fix references to TransformerListener
@@ -429,7 +429,7 @@ The same idea applies to task models that power the **downstream components**.
Most of spaCy's built-in model creation functions support a `tok2vec` argument,
which should be a Thinc layer of type ~~Model[List[Doc], List[Floats2d]]~~. This
is where we'll plug in our transformer model, using the
-[Tok2VecListener](/api/architectures#Tok2VecListener) layer, which sneakily
+[TransformerListener](/api/architectures#TransformerListener) layer, which sneakily
delegates to the `Transformer` pipeline component.
```ini
@@ -445,14 +445,14 @@ maxout_pieces = 3
use_upper = false
[nlp.pipeline.ner.model.tok2vec]
-@architectures = "spacy-transformers.Tok2VecListener.v1"
+@architectures = "spacy-transformers.TransformerListener.v1"
grad_factor = 1.0
[nlp.pipeline.ner.model.tok2vec.pooling]
@layers = "reduce_mean.v1"
```
-The [Tok2VecListener](/api/architectures#Tok2VecListener) layer expects a
+The [TransformerListener](/api/architectures#TransformerListener) layer expects a
[pooling layer](https://thinc.ai/docs/api-layers#reduction-ops) as the argument
`pooling`, which needs to be of type ~~Model[Ragged, Floats2d]~~. This layer
determines how the vector for each spaCy token will be computed from the zero or
