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svlandeg 2020-08-21 18:02:20 +02:00
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website/docs/usage/training.md
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@ -35,8 +35,8 @@ ready-to-use spaCy models.
The recommended way to train your spaCy models is via the The recommended way to train your spaCy models is via the
[`spacy train`](/api/cli#train) command on the command line. It only needs a [`spacy train`](/api/cli#train) command on the command line. It only needs a
single [`config.cfg`](#config) **configuration file** that includes all settings single [`config.cfg`](#config) **configuration file** that includes all settings
and hyperparameters. You can optionally [overwritten](#config-overrides) and hyperparameters. You can optionally [overwrite](#config-overrides) settings
settings on the command line, and load in a Python file to register on the command line, and load in a Python file to register
[custom functions](#custom-code) and architectures. This quickstart widget helps [custom functions](#custom-code) and architectures. This quickstart widget helps
you generate a starter config with the **recommended settings** for your you generate a starter config with the **recommended settings** for your
specific use case. It's also available in spaCy as the specific use case. It's also available in spaCy as the
@ -82,7 +82,7 @@ $ python -m spacy init fill-config base_config.cfg config.cfg
Instead of exporting your starter config from the quickstart widget and Instead of exporting your starter config from the quickstart widget and
auto-filling it, you can also use the [`init config`](/api/cli#init-config) auto-filling it, you can also use the [`init config`](/api/cli#init-config)
command and specify your requirement and settings and CLI arguments. You can now command and specify your requirement and settings as CLI arguments. You can now
add your data and run [`train`](/api/cli#train) with your config. See the add your data and run [`train`](/api/cli#train) with your config. See the
[`convert`](/api/cli#convert) command for details on how to convert your data to [`convert`](/api/cli#convert) command for details on how to convert your data to
spaCy's binary `.spacy` format. You can either include the data paths in the spaCy's binary `.spacy` format. You can either include the data paths in the
@ -121,9 +121,10 @@ Some of the main advantages and features of spaCy's training config are:
functions like [model architectures](/api/architectures), functions like [model architectures](/api/architectures),
[optimizers](https://thinc.ai/docs/api-optimizers) or [optimizers](https://thinc.ai/docs/api-optimizers) or
[schedules](https://thinc.ai/docs/api-schedules) and define arguments that are [schedules](https://thinc.ai/docs/api-schedules) and define arguments that are
passed into them. You can also register your own functions to define passed into them. You can also
[custom architectures](#custom-functions), reference them in your config and [register your own functions](#custom-functions) to define custom
tweak their parameters. architectures or methods, reference them in your config and tweak their
parameters.
- **Interpolation.** If you have hyperparameters or other settings used by - **Interpolation.** If you have hyperparameters or other settings used by
multiple components, define them once and reference them as multiple components, define them once and reference them as
[variables](#config-interpolation). [variables](#config-interpolation).