Update 101 and usage docs

website/assets/img/docs/pipeline.svg

@@ -2,7 +2,7 @@
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         .svg__pipeline__text-small { fill: #1a1e23; font: bold 18px "Source Sans Pro" }
-        .svg__pipeline__text-code {  fill: #1a1e23; font: 600 16px "Source Code Pro" }
+        .svg__pipeline__text-code { fill: #1a1e23; font: 600 16px "Source Code Pro" }
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website/docs/usage/_spacy-101/_vocab-stringstore.jade

@@ -89,4 +89,6 @@ p
 
 p
     |  Even though both #[code Doc] objects contain the same words, the internal
-    |  integer IDs are very different.
+    |  integer IDs are very different. The same applies for all other strings,
+    |  like the annotation scheme. To avoid mismatched IDs, spaCy will always
+    |  export the vocab if you save a #[code Doc] or #[code nlp] object.

website/docs/usage/lightning-tour.jade

@@ -139,6 +139,8 @@ p
     new_doc = Doc(Vocab()).from_disk('/moby_dick.bin')
 
 +infobox
+    |  #[strong API:] #[+api("language") #[code Language]],
+    |  #[+api("doc") #[code Doc]]
     |  #[strong Usage:] #[+a("/docs/usage/saving-loading") Saving and loading]
 
 +h(2, "rule-matcher") Match text with token rules

website/docs/usage/rule-based-matching.jade

@@ -345,7 +345,7 @@ p
     |  account and check the #[code subtree] for intensifiers like "very", to
     |  increase the sentiment score. At some point, you might also want to train
     |  a sentiment model. However, the approach described in this example is
-    |  very useful for #[strong bootstrapping rules to gather training data].
+    |  very useful for #[strong bootstrapping rules to collect training data].
     |  It's also an incredibly fast way to gather first insights into your data
     |  – with about 1 million tweets, you'd be looking at a processing time of
     |  #[strong under 1 minute].