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Update TextCategorizer docs

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Ines Montani 2019-02-24 13:11:57 +01:00
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website/docs/api/textcategorizer.md
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@ -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
@ -38,12 +39,27 @@ shortcut for this and instantiate the component using its string name and
> textcat.from_disk("/path/to/model")
> ```
| 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. |
| **RETURNS** | `TextCategorizer` | The newly constructed object. |
| Name | Type | Description |
| ------------------- | ----------------------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------- |
| `vocab` | `Vocab` | The shared vocabulary. |
| `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"}