From c6659e37d8a1d3754c52f2333631e75fcc96720e Mon Sep 17 00:00:00 2001 From: svlandeg Date: Fri, 21 Aug 2020 18:02:20 +0200 Subject: [PATCH] small fixes --- website/docs/usage/training.md | 13 +++++++------ 1 file changed, 7 insertions(+), 6 deletions(-) diff --git a/website/docs/usage/training.md b/website/docs/usage/training.md index 5f188a75a..d738cfd04 100644 --- a/website/docs/usage/training.md +++ b/website/docs/usage/training.md @@ -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).