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svlandeg 2020-08-26 11:51:57 +02:00
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4
website/docs/api/transformer.md
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@ -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#TransformerListener) layer. This
the [TransformerListener](/api/architectures##transformers-Tok2VecListener) 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#TransformerListener) sublayer. If there
[TransformerListener](/api/architectures##transformers-Tok2VecListener) 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.

4
website/docs/usage/embeddings-transformers.md
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@ -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#TransformerListener) layer do the same
[TransformerListener](/api/architectures#transformers-Tok2VecListener) 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,
@ -179,7 +179,7 @@ interoperates with [PyTorch](https://pytorch.org) and the
giving you access to thousands of pretrained models for your pipelines. There
are many [great guides](http://jalammar.github.io/illustrated-transformer/) to
transformer models, but for practical purposes, you can simply think of them as
a drop-in replacement that let you achieve **higher accuracy** in exchange for
drop-in replacements that let you achieve **higher accuracy** in exchange for
**higher training and runtime costs**.
### Setup and installation {#transformers-installation}