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Add note about updating with fill-config

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Adriane Boyd 2021-06-29 10:45:36 +02:00
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website/docs/usage/v3-1.md
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@ -24,10 +24,10 @@ By default, components are updated in isolation during training, which means
that they don't see the predictions of any earlier components in the pipeline.
The new
[`[training.annotating_components]`](/usage/training#annotating-components)
config setting lets you specify pipeline components that should set
annotations on the predicted docs during training. This makes it easy to use the
predictions of a previous component in the pipeline as features for a subsequent
component, e.g. the dependency labels in the tagger:
config setting lets you specify pipeline components that should set annotations
on the predicted docs during training. This makes it easy to use the predictions
of a previous component in the pipeline as features for a subsequent component,
e.g. the dependency labels in the tagger:
```ini
### config.cfg (excerpt) {highlight="7,12"}
@ -228,6 +228,19 @@ working as expected, you can update the spaCy version requirements in the
+ "spacy_version": ">=3.0.0,<3.2.0",
```
### Updating v3.0 configs
To update a config from spaCy v3.0 with the new v3.1 settings, run
[`init fill-config`](/api/cli#init-fill-config):
```bash
python -m spacy init fill-config config-v3.0.cfg config-v3.1.cfg
```
In many cases (`spacy train`, `spacy.load()`), the new defaults will be filled
in automatically, but you'll need to fill in the new settings to run
[`debug config`](/api/cli#debug) and [`debug data`](/api/cli#debug-data).
### Sourcing pipeline components with vectors {#source-vectors}
If you're sourcing a pipeline component that requires static vectors (for