document individual component API pages

website/docs/api/dependencyparser.md

@@ -307,6 +307,32 @@ Add a new label to the pipe.
 | `label`     | The label to add. ~~str~~                                   |
 | **RETURNS** | `0` if the label is already present, otherwise `1`. ~~int~~ |
 
+Note that you don't have to call `pipe.add_label` if you provide a
+representative data sample to the [`begin_training`](#begin_training) method. In
+this case, all labels found in the sample will be automatically added to the
+model, and the output dimension will be
+[inferred](/usage/layers-architectures#shape-inference) automatically.
+
+## DependencyParser.set_output {#set_output tag="method"}
+
+Change the output dimension of the component's model by calling the model's
+attribute `resize_output`. This is a function that takes the original model and
+the new output dimension `nO`, and changes the model in place.
+
+> #### Example
+>
+> ```python
+> parser = nlp.add_pipe("parser")
+> parser.set_output(512)
+> ```
+
+| Name | Description                       |
+| ---- | --------------------------------- |
+| `nO` | The new output dimension. ~~int~~ |
+
+When resizing an already trained model, care should be taken to avoid the
+"catastrophic forgetting" problem.
+
 ## DependencyParser.to_disk {#to_disk tag="method"}
 
 Serialize the pipe to disk.

website/docs/api/entityrecognizer.md

@@ -295,6 +295,32 @@ Add a new label to the pipe.
 | `label`     | The label to add. ~~str~~                                   |
 | **RETURNS** | `0` if the label is already present, otherwise `1`. ~~int~~ |
 
+Note that you don't have to call `pipe.add_label` if you provide a
+representative data sample to the [`begin_training`](#begin_training) method. In
+this case, all labels found in the sample will be automatically added to the
+model, and the output dimension will be
+[inferred](/usage/layers-architectures#shape-inference) automatically.
+
+## EntityRecognizer.set_output {#set_output tag="method"}
+
+Change the output dimension of the component's model by calling the model's
+attribute `resize_output`. This is a function that takes the original model and
+the new output dimension `nO`, and changes the model in place.
+
+> #### Example
+>
+> ```python
+> ner = nlp.add_pipe("ner")
+> ner.set_output(512)
+> ```
+
+| Name | Description                       |
+| ---- | --------------------------------- |
+| `nO` | The new output dimension. ~~int~~ |
+
+When resizing an already trained model, care should be taken to avoid the
+"catastrophic forgetting" problem.
+
 ## EntityRecognizer.to_disk {#to_disk tag="method"}
 
 Serialize the pipe to disk.

website/docs/api/morphologizer.md

@@ -258,6 +258,8 @@ context, the original parameters are restored.
 
 Add a new label to the pipe. If the `Morphologizer` should set annotations for
 both `pos` and `morph`, the label should include the UPOS as the feature `POS`.
+Raises an error if the output dimension is already set, or if the model has
+already been fully [initialized](#begin_training).
 
 > #### Example
 >
@@ -271,6 +273,12 @@ both `pos` and `morph`, the label should include the UPOS as the feature `POS`.
 | `label`     | The label to add. ~~str~~                                   |
 | **RETURNS** | `0` if the label is already present, otherwise `1`. ~~int~~ |
 
+Note that you don't have to call `pipe.add_label` if you provide a
+representative data sample to the [`begin_training`](#begin_training) method. In
+this case, all labels found in the sample will be automatically added to the
+model, and the output dimension will be
+[inferred](/usage/layers-architectures#shape-inference) automatically.
+
 ## Morphologizer.to_disk {#to_disk tag="method"}
 
 Serialize the pipe to disk.

website/docs/api/tagger.md

@@ -249,9 +249,9 @@ Score a batch of examples.
 > scores = tagger.score(examples)
 > ```
 
-| Name        | Description                                                                                                                                               |
+| Name        | Description                                                                                                                       |
-| ----------- | --------------------------------------------------------------------------------------------------------------------------------------------------------- |
+| ----------- | --------------------------------------------------------------------------------------------------------------------------------- |
-| `examples`  | The examples to score. ~~Iterable[Example]~~                                                                                                              |
+| `examples`  | The examples to score. ~~Iterable[Example]~~                                                                                      |
 | **RETURNS** | The scores, produced by [`Scorer.score_token_attr`](/api/scorer#score_token_attr) for the attribute `"tag"`. ~~Dict[str, float]~~ |
 
 ## Tagger.create_optimizer {#create_optimizer tag="method"}
@@ -288,7 +288,8 @@ context, the original parameters are restored.
 
 ## Tagger.add_label {#add_label tag="method"}
 
-Add a new label to the pipe.
+Add a new label to the pipe. Raises an error if the output dimension is already
+set, or if the model has already been fully [initialized](#begin_training).
 
 > #### Example
 >
@@ -302,6 +303,12 @@ Add a new label to the pipe.
 | `label`     | The label to add. ~~str~~                                   |
 | **RETURNS** | `0` if the label is already present, otherwise `1`. ~~int~~ |
 
+Note that you don't have to call `pipe.add_label` if you provide a
+representative data sample to the [`begin_training`](#begin_training) method. In
+this case, all labels found in the sample will be automatically added to the
+model, and the output dimension will be
+[inferred](/usage/layers-architectures#shape-inference) automatically.
+
 ## Tagger.to_disk {#to_disk tag="method"}
 
 Serialize the pipe to disk.

website/docs/api/textcategorizer.md

@@ -297,7 +297,8 @@ Modify the pipe's model, to use the given parameter values.
 
 ## TextCategorizer.add_label {#add_label tag="method"}
 
-Add a new label to the pipe.
+Add a new label to the pipe. Raises an error if the output dimension is already
+set, or if the model has already been fully [initialized](#begin_training).
 
 > #### Example
 >
@@ -311,6 +312,12 @@ Add a new label to the pipe.
 | `label`     | The label to add. ~~str~~                                   |
 | **RETURNS** | `0` if the label is already present, otherwise `1`. ~~int~~ |
 
+Note that you don't have to call `pipe.add_label` if you provide a
+representative data sample to the [`begin_training`](#begin_training) method. In
+this case, all labels found in the sample will be automatically added to the
+model, and the output dimension will be
+[inferred](/usage/layers-architectures#shape-inference) automatically.
+
 ## TextCategorizer.to_disk {#to_disk tag="method"}
 
 Serialize the pipe to disk.