Update TextCategorizer docs
@@ -31,6 +31,7 @@ shortcut for this and instantiate the component using its string name and
> ```python
> # Construction via create_pipe
> textcat = nlp.create_pipe("textcat")
+> textcat = nlp.create_pipe("textcat", config={"exclusive_classes": True})
>
> # Construction from class
> from spacy.pipeline import TextCategorizer
@@ -39,12 +40,27 @@ shortcut for this and instantiate the component using its string name and
> ```
| Name | Type | Description |
-| ----------- | ------------------------------ | ----------------------------------------------------------------------------------------------------------------------------------------------------- |
+| ------------------- | ----------------------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------- |
| `vocab` | `Vocab` | The shared vocabulary. |
-| `model` | `thinc.neural.Model` or `True` | The model powering the pipeline component. If no model is supplied, the model is created when you call `begin_training`, `from_disk` or `from_bytes`. |
-| `**cfg` | - | Configuration parameters. |
+| `model` | `thinc.neural.Model` / `True` | The model powering the pipeline component. If no model is supplied, the model is created when you call `begin_training`, `from_disk` or `from_bytes`. |
+| `exclusive_classes` | bool | Make categories mutually exclusive. Defaults to `False`. |
+| `architecture` | unicode | Model architecture to use, see [architectures](#architectures) for details. Defaults to `"ensemble"`. |
| **RETURNS** | `TextCategorizer` | The newly constructed object. |
+### Architectures {#architectures new="2.1"}
+
+Text classification models can be used to solve a wide variety of problems.
+Differences in text length, number of labels, difficulty, and runtime
+performance constraints mean that no single algorithm performs well on all types
+of problems. To handle a wider variety of problems, the `TextCategorizer` object
+allows configuration of its model architecture, using the `architecture` keyword
+argument.
+
+| Name | Description |
+| -------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------- |
+| `"ensemble"` | **Default:** Stacked ensemble of a unigram bag-of-words model and a neural network model. The neural network uses a CNN with mean pooling and attention. |
+| `"simple_cnn"` | A neural network model where token vectors are calculated using a CNN. The vectors are mean pooled and used as features in a feed-forward network. |
+
## TextCategorizer.\_\_call\_\_ {#call tag="method"}
Apply the pipe to one document. The document is modified in place, and returned.
