              wp_title = fields[2]
              start = fields[3]
              end = fields[4]
  
              entries_by_offset = entries_per_article.get(article_id, dict())
              entries_by_offset[start + "-" + end] = (alias, wp_title)
  
              entries_per_article[article_id] = entries_by_offset
  
      return entries_per_article
  
 
</s> remove             entries_per_article[article_id] = entries_by_offset
 </s> add                     entries_per_article[article_id] = entries_by_offset </s> remove                     gold_entities = list()
                    for ent in article_doc.ents:
                        start = ent.start_char
                        end = ent.end_char

                        entity_tuple = entries_by_offset.get(str(start) + "-" + str(end), None)
                        if entity_tuple:
                            alias, wp_title = entity_tuple
                            if ent.text != alias:
                                print("Non-matching entity in", article_id, start, end)
                            else:
                                gold_entities.append((start, end, wp_title))

                    if gold_entities:
                        gold = GoldParse(doc=article_doc, links=gold_entities)
                        data.append((article_doc, gold))

                    cnt += 1
                except Exception as e:
                    print("Problem parsing article", article_id)
                    print(e)
                    raise e

    if to_print:
        print()
        print("Processed", cnt, "training articles, dev=" + str(dev))
        print()
 </s> add  </s> remove                     entries_by_offset = entries_per_article.get(article_id, dict())
 </s> add         except Exception as e:
            print("Problem parsing article", article_id)
            print(e)
            raise e </s> remove     cnt = 0
    files = listdir(training_dir)
    for f in files:
        if not limit or cnt < limit:
            if dev == run_el.is_dev(f):
                article_id = f.replace(".txt", "")
                if cnt % 500 == 0 and to_print:
                    print(datetime.datetime.now(), "processed", cnt, "files in the training dataset")

                try:
                    # parse the article text
                    with open(os.path.join(training_dir, f), mode="r", encoding='utf8') as file:
                        text = file.read()
                        article_doc = nlp(text)
 </s> add             if gold_entities:
                gold = GoldParse(doc=article_doc, links=gold_entities)
                data.append((article_doc, gold)) </s> remove                                             print("alias (" + lang + "):", item["value"])
 </s> add                                              print("alias (" + lang + "):", item["value"])
                    alias = fields[1]
                    wp_title = fields[2]
                    start = fields[3]
                    end = fields[4]

                    entries_by_offset = entries_per_article.get(article_id, dict())
                    entries_by_offset[start + "-" + end] = (alias, wp_title)
              wp_title = fields[2]
              start = fields[3]
              end = fields[4]
  
                    alias = fields[1]
                    wp_title = fields[2]
                    start = fields[3]
                    end = fields[4]

                    entries_by_offset = entries_per_article.get(article_id, dict())
                    entries_by_offset[start + "-" + end] = (alias, wp_title)
                    alias = fields[1]
                    wp_title = fields[2]
                    start = fields[3]
                    end = fields[4]

                    entries_by_offset = entries_per_article.get(article_id, dict())
                    entries_by_offset[start + "-" + end] = (alias, wp_title)
  
              entries_per_article[article_id] = entries_by_offset
  
      return entries_per_article
  
keep keep keep keep replace replace keep keep keep keep keep
 reprocessing all of wikipedia for training data 
examples/pipeline/wiki_entity_linking/training_set_creator.py