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Update docs [ci skip]

This commit is contained in:
Ines Montani 2020-07-29 19:48:26 +02:00
parent 9c80cb673d
commit 3449c45fd9
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13
website/docs/usage/training.md
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@ -243,7 +243,14 @@ compound = 1.001
### Using transformer models like BERT {#transformers}
<!-- TODO: document usage of spacy-transformers, refer to example config/project -->
spaCy v3.0 lets you use almost any statistical model to power your pipeline. You
can use models implemented in a variety of frameworks. A transformer model is
just a statistical model, so the
[`spacy-transformers`](https://github.com/explosion/spacy-transformers) package
actually has very little work to do: it just has to provide a few functions that
do the required plumbing. It also provides a pipeline component,
[`Transformer`](/api/transformer), that lets you do multi-task learning and lets
you save the transformer outputs for later use.
<Project id="en_core_bert">
@ -253,6 +260,10 @@ visualize your model.
</Project>
For more details on how to integrate transformer models into your training
config and customize the implementations, see the usage guide on
[training transformers](/usage/transformers#training).
### Pretraining with spaCy {#pretraining}
<!-- TODO: document spacy pretrain -->

7
website/docs/usage/transformers.md
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@ -18,8 +18,8 @@ frameworks to be wrapped with a common interface, using our machine learning
library [Thinc](https://thinc.ai). A transformer model is just a statistical
model, so the
[`spacy-transformers`](https://github.com/explosion/spacy-transformers) package
actually has very little work to do: we just have to provide a few functions
that do the required plumbing. We also provide a pipeline component,
actually has very little work to do: it just has to provide a few functions that
do the required plumbing. It also provides a pipeline component,
[`Transformer`](/api/transformer), that lets you do multi-task learning and lets
you save the transformer outputs for later use.
@ -201,7 +201,8 @@ def configure_custom_sent_spans():
To resolve the config during training, spaCy needs to know about your custom
function. You can make it available via the `--code` argument that can point to
a Python file:
a Python file. For more details on training with custom code, see the
[training documentation](/usage/training#custom-code).
```bash
$ python -m spacy train ./train.spacy ./dev.spacy ./config.cfg --code ./code.py