Update docstrings and docs [ci skip]

spacy/pipeline/textcat.py

@@ -172,7 +172,7 @@ class TextCategorizer(Pipe):
         return scores
 
     def set_annotations(self, docs: Iterable[Doc], scores) -> None:
-        """Modify a batch of documents, using pre-computed scores.
+        """Modify a batch of [`Doc`](/api/doc) objects, using pre-computed scores.
 
         docs (Iterable[Doc]): The documents to modify.
         scores: The scores to set, produced by TextCategorizer.predict.

website/docs/api/dependencyparser.md

@@ -162,7 +162,8 @@ Initialize the pipe for training, using data examples if available. Returns an
 
 ## DependencyParser.predict {#predict tag="method"}
 
-Apply the pipeline's model to a batch of docs, without modifying them.
+Apply the component's model to a batch of [`Doc`](/api/doc) objects, without
+modifying them.
 
 > #### Example
 >
@@ -178,7 +179,7 @@ Apply the pipeline's model to a batch of docs, without modifying them.
 
 ## DependencyParser.set_annotations {#set_annotations tag="method"}
 
-Modify a batch of documents, using pre-computed scores.
+Modify a batch of [`Doc`](/api/doc) objects, using pre-computed scores.
 
 > #### Example
 >

website/docs/api/entitylinker.md

@@ -162,9 +162,9 @@ Initialize the pipe for training, using data examples if available. Returns an
 
 ## EntityLinker.predict {#predict tag="method"}
 
-Apply the pipeline's model to a batch of docs, without modifying them. Returns
-the KB IDs for each entity in each doc, including `NIL` if there is no
-prediction.
+Apply the component's model to a batch of [`Doc`](/api/doc) objects, without
+modifying them. Returns the KB IDs for each entity in each doc, including `NIL`
+if there is no prediction.
 
 > #### Example
 >

website/docs/api/entityrecognizer.md

@@ -151,7 +151,8 @@ Initialize the pipe for training, using data examples if available. Returns an
 
 ## EntityRecognizer.predict {#predict tag="method"}
 
-Apply the pipeline's model to a batch of docs, without modifying them.
+Apply the component's model to a batch of [`Doc`](/api/doc) objects, without
+modifying them.
 
 > #### Example
 >
@@ -167,7 +168,7 @@ Apply the pipeline's model to a batch of docs, without modifying them.
 
 ## EntityRecognizer.set_annotations {#set_annotations tag="method"}
 
-Modify a batch of documents, using pre-computed scores.
+Modify a batch of [`Doc`](/api/doc) objects, using pre-computed scores.
 
 > #### Example
 >

website/docs/api/morphologizer.md

@@ -142,7 +142,8 @@ Initialize the pipe for training, using data examples if available. Returns an
 
 ## Morphologizer.predict {#predict tag="method"}
 
-Apply the pipeline's model to a batch of docs, without modifying them.
+Apply the component's model to a batch of [`Doc`](/api/doc) objects, without
+modifying them.
 
 > #### Example
 >
@@ -158,7 +159,7 @@ Apply the pipeline's model to a batch of docs, without modifying them.
 
 ## Morphologizer.set_annotations {#set_annotations tag="method"}
 
-Modify a batch of documents, using pre-computed scores.
+Modify a batch of [`Doc`](/api/doc) objects, using pre-computed scores.
 
 > #### Example
 >
@@ -175,8 +176,9 @@ Modify a batch of documents, using pre-computed scores.
 
 ## Morphologizer.update {#update tag="method"}
 
-Learn from a batch of documents and gold-standard information, updating the
-pipe's model. Delegates to [`predict`](/api/morphologizer#predict) and
+Learn from a batch of [`Example`](/api/example) objects containing the
+predictions and gold-standard annotations, and update the component's model.
+Delegates to [`predict`](/api/morphologizer#predict) and
 [`get_loss`](/api/morphologizer#get_loss).
 
 > #### Example

website/docs/api/pipe.md

@@ -8,7 +8,18 @@ This class is a base class and **not instantiated directly**. Trainable pipeline
 components like the [`EntityRecognizer`](/api/entityrecognizer) or
 [`TextCategorizer`](/api/textcategorizer) inherit from it and it defines the
 interface that components should follow to function as trainable components in a
-spaCy pipeline.
+spaCy pipeline. See the docs on
+[writing trainable components](/usage/processing-pipelines#trainable) for how to
+use the `Pipe` base class to implement custom components.
+
+> #### Why is Pipe implemented in Cython?
+>
+> The `Pipe` class is implemented in a `.pyx` module, the extension used by
+> [Cython](/api/cython). This is needed so that **other** Cython classes, like
+> the [`EntityRecognizer`](/api/entityrecognizer) can inherit from it. But it
+> doesn't mean you have to implement trainable components in Cython – pure
+> Python components like the [`TextCategorizer`](/api/textcategorizer) can also
+> inherit from `Pipe`.
 
 ```python
 https://github.com/explosion/spaCy/blob/develop/spacy/pipeline/pipe.pyx
@@ -115,7 +126,8 @@ Initialize the pipe for training, using data examples if available. Returns an
 
 ## Pipe.predict {#predict tag="method"}
 
-Apply the pipeline's model to a batch of docs, without modifying them.
+Apply the component's model to a batch of [`Doc`](/api/doc) objects, without
+modifying them.
 
 <Infobox variant="danger">
 
@@ -137,7 +149,7 @@ This method needs to be overwritten with your own custom `predict` method.
 
 ## Pipe.set_annotations {#set_annotations tag="method"}
 
-Modify a batch of documents, using pre-computed scores.
+Modify a batch of [`Doc`](/api/doc) objects, using pre-computed scores.
 
 <Infobox variant="danger">
 
@@ -161,8 +173,8 @@ method.
 
 ## Pipe.update {#update tag="method"}
 
-Learn from a batch of documents and gold-standard information, updating the
-pipe's model. Delegates to [`predict`](/api/pipe#predict).
+Learn from a batch of [`Example`](/api/example) objects containing the
+predictions and gold-standard annotations, and update the component's model.
 
 <Infobox variant="danger">
 

website/docs/api/sentencerecognizer.md

@@ -136,7 +136,8 @@ Initialize the pipe for training, using data examples if available. Returns an
 
 ## SentenceRecognizer.predict {#predict tag="method"}
 
-Apply the pipeline's model to a batch of docs, without modifying them.
+Apply the component's model to a batch of [`Doc`](/api/doc) objects, without
+modifying them.
 
 > #### Example
 >
@@ -152,7 +153,7 @@ Apply the pipeline's model to a batch of docs, without modifying them.
 
 ## SentenceRecognizer.set_annotations {#set_annotations tag="method"}
 
-Modify a batch of documents, using pre-computed scores.
+Modify a batch of [`Doc`](/api/doc) objects, using pre-computed scores.
 
 > #### Example
 >
@@ -169,8 +170,9 @@ Modify a batch of documents, using pre-computed scores.
 
 ## SentenceRecognizer.update {#update tag="method"}
 
-Learn from a batch of documents and gold-standard information, updating the
-pipe's model. Delegates to [`predict`](/api/sentencerecognizer#predict) and
+Learn from a batch of [`Example`](/api/example) objects containing the
+predictions and gold-standard annotations, and update the component's model.
+Delegates to [`predict`](/api/sentencerecognizer#predict) and
 [`get_loss`](/api/sentencerecognizer#get_loss).
 
 > #### Example

website/docs/api/tagger.md

@@ -134,7 +134,8 @@ Initialize the pipe for training, using data examples if available. Returns an
 
 ## Tagger.predict {#predict tag="method"}
 
-Apply the pipeline's model to a batch of docs, without modifying them.
+Apply the component's model to a batch of [`Doc`](/api/doc) objects, without
+modifying them.
 
 > #### Example
 >
@@ -150,7 +151,7 @@ Apply the pipeline's model to a batch of docs, without modifying them.
 
 ## Tagger.set_annotations {#set_annotations tag="method"}
 
-Modify a batch of documents, using pre-computed scores.
+Modify a batch of [`Doc`](/api/doc) objects, using pre-computed scores.
 
 > #### Example
 >
@@ -167,8 +168,9 @@ Modify a batch of documents, using pre-computed scores.
 
 ## Tagger.update {#update tag="method"}
 
-Learn from a batch of documents and gold-standard information, updating the
-pipe's model. Delegates to [`predict`](/api/tagger#predict) and
+Learn from a batch of [`Example`](/api/example) objects containing the
+predictions and gold-standard annotations, and update the component's model.
+Delegates to [`predict`](/api/tagger#predict) and
 [`get_loss`](/api/tagger#get_loss).
 
 > #### Example

website/docs/api/textcategorizer.md

@@ -142,7 +142,8 @@ Initialize the pipe for training, using data examples if available. Returns an
 
 ## TextCategorizer.predict {#predict tag="method"}
 
-Apply the pipeline's model to a batch of docs, without modifying them.
+Apply the component's model to a batch of [`Doc`](/api/doc) objects, without
+modifying them.
 
 > #### Example
 >
@@ -158,7 +159,7 @@ Apply the pipeline's model to a batch of docs, without modifying them.
 
 ## TextCategorizer.set_annotations {#set_annotations tag="method"}
 
-Modify a batch of documents, using pre-computed scores.
+Modify a batch of [`Doc`](/api/doc) objects, using pre-computed scores.
 
 > #### Example
 >
@@ -175,8 +176,9 @@ Modify a batch of documents, using pre-computed scores.
 
 ## TextCategorizer.update {#update tag="method"}
 
-Learn from a batch of documents and gold-standard information, updating the
-pipe's model. Delegates to [`predict`](/api/textcategorizer#predict) and
+Learn from a batch of [`Example`](/api/example) objects containing the
+predictions and gold-standard annotations, and update the component's model.
+Delegates to [`predict`](/api/textcategorizer#predict) and
 [`get_loss`](/api/textcategorizer#get_loss).
 
 > #### Example

website/docs/api/tok2vec.md

@@ -145,7 +145,8 @@ Initialize the pipe for training, using data examples if available. Returns an
 
 ## Tok2Vec.predict {#predict tag="method"}
 
-Apply the pipeline's model to a batch of docs, without modifying them.
+Apply the component's model to a batch of [`Doc`](/api/doc) objects, without
+modifying them.
 
 > #### Example
 >
@@ -161,7 +162,7 @@ Apply the pipeline's model to a batch of docs, without modifying them.
 
 ## Tok2Vec.set_annotations {#set_annotations tag="method"}
 
-Modify a batch of documents, using pre-computed scores.
+Modify a batch of [`Doc`](/api/doc) objects, using pre-computed scores.
 
 > #### Example
 >
@@ -178,8 +179,9 @@ Modify a batch of documents, using pre-computed scores.
 
 ## Tok2Vec.update {#update tag="method"}
 
-Learn from a batch of documents and gold-standard information, updating the
-pipe's model. Delegates to [`predict`](/api/tok2vec#predict).
+Learn from a batch of [`Example`](/api/example) objects containing the
+predictions and gold-standard annotations, and update the component's model.
+Delegates to [`predict`](/api/tok2vec#predict).
 
 > #### Example
 >

website/docs/api/transformer.md

@@ -179,7 +179,8 @@ Initialize the pipe for training, using data examples if available. Returns an
 
 ## Transformer.predict {#predict tag="method"}
 
-Apply the pipeline's model to a batch of docs, without modifying them.
+Apply the component's model to a batch of [`Doc`](/api/doc) objects, without
+modifying them.
 
 > #### Example
 >