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website/docs/api/curatedtransformer.mdx
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@ -78,7 +78,7 @@ below).
| Setting | Description |
| ------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `model` | The Thinc [`Model`](https://thinc.ai/docs/api-model) wrapping the transformer. Defaults to [CuratedTransformerModel](/api/architectures#CuratedTransformerModel). ~~Model[List[Doc], FullCuratedTransformerBatch]~~ |
| `model` | The Thinc [`Model`](https://thinc.ai/docs/api-model) wrapping the transformer. Defaults to [`XlmrTransformer`](/api/architectures#curated-trf). ~~Model~~ |
| `frozen` | If `True`, the model's weights are frozen and no backpropagation is performed. ~~bool~~ |
| `all_layer_outputs` | If `True`, the model returns the outputs of all the layers. Otherwise, only the output of the last layer is returned. This must be set to `True` if any of the pipe's downstream listeners require the outputs of all transformer layers. ~~bool~~ |
@ -135,9 +135,10 @@ component using its string name and [`nlp.add_pipe`](/api/language#create_pipe).
Apply the pipe to one document. The document is modified in place, and returned.
This usually happens under the hood when the `nlp` object is called on a text
and all pipeline components are applied to the `Doc` in order. Both
[`__call__`](/api/transformer#call) and [`pipe`](/api/transformer#pipe) delegate
to the [`predict`](/api/transformer#predict) and
[`set_annotations`](/api/transformer#set_annotations) methods.
[`__call__`](/api/curatedtransformer#call) and
[`pipe`](/api/curatedtransformer#pipe) delegate to the
[`predict`](/api/curatedtransformer#predict) and
[`set_annotations`](/api/curatedtransformer#set_annotations) methods.
> #### Example
>
@ -157,10 +158,10 @@ to the [`predict`](/api/transformer#predict) and
Apply the pipe to a stream of documents. This usually happens under the hood
when the `nlp` object is called on a text and all pipeline components are
applied to the `Doc` in order. Both [`__call__`](/api/transformer#call) and
[`pipe`](/api/transformer#pipe) delegate to the
[`predict`](/api/transformer#predict) and
[`set_annotations`](/api/transformer#set_annotations) methods.
applied to the `Doc` in order. Both [`__call__`](/api/curatedtransformer#call)
and [`pipe`](/api/curatedtransformer#pipe) delegate to the
[`predict`](/api/curatedtransformer#predict) and
[`set_annotations`](/api/curatedtransformer#set_annotations) methods.
> #### Example
>
@ -402,10 +403,11 @@ serialization by passing in the string names via the `exclude` argument.
CuratedTransformer tokens and outputs for one `Doc` object. The transformer
models return tensors that refer to a whole padded batch of documents. These
tensors are wrapped into the
[FullCuratedTransformerBatch](/api/transformer#fulltransformerbatch) object. The
`FullCuratedTransformerBatch` then splits out the per-document data, which is
handled by this class. Instances of this class are typically assigned to the
[`Doc._.trf_data`](/api/transformer#assigned-attributes) extension attribute.
[FullCuratedTransformerBatch](/api/curatedtransformer#fulltransformerbatch)
object. The `FullCuratedTransformerBatch` then splits out the per-document data,
which is handled by this class. Instances of this class are typically assigned
to the [`Doc._.trf_data`](/api/curatedtransformer#assigned-attributes) extension
attribute.
| Name | Description |
| -------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |