rename to TransformerListener

website/docs/api/architectures.md

@@ -346,13 +346,13 @@ in other components, see
 | `tokenizer_config` | Tokenizer settings passed to [`transformers.AutoTokenizer`](https://huggingface.co/transformers/model_doc/auto.html#transformers.AutoTokenizer). ~~Dict[str, Any]~~                                                                                   |
 | **CREATES**        | The model using the architecture. ~~Model[List[Doc], FullTransformerBatch]~~                                                                                                                                                                          |
 
-### spacy-transformers.Tok2VecListener.v1 {#transformers-Tok2VecListener}
+### spacy-transformers.TransformerListener.v1 {#TransformerListener}
 
 > #### Example Config
 >
 > ```ini
 > [model]
-> @architectures = "spacy-transformers.Tok2VecListener.v1"
+> @architectures = "spacy-transformers.TransformerListener.v1"
 > grad_factor = 1.0
 >
 > [model.pooling]

website/docs/api/transformer.md

@@ -29,7 +29,7 @@ This pipeline component lets you use transformer models in your pipeline.
 Supports all models that are available via the
 [HuggingFace `transformers`](https://huggingface.co/transformers) library.
 Usually you will connect subsequent components to the shared transformer using
-the [TransformerListener](/api/architectures##transformers-Tok2VecListener) layer. This
+the [TransformerListener](/api/architectures#TransformerListener) layer. This
 works similarly to spaCy's [Tok2Vec](/api/tok2vec) component and
 [Tok2VecListener](/api/architectures/Tok2VecListener) sublayer.
 
@@ -233,7 +233,7 @@ The `Transformer` component therefore does **not** perform a weight update
 during its own `update` method. Instead, it runs its transformer model and
 communicates the output and the backpropagation callback to any **downstream
 components** that have been connected to it via the
-[TransformerListener](/api/architectures##transformers-Tok2VecListener) sublayer. If there
+[TransformerListener](/api/architectures#TransformerListener) sublayer. If there
 are multiple listeners, the last layer will actually backprop to the transformer
 and call the optimizer, while the others simply increment the gradients.
 

website/docs/usage/embeddings-transformers.md

@@ -101,7 +101,7 @@ it processes a batch of documents, it will pass forward its predictions to the
 listeners, allowing the listeners to **reuse the predictions** when they are
 eventually called. A similar mechanism is used to pass gradients from the
 listeners back to the model. The [`Transformer`](/api/transformer) component and
-[TransformerListener](/api/architectures#transformers-Tok2VecListener) layer do the same
+[TransformerListener](/api/architectures#TransformerListener) layer do the same
 thing for transformer models, but the `Transformer` component will also save the
 transformer outputs to the
 [`Doc._.trf_data`](/api/transformer#custom_attributes) extension attribute,

website/docs/usage/v3.md

@@ -64,7 +64,7 @@ menu:
   [`TransformerData`](/api/transformer#transformerdata),
   [`FullTransformerBatch`](/api/transformer#fulltransformerbatch)
 - **Architectures: ** [TransformerModel](/api/architectures#TransformerModel),
-  [Tok2VecListener](/api/architectures#transformers-Tok2VecListener),
+  [TransformerListener](/api/architectures#TransformerListener),
   [Tok2VecTransformer](/api/architectures#Tok2VecTransformer)
 - **Models:** [`en_core_trf_lg_sm`](/models/en)
 - **Implementation:**