ 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
 [`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
+[`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