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