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small fixes

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svlandeg 2020-08-31 14:46:00 +02:00
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website/docs/api/transformer.md
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@ -102,14 +102,14 @@ attribute. You can also provide a callback to set additional annotations. In
your application, you would normally use a shortcut for this and instantiate the
component using its string name and [`nlp.add_pipe`](/api/language#create_pipe).
| Name | Description |
| ------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `vocab` | The shared vocabulary. ~~Vocab~~ |
| `model` | The Thinc [`Model`](https://thinc.ai/docs/api-model) wrapping the transformer. Usually you will want to use the [TransformerModel](/api/architectures#TransformerModel) layer for this. ~~Model[List[Doc], FullTransformerBatch]~~ |
| `annotation_setter` | Function that takes a batch of `Doc` objects and transformer outputs and stores the annotations on the `Doc`. By default, the function `trfdata_setter` sets the `Doc._.trf_data` attribute. ~~Callable[[List[Doc], FullTransformerBatch], None]~~ |
| _keyword-only_ | |
| `name` | String name of the component instance. Used to add entries to the `losses` during training. ~~str~~ |
| `max_batch_items` | Maximum size of a padded batch. Defaults to `128*32`. ~~int~~ |
| Name | Description |
| ------------------- | --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `vocab` | The shared vocabulary. ~~Vocab~~ |
| `model` | The Thinc [`Model`](https://thinc.ai/docs/api-model) wrapping the transformer. Usually you will want to use the [TransformerModel](/api/architectures#TransformerModel) layer for this. ~~Model[List[Doc], FullTransformerBatch]~~ |
| `annotation_setter` | Function that takes a batch of `Doc` objects and transformer outputs and stores the annotations on the `Doc`. The `Doc._.trf_data` attribute is set prior to calling the callback. By default, no additional annotations are set. ~~Callable[[List[Doc], FullTransformerBatch], None]~~ |
| _keyword-only_ | |
| `name` | String name of the component instance. Used to add entries to the `losses` during training. ~~str~~ |
| `max_batch_items` | Maximum size of a padded batch. Defaults to `128*32`. ~~int~~ |
## Transformer.\_\_call\_\_ {#call tag="method"}
@ -532,7 +532,7 @@ You can register custom annotation setters using the
> def setter(docs: List[Doc], trf_data: FullTransformerBatch) -> None:
> pass
>
> return setter
> return setter
> ```
| Name | Description |