removing some double spaces

Author: iansedano

spacy/examples.py

@@ -22,10 +22,8 @@
 # Extract tokens for the given doc
 print([token.text for token in introduction_doc])
 
-
 # %% Reading text from a file instead
 
-
 file_name = "introduction.txt"
 introduction_file_text = pathlib.Path(file_name).read_text()
 introduction_file_doc = nlp(introduction_file_text)
@@ -78,7 +76,6 @@ def set_custom_boundaries(doc):
 for sentence in custom_ellipsis_sentences:
     print(sentence)
 
-
 ellipsis_doc = nlp(ellipsis_text)
 ellipsis_sentences = list(ellipsis_doc.sents)
 for sentence in ellipsis_sentences:
@@ -117,7 +114,6 @@ def set_custom_boundaries(doc):
 
 print([token.text for token in nlp(custom_about_text)[8:15]])
 
-
 custom_nlp = spacy.load("en_core_web_sm")
 prefix_re = spacy.util.compile_prefix_regex(custom_nlp.Defaults.prefixes)
 suffix_re = spacy.util.compile_suffix_regex(custom_nlp.Defaults.suffixes)
@@ -176,10 +172,8 @@ def set_custom_boundaries(doc):
     if str(token) != str(token.lemma_):
         print(f"{str(token):>20} : {str(token.lemma_):20}")
 
-
 # %% Making use of stop words to count words that aren't stop words
 
-
 complete_text = (
     "Gus Proto is a Python developer currently"
     " working for a London-based Fintech company. He is"
@@ -224,7 +218,6 @@ def set_custom_boundaries(doc):
     ).most_common(5)
 )
 
-
 # %% Part of speech tagging
 
 for token in about_doc[:5]:
@@ -255,7 +248,6 @@ def set_custom_boundaries(doc):
 
 # Windows server needs to be manually changed to 127.0.0.1
 
-
 about_interest_text = (
     "He is interested in learning" " Natural Language Processing."
 )
@@ -297,7 +289,6 @@ def preprocess_token(token):
 
 # %% Rule based matching
 
-
 matcher = Matcher(nlp.vocab)
 
 
@@ -314,7 +305,6 @@ def extract_full_name(nlp_doc):
 
 # %% Extracting phone numbers from text with patterns
 
-
 matcher = Matcher(nlp.vocab)
 conference_org_text = (
     "There is a developer conference"
@@ -358,7 +348,6 @@ def extract_phone_number(nlp_doc):
 {token.dep_ = }"""
     )
 
-
 displacy.serve(piano_doc, style="dep")
 
 # %% Navigating the parsed tree and subtree
@@ -409,7 +398,6 @@ def flatten_tree(tree):
 
 # %% Verb phrase detection
 
-
 about_talk_text = (
     "In this talk, the speaker will introduce the audience to the use"
     " cases of Natural Language Processing in Fintech, making use of"
@@ -420,7 +408,6 @@ def flatten_tree(tree):
 about_talk_doc = textacy.make_spacy_doc(about_talk_text, lang="en_core_web_sm")
 verb_phrases = textacy.extract.token_matches(about_talk_doc, patterns=patterns)
 
-
 # Print all verb phrases
 
 for chunk in verb_phrases:
@@ -450,7 +437,6 @@ def flatten_tree(tree):
 {spacy.explain(ent.label_) = }"""
     )
 
-
 displacy.serve(piano_class_doc, style="ent")
 # %% Use NER to redact names in document