Port over docs changes [ci skip]

website/api/_top-level/_displacy.jade

@@ -208,7 +208,7 @@ p
     +row
         +cell #[code word_spacing]
         +cell int
-        +cell Horizontal spacing between words and arcs in px.
+        +cell Vertical spacing between words and arcs in px.
         +cell #[code 45]
 
     +row

website/api/doc.jade

@@ -674,7 +674,7 @@ p
     |  token vectors.
 
 +aside-code("Example").
-    apples = nlp(u'I like apples')
+    doc = nlp(u'I like apples')
     assert doc.vector.dtype == 'float32'
     assert doc.vector.shape == (300,)
 

website/api/goldcorpus.jade

@@ -12,11 +12,24 @@ p Create a #[code GoldCorpus].
 
 +table(["Name", "Type", "Description"])
     +row
-        +cell #[code train_path]
-        +cell unicode or #[code Path]
-        +cell File or directory of training data.
+        +cell #[code train]
+        +cell unicode or #[code Path] or iterable
+        +cell
+            |  Training data, as a path (file or directory) or iterable. If an
+            |  iterable, each item should be a #[code (text, paragraphs)]
+            |  tuple, where each paragraph is a tuple
+            |  #[code.u-break (sentences, brackets)],and each sentence is a
+            |  tuple #[code.u-break (ids, words, tags, heads, ner)]. See the
+            |  implementation of
+            |  #[+src(gh("spacy", "spacy/gold.pyx")) #[code gold.read_json_file]]
+            |  for further details.
 
     +row
-        +cell #[code dev_path]
-        +cell unicode or #[code Path]
-        +cell File or directory of development data.
+        +cell #[code dev]
+        +cell unicode or #[code Path] or iterable
+        +cell Development data, as a path (file or directory) or iterable.
+
+    +row("foot")
+        +cell returns
+        +cell #[code GoldCorpus]
+        +cell The newly constructed object.

website/api/lexeme.jade

@@ -325,6 +325,12 @@ p The L2 norm of the lexeme's vector representation.
         +cell bool
         +cell Is the lexeme a quotation mark?
 
+    +row
+        +cell #[code is_currency]
+            +tag-new("2.0.8")
+        +cell bool
+        +cell Is the lexeme a currency symbol?
+
     +row
         +cell #[code like_url]
         +cell bool

website/api/matcher.jade

@@ -111,6 +111,25 @@ p Match a stream of documents, yielding them in turn.
             |  parallel, if the #[code Matcher] implementation supports
             |  multi-threading.
 
+    +row
+        +cell #[code return_matches]
+            +tag-new(2.1)
+        +cell bool
+        +cell
+            |  Yield the match lists along with the docs, making results
+            |  #[code (doc, matches)] tuples.
+
+    +row
+        +cell #[code as_tuples]
+            +tag-new(2.1)
+        +cell bool
+        +cell
+            |  Interpret the input stream as #[code (doc, context)] tuples, and
+            |  yield #[code (result, context)] tuples out. If both
+            |  #[code return_matches] and #[code as_tuples] are #[code True],
+            |  the output will be a sequence of
+            |  #[code ((doc, matches), context)] tuples.
+
     +row("foot")
         +cell yields
         +cell #[code Doc]

website/api/pipe.jade

@@ -209,7 +209,7 @@ p
 
     +row
         +cell #[code drop]
-        +cell int
+        +cell float
         +cell The dropout rate.
 
     +row

website/api/token.jade

@@ -740,6 +740,12 @@ p The L2 norm of the token's vector representation.
         +cell bool
         +cell Is the token a quotation mark?
 
+    +row
+        +cell #[code is_currency]
+            +tag-new("2.0.8")
+        +cell bool
+        +cell Is the token a currency symbol?
+
     +row
         +cell #[code like_url]
         +cell bool

website/models/_data.json

@@ -76,13 +76,15 @@
     },
 
     "MODEL_LICENSES": {
-        "CC BY-SA":     "https://creativecommons.org/licenses/by-sa/3.0/",
-        "CC BY-SA 3.0": "https://creativecommons.org/licenses/by-sa/3.0/",
-        "CC BY-SA 4.0": "https://creativecommons.org/licenses/by-sa/4.0/",
-        "CC BY-NC":     "https://creativecommons.org/licenses/by-nc/3.0/",
-        "CC BY-NC 3.0": "https://creativecommons.org/licenses/by-nc/3.0/",
-        "GPL":          "https://www.gnu.org/licenses/gpl.html",
-        "LGPL":         "https://www.gnu.org/licenses/lgpl.html"
+        "CC BY 4.0":       "https://creativecommons.org/licenses/by/4.0/",
+        "CC BY-SA":        "https://creativecommons.org/licenses/by-sa/3.0/",
+        "CC BY-SA 3.0":    "https://creativecommons.org/licenses/by-sa/3.0/",
+        "CC BY-SA 4.0":    "https://creativecommons.org/licenses/by-sa/4.0/",
+        "CC BY-NC":        "https://creativecommons.org/licenses/by-nc/3.0/",
+        "CC BY-NC 3.0":    "https://creativecommons.org/licenses/by-nc/3.0/",
+        "CC-BY-NC-SA 3.0": "https://creativecommons.org/licenses/by-nc-sa/3.0/",
+        "GPL":             "https://www.gnu.org/licenses/gpl.html",
+        "LGPL":            "https://www.gnu.org/licenses/lgpl.html"
     },
 
     "MODEL_BENCHMARKS": {

website/usage/_processing-pipelines/_pipelines.jade

@@ -40,7 +40,7 @@ p
     +item
         |  Make the #[strong model data] available to the #[code Language] class
         |  by calling #[+api("language#from_disk") #[code from_disk]] with the
-        |  path to the model data ditectory.
+        |  path to the model data directory.
 
 p
     |  So when you call this...
@@ -53,7 +53,7 @@ p
     |  pipeline #[code.u-break ["tagger", "parser", "ner"]]. spaCy will then
     |  initialise #[code spacy.lang.en.English], and create each pipeline
     |  component and add it to the processing pipeline. It'll then load in the
-    |  model's data from its data ditectory and return the modified
+    |  model's data from its data directory and return the modified
     |  #[code Language] class for you to use as the #[code nlp] object.
 
 p

website/usage/_spacy-101/_similarity.jade

@@ -37,7 +37,7 @@ p
             +cell.u-text-label.u-color-theme=label
             for cell in cells
                 +cell.u-text-center
-                    - var result = cell > 0.5 ? ["yes", "similar"] : cell != 1 ? ["no", "dissimilar"] : ["neutral", "identical"]
+                    - var result = cell < 0.5 ? ["no", "dissimilar"] : cell != 1 ? ["yes", "similar"] : ["neutral", "identical"]
                     |  #[code=cell.toFixed(2)] #[+procon(...result)]
 
 p

website/usage/_v2/_features.jade

@@ -163,7 +163,7 @@ p
     nlp = English().from_disk('/path/to/nlp')
 
 p
-    |  spay's serialization API has been made consistent across classes and
+    |  spaCy's serialization API has been made consistent across classes and
     |  objects. All container classes, i.e. #[code Language], #[code Doc],
     |  #[code Vocab] and #[code StringStore] now have a #[code to_bytes()],
     |  #[code from_bytes()], #[code to_disk()] and #[code from_disk()] method

website/usage/spacy-101.jade

@@ -68,7 +68,7 @@ p
     +item #[strong spaCy is not research software].
         |  It's built on the latest research, but it's designed to get
         |  things done. This leads to fairly different design decisions than
-        |  #[+a("https://github./nltk/nltk") NLTK]
+        |  #[+a("https://github.com/nltk/nltk") NLTK]
         |  or #[+a("https://stanfordnlp.github.io/CoreNLP/") CoreNLP], which were
         |  created as platforms for teaching and research. The main difference
         |  is that spaCy is integrated and opinionated. spaCy tries to avoid asking