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fix references to TransformerListener

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svlandeg 2020-08-27 14:33:28 +02:00
parent 4d37ac3f33
commit 28e4ba7270
1 changed files with 4 additions and 4 deletions
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website/docs/usage/embeddings-transformers.md
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@ -399,7 +399,7 @@ def configure_custom_sent_spans(max_length: int):
start += max_length
end += max_length
if start < len(sent):
spans[-1].append(sent[start : len(sent)])
spans[-1].append(sent[start:len(sent)])
return spans
return get_custom_sent_spans
@ -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