From a35236e5f068849ca0b0c70944cd85e14e739dff Mon Sep 17 00:00:00 2001
From: Ines Montani <ines@ines.io>
Date: Mon, 6 Jul 2020 15:57:44 +0200
Subject: [PATCH] Update v3 docs WIP [ci skip]

---
 website/docs/usage/spacy-101.md |  2 +-
 website/docs/usage/training.md  | 51 +++++++++++++++++++++++++--------
 website/meta/site.json          |  2 +-
 website/package.json            |  2 +-
 4 files changed, 42 insertions(+), 15 deletions(-)

diff --git a/website/docs/usage/spacy-101.md b/website/docs/usage/spacy-101.md
index 3c4e85a7d..0cfe404f2 100644
--- a/website/docs/usage/spacy-101.md
+++ b/website/docs/usage/spacy-101.md
@@ -106,7 +106,7 @@ systems, or to pre-process text for **deep learning**.
 
 - **spaCy is not a company**. It's an open-source library. Our company
   publishing spaCy and other software is called
-  [Explosion AI](https://explosion.ai).
+  [Explosion](https://explosion.ai).
 
 ## Features {#features}
 
diff --git a/website/docs/usage/training.md b/website/docs/usage/training.md
index 53b713f98..2bbf5dddd 100644
--- a/website/docs/usage/training.md
+++ b/website/docs/usage/training.md
@@ -36,22 +36,19 @@ 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.
 
-1. The **training data** in spaCy's
-   [binary format](/api/data-formats#binary-training) created using
+1. The **training and evaluation data** in spaCy's
+   [binary `.spacy` format](/api/data-formats#binary-training) created using
    [`spacy convert`](/api/cli#convert).
-2. A `config.cfg` **configuration file** with all settings and hyperparameters.
+2. A [`config.cfg`](#config) **configuration file** with all settings and
+   hyperparameters.
 3. An optional **Python file** to register
    [custom models and architectures](#custom-models).
 
 <!-- TODO: decide how we want to present the "getting started" workflow here, get a default config etc. -->
 
-<Project id="some_example_project">
-
-Lorem ipsum dolor sit amet, consectetur adipiscing elit. Phasellus interdum
-sodales lectus, ut sodales orci ullamcorper id. Sed condimentum neque ut erat
-mattis pretium.
-
-</Project>
+```bash
+$ python -m spacy train train.spacy dev.spacy config.cfg --output ./output
+```
 
 > #### Tip: Debug your data
 >
@@ -60,9 +57,17 @@ mattis pretium.
 > invalid entity annotations, cyclic dependencies, low data labels and more.
 >
 > ```bash
-> $ python -m spacy debug-data en train.json dev.json --verbose
+> $ python -m spacy debug-data en train.spacy dev.spacy --verbose
 > ```
 
+<Project id="some_example_project">
+
+The easiest way to get started with an end-to-end training process is to clone a
+[project](/usage/projects) template. Projects let you manage multi-step
+workflows, from data preprocessing to training and packaging your model.
+
+</Project>
+
 <Accordion title="Understanding the training output">
 
 When you train a model using the [`spacy train`](/api/cli#train) command, you'll
@@ -94,7 +99,28 @@ still look good.
 
 ---
 
-### Training config files {#cli}
+### Training config files {#config}
+
+> #### Migration from spaCy v2.x
+>
+> TODO: ...
+
+Training config files include all **settings and hyperparameters** for training
+your model. Instead of providing lots of arguments on the command line, you only
+need to pass your `config.cfg` file to [`spacy train`](/api/cli#train).
+
+To read more about how the config system works under the hood, check out the
+[Thinc documentation](https://thinc.ai/docs/usage-config).
+
+- **Structured sections.**
+- **References to registered functions.** Sections can refer to registered
+  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-models), reference them in your config,
+- **Interpolation.** If you have hyperparameters used by multiple components,
+  define them once and reference them as variables.
 
 <!-- TODO: we need to come up with a good way to present the sections and their expected values visually? -->
 
@@ -174,6 +200,7 @@ mattis pretium.
 ### Training with custom code
 
 <!-- TODO: document usage of spacy train with --code -->
+<!-- TODO: link to type annotations and maybe show example: https://thinc.ai/docs/usage-config#advanced-types -->
 
 ## Transfer learning {#transfer-learning}
 
diff --git a/website/meta/site.json b/website/meta/site.json
index 724665060..5fb1a4533 100644
--- a/website/meta/site.json
+++ b/website/meta/site.json
@@ -6,7 +6,7 @@
     "siteUrlNightly": "https://nightly.spacy.io",
     "nightlyBranches": ["nightly.spacy.io"],
     "email": "contact@explosion.ai",
-    "company": "Explosion AI",
+    "company": "Explosion",
     "companyUrl": "https://explosion.ai",
     "repo": "explosion/spaCy",
     "modelsRepo": "explosion/spacy-models",
diff --git a/website/package.json b/website/package.json
index d5c770ddf..3c76014b3 100644
--- a/website/package.json
+++ b/website/package.json
@@ -3,7 +3,7 @@
     "private": true,
     "description": "spaCy website",
     "version": "3.0.0",
-    "author": "Explosion AI <contact@explosion.ai>",
+    "author": "Explosion <contact@explosion.ai>",
     "license": "MIT",
     "dependencies": {
         "@jupyterlab/outputarea": "^0.19.1",