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small fixes
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@ -35,8 +35,8 @@ ready-to-use spaCy models.
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The recommended way to train your spaCy models is via the
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[`spacy train`](/api/cli#train) command on the command line. It only needs a
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single [`config.cfg`](#config) **configuration file** that includes all settings
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and hyperparameters. You can optionally [overwritten](#config-overrides)
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settings on the command line, and load in a Python file to register
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and hyperparameters. You can optionally [overwrite](#config-overrides) settings
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on the command line, and load in a Python file to register
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[custom functions](#custom-code) and architectures. This quickstart widget helps
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you generate a starter config with the **recommended settings** for your
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specific use case. It's also available in spaCy as the
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@ -82,7 +82,7 @@ $ python -m spacy init fill-config base_config.cfg config.cfg
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Instead of exporting your starter config from the quickstart widget and
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auto-filling it, you can also use the [`init config`](/api/cli#init-config)
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command and specify your requirement and settings and CLI arguments. You can now
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command and specify your requirement and settings as CLI arguments. You can now
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add your data and run [`train`](/api/cli#train) with your config. See the
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[`convert`](/api/cli#convert) command for details on how to convert your data to
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spaCy's binary `.spacy` format. You can either include the data paths in the
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@ -121,9 +121,10 @@ Some of the main advantages and features of spaCy's training config are:
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functions like [model architectures](/api/architectures),
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[optimizers](https://thinc.ai/docs/api-optimizers) or
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[schedules](https://thinc.ai/docs/api-schedules) and define arguments that are
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passed into them. You can also register your own functions to define
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[custom architectures](#custom-functions), reference them in your config and
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tweak their parameters.
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passed into them. You can also
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[register your own functions](#custom-functions) to define custom
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architectures or methods, reference them in your config and tweak their
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parameters.
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- **Interpolation.** If you have hyperparameters or other settings used by
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multiple components, define them once and reference them as
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[variables](#config-interpolation).
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