Fix formatting

examples/information_extraction/entity_relations.py

@@ -1,7 +1,6 @@
 #!/usr/bin/env python
 # coding: utf8
-"""
+"""A simple example of extracting relations between phrases and entities using
-A simple example of extracting relations between phrases and entities using
 spaCy's named entity recognizer and the dependency parse. Here, we extract
 money and currency values (entities labelled as MONEY) and then check the
 dependency tree to find the noun phrase they are referring to – for example:

examples/information_extraction/parse_subtrees.py

@@ -1,8 +1,7 @@
 #!/usr/bin/env python
 # coding: utf8
-"""
+"""This example shows how to navigate the parse tree including subtrees
-This example shows how to navigate the parse tree including subtrees attached
+attached to a word.
-to a word.
 
 Based on issue #252:
 "In the documents and tutorials the main thing I haven't found is

examples/information_extraction/phrase_matcher.py

@@ -1,9 +1,10 @@
+#!/usr/bin/env python
+# coding: utf8
 """Match a large set of multi-word expressions in O(1) time.
 
 The idea is to associate each word in the vocabulary with a tag, noting whether
 they begin, end, or are inside at least one pattern. An additional tag is used
 for single-word patterns. Complete patterns are also stored in a hash set.
-
 When we process a document, we look up the words in the vocabulary, to
 associate the words with the tags.  We then search for tag-sequences that
 correspond to valid candidates. Finally, we look up the candidates in the hash

examples/pipeline/multi_processing.py

@@ -1,5 +1,6 @@
-"""
+#!/usr/bin/env python
-Example of multi-processing with Joblib. Here, we're exporting
+# coding: utf8
+"""Example of multi-processing with Joblib. Here, we're exporting
 part-of-speech-tagged, true-cased, (very roughly) sentence-separated text, with
 each "sentence" on a newline, and spaces between tokens. Data is loaded from
 the IMDB movie reviews dataset and will be loaded automatically via Thinc's

examples/training/train_ner.py

@@ -1,7 +1,6 @@
 #!/usr/bin/env python
 # coding: utf8
-"""
+"""Example of training spaCy's named entity recognizer, starting off with an
-Example of training spaCy's named entity recognizer, starting off with an
 existing model or a blank model.
 
 For more details, see the documentation:

examples/training/train_new_entity_type.py

@@ -1,7 +1,6 @@
 #!/usr/bin/env python
 # coding: utf8
-"""
+"""Example of training an additional entity type
-Example of training an additional entity type
 
 This script shows how to add a new entity type to an existing pre-trained NER
 model. To keep the example short and simple, only four sentences are provided

examples/training/train_parser.py

@@ -1,10 +1,7 @@
 #!/usr/bin/env python
 # coding: utf8
-"""
+"""Example of training spaCy dependency parser, starting off with an existing
-Example of training spaCy dependency parser, starting off with an existing model
+model or a blank model. For more details, see the documentation:
-or a blank model.
-
-For more details, see the documentation:
 * Training: https://alpha.spacy.io/usage/training
 * Dependency Parse: https://alpha.spacy.io/usage/linguistic-features#dependency-parse
 

examples/training/train_tagger.py

@@ -3,9 +3,8 @@
 """
 A simple example for training a part-of-speech tagger with a custom tag map.
 To allow us to update the tag map with our custom one, this example starts off
-with a blank Language class and modifies its defaults.
+with a blank Language class and modifies its defaults. For more details, see
-
+the documentation:
-For more details, see the documentation:
 * Training: https://alpha.spacy.io/usage/training
 * POS Tagging: https://alpha.spacy.io/usage/linguistic-features#pos-tagging
 

examples/training/train_textcat.py

@@ -3,9 +3,8 @@
 """Train a multi-label convolutional neural network text classifier on the
 IMDB dataset, using the TextCategorizer component. The dataset will be loaded
 automatically via Thinc's built-in dataset loader. The model is added to
-spacy.pipeline, and predictions are available via `doc.cats`.
+spacy.pipeline, and predictions are available via `doc.cats`. For more details,
-
+see the documentation:
-For more details, see the documentation:
 * Training: https://alpha.spacy.io/usage/training
 * Text classification: https://alpha.spacy.io/usage/text-classification
 

examples/vectors_fast_text.py

@@ -13,8 +13,7 @@ import from spacy.language import Language
 @plac.annotations(
     vectors_loc=("Path to vectors", "positional", None, str))
 def main(vectors_loc):
-    nlp = Language()
+    nlp = Language()  # start off with a blank Language class
-
     with open(vectors_loc, 'rb') as file_:
         header = file_.readline()
         nr_row, nr_dim = header.split()
@@ -24,9 +23,11 @@ def main(vectors_loc):
             pieces = line.split()
             word = pieces[0]
             vector = numpy.asarray([float(v) for v in pieces[1:]], dtype='f')
-            nlp.vocab.set_vector(word, vector)
+            nlp.vocab.set_vector(word, vector)  # add the vectors to the vocab
-    doc = nlp(u'class colspan')
+    # test the vectors and similarity
-    print(doc[0].similarity(doc[1]))
+    text = 'class colspan'
+    doc = nlp(text)
+    print(text, doc[0].similarity(doc[1]))
 
 
 if __name__ == '__main__':