Update fastText example and add to examples in docs

examples/vectors_fast_text.py

@@ -1,6 +1,6 @@
 #!/usr/bin/env python
 # coding: utf8
-"""Load vectors for a language trained using FastText
+"""Load vectors for a language trained using fastText
 https://github.com/facebookresearch/fastText/blob/master/pretrained-vectors.md
 """
 from __future__ import unicode_literals
@@ -10,7 +10,8 @@ import numpy
 import from spacy.language import Language
 
 
-@plac.annotations(vectors_loc=("Path to vectors", "positional", None, str))
+@plac.annotations(
+    vectors_loc=("Path to vectors", "positional", None, str))
 def main(vectors_loc):
     nlp = Language()
 

website/usage/_data.json

@@ -199,6 +199,7 @@
             "Information Extraction": "information-extraction",
             "Pipeline": "pipeline",
             "Training": "training",
+            "Vectors & Similarity": "vectors",
             "Deep Learning": "deep-learning"
         }
     }

website/usage/examples.jade

@@ -119,6 +119,18 @@ include ../_includes/_mixins
 
     +github("spacy", "examples/training/train_textcat.py")
 
++section("vectors")
+    +h(3, "fasttext") Loading pre-trained FastText vectors
+
+    p
+        |  This simple snippet is all you need to be able to use the Facebook's
+        |  #[+a("https://github.com/facebookresearch/fastText/blob/master/pretrained-vectors.md") fastText vectors]
+        |  (294 languages, pre-trained on Wikipedia) with spaCy.  Once they're
+        |  loaded, the vectors will be available via spaCy's built-in
+        |  #[code similarity()] methods.
+
+    +github("spacy", "examples/vectors_fast_text.py")
+
 +section("deep-learning")
     +h(3, "keras") Text classification with Keras