Update docs to reflect flattened model meta.json

Don't use "setup" key and instead, keep "lang" on root level and add
"pipeline".

website/docs/usage/language-processing-pipeline.jade

@@ -19,19 +19,17 @@ p
 
 p
     |  When you load a model, spaCy first consults the model's
-    |  #[+a("/docs/usage/saving-loading#models-generating") meta.json] for its
+    |  #[+a("/docs/usage/saving-loading#models-generating") meta.json]. The
-    |  #[code setup] details. This typically includes the ID of a language class,
+    |  meta typically includes the model details, the ID of a language class,
     |  and an optional list of pipeline components. spaCy then does the
     |  following:
 
 +aside-code("meta.json (excerpt)", "json").
     {
         "name": "example_model",
+        "lang": "en"
         "description": "Example model for spaCy",
-        "setup": {
+        "pipeline": ["token_vectors", "tagger"]
-            "lang": "en",
-            "pipeline": ["token_vectors", "tagger"]
-        }
     }
 
 +list("numbers")
@@ -287,17 +285,15 @@ p
 
 p
     |  In the model package's meta.json, specify the language class and pipeline
-    |  IDs in #[code setup]:
+    |  IDs:
 
 +code("meta.json (excerpt)", "json").
     {
-        "name": "my_sentiment_model",
+        "name": "sentiment_model",
+        "lang": "en",
         "version": "1.0.0",
         "spacy_version": ">=2.0.0,<3.0.0",
-        "setup": {
+        "pipeline": ["vectorizer", "sentiment"]
-            "lang": "en",
-            "pipeline": ["vectorizer", "sentiment"]
-        }
     }
 
 p
@@ -307,7 +303,7 @@ p
     |  by your custom #[code "sentiment"] factory.
 
 +code.
-    nlp = spacy.load('my_sentiment_model')
+    nlp = spacy.load('en_sentiment_model')
     doc = nlp(u'I love pizza')
     assert doc.sentiment
 

website/docs/usage/saving-loading.jade

@@ -74,16 +74,14 @@ p
 +aside-code("meta.json", "json").
     {
         "name": "example_model",
+        "lang": "en",
         "version": "1.0.0",
         "spacy_version": ">=2.0.0,<3.0.0",
         "description": "Example model for spaCy",
         "author": "You",
         "email": "you@example.com",
         "license": "CC BY-SA 3.0",
-        "setup": {
+        "pipeline": ["token_vectors", "tagger"]
-            "lang": "en",
-            "pipeline": ["token_vectors", "tagger"]
-        }
     }
 
 +code(false, "bash").
@@ -110,9 +108,9 @@ p
 +h(3, "models-custom") Customising the model setup
 
 p
-    |  The meta.json includes a #[code setup] key that lets you customise how
+    |  The meta.json includes the model details, like name, requirements and
-    |  the model should be initialised and loaded. You can define the language
+    |  license, and lets you customise how the model should be initialised and
-    |  data to be loaded and the
+    |  loaded. You can define the language data to be loaded and the
     |  #[+a("/docs/usage/language-processing-pipeline") processing pipeline] to
     |  execute.
 
@@ -183,9 +181,9 @@ p
 p
     |  To load a model from a data directory, you can use
     |  #[+api("spacy#load") #[code spacy.load()]] with the local path. This will
-    |  look for a meta.json in the directory and use the #[code setup] details
+    |  look for a meta.json in the directory and use the #[code lang] and
-    |  to initialise a #[code Language] class with a processing pipeline and
+    |  #[code pipeline] settings to initialise a #[code Language] class with a
-    |  load in the model data.
+    |  processing pipeline and load in the model data.
 
 +code.
     nlp = spacy.load('/path/to/model')