Update docs [ci skip]

This commit is contained in:
Ines Montani 2020-07-29 19:48:26 +02:00
parent 9c80cb673d
commit 3449c45fd9
2 changed files with 16 additions and 4 deletions

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@ -243,7 +243,14 @@ compound = 1.001
### Using transformer models like BERT {#transformers} ### 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"> <Project id="en_core_bert">
@ -253,6 +260,10 @@ visualize your model.
</Project> </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} ### Pretraining with spaCy {#pretraining}
<!-- TODO: document spacy pretrain --> <!-- TODO: document spacy pretrain -->

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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 library [Thinc](https://thinc.ai). A transformer model is just a statistical
model, so the model, so the
[`spacy-transformers`](https://github.com/explosion/spacy-transformers) package [`spacy-transformers`](https://github.com/explosion/spacy-transformers) package
actually has very little work to do: we just have to provide a few functions actually has very little work to do: it just has to provide a few functions that
that do the required plumbing. We also provide a pipeline component, do the required plumbing. It also provides a pipeline component,
[`Transformer`](/api/transformer), that lets you do multi-task learning and lets [`Transformer`](/api/transformer), that lets you do multi-task learning and lets
you save the transformer outputs for later use. 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 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 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 ```bash
$ python -m spacy train ./train.spacy ./dev.spacy ./config.cfg --code ./code.py $ python -m spacy train ./train.spacy ./dev.spacy ./config.cfg --code ./code.py