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fix references to TransformerListener

This commit is contained in:
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 start += max_length
end += max_length end += max_length
if start < len(sent): if start < len(sent):
spans[-1].append(sent[start : len(sent)]) spans[-1].append(sent[start:len(sent)])
return spans return spans
return get_custom_sent_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, 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 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 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. delegates to the `Transformer` pipeline component.
```ini ```ini
@ -445,14 +445,14 @@ maxout_pieces = 3
use_upper = false use_upper = false
[nlp.pipeline.ner.model.tok2vec] [nlp.pipeline.ner.model.tok2vec]
@architectures = "spacy-transformers.Tok2VecListener.v1" @architectures = "spacy-transformers.TransformerListener.v1"
grad_factor = 1.0 grad_factor = 1.0
[nlp.pipeline.ner.model.tok2vec.pooling] [nlp.pipeline.ner.model.tok2vec.pooling]
@layers = "reduce_mean.v1" @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 layer](https://thinc.ai/docs/api-layers#reduction-ops) as the argument
`pooling`, which needs to be of type ~~Model[Ragged, Floats2d]~~. This layer `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 determines how the vector for each spaCy token will be computed from the zero or