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svlandeg 2020-08-26 11:51:57 +02:00
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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 Supports all models that are available via the
[HuggingFace `transformers`](https://huggingface.co/transformers) library. [HuggingFace `transformers`](https://huggingface.co/transformers) library.
Usually you will connect subsequent components to the shared transformer using 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 works similarly to spaCy's [Tok2Vec](/api/tok2vec) component and
[Tok2VecListener](/api/architectures/Tok2VecListener) sublayer. [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 during its own `update` method. Instead, it runs its transformer model and
communicates the output and the backpropagation callback to any **downstream communicates the output and the backpropagation callback to any **downstream
components** that have been connected to it via the 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 are multiple listeners, the last layer will actually backprop to the transformer
and call the optimizer, while the others simply increment the gradients. and call the optimizer, while the others simply increment the gradients.

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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 listeners, allowing the listeners to **reuse the predictions** when they are
eventually called. A similar mechanism is used to pass gradients from the eventually called. A similar mechanism is used to pass gradients from the
listeners back to the model. The [`Transformer`](/api/transformer) component and 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 thing for transformer models, but the `Transformer` component will also save the
transformer outputs to the transformer outputs to the
[`Doc._.trf_data`](/api/transformer#custom_attributes) extension attribute, [`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 giving you access to thousands of pretrained models for your pipelines. There
are many [great guides](http://jalammar.github.io/illustrated-transformer/) to are many [great guides](http://jalammar.github.io/illustrated-transformer/) to
transformer models, but for practical purposes, you can simply think of them as 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**. **higher training and runtime costs**.
### Setup and installation {#transformers-installation} ### Setup and installation {#transformers-installation}