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small fixes

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svlandeg 2020-08-21 18:02:20 +02:00
parent 518a1f97f3
commit c6659e37d8
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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
[`spacy train`](/api/cli#train) command on the command line. It only needs a
single [`config.cfg`](#config) **configuration file** that includes all settings
and hyperparameters. You can optionally [overwritten](#config-overrides)
settings on the command line, and load in a Python file to register
and hyperparameters. You can optionally [overwrite](#config-overrides) settings
on the command line, and load in a Python file to register
[custom functions](#custom-code) and architectures. This quickstart widget helps
you generate a starter config with the **recommended settings** for your
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
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
[`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
@ -121,9 +121,10 @@ Some of the main advantages and features of spaCy's training config are:
functions like [model architectures](/api/architectures),
[optimizers](https://thinc.ai/docs/api-optimizers) or
[schedules](https://thinc.ai/docs/api-schedules) and define arguments that are
passed into them. You can also register your own functions to define
[custom architectures](#custom-functions), reference them in your config and
tweak their parameters.
passed into them. You can also
[register your own functions](#custom-functions) to define custom
architectures or methods, reference them in your config and tweak their
parameters.
- **Interpolation.** If you have hyperparameters or other settings used by
multiple components, define them once and reference them as
[variables](#config-interpolation).