  
          train_data = training_set_creator.read_training(nlp=nlp_2,
                                                          training_dir=TRAINING_DIR,
                                                          dev=False,
                                                          limit=train_limit,
                                                          to_print=False)
  
          dev_data = training_set_creator.read_training(nlp=nlp_2,
                                                        training_dir=TRAINING_DIR,
                                                        dev=True,
                                                        limit=dev_limit,
 
</s> remove         print("Training on", train_limit, "articles")
        print("Dev testing on", dev_limit, "articles")
        print()
 </s> add  </s> remove                                                       limit=dev_limit,
                                                      to_print=False)

        for itn in range(EPOCHS):
            random.shuffle(train_data)
            losses = {}
            batches = minibatch(train_data, size=compounding(4.0, 128.0, 1.001))
            batchnr = 0

            with nlp_2.disable_pipes(*other_pipes):
                for batch in batches:
                    try:
                        docs, golds = zip(*batch)
                        nlp_2.update(
                            docs,
                            golds,
                            drop=DROPOUT,
                            losses=losses,
                        )
                        batchnr += 1
                    except Exception as e:
                        print("Error updating batch", e)

            losses['entity_linker'] = losses['entity_linker'] / batchnr
            print("Epoch, train loss", itn, round(losses['entity_linker'], 2))

        if measure_performance:
 </s> add                                                       limit=dev_limit)
        print("Dev testing on", len(dev_data), "articles")
        print()

        if len(dev_data) and measure_performance: </s> remove MIN_ENTITY_FREQ = 200
 </s> add MIN_ENTITY_FREQ = 20 </s> remove             entries_by_offset = entries_per_article.get(article_id, dict())
            entries_by_offset[start + "-" + end] = (alias, wp_title)
 </s> add                     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) </s> remove     to_read_kb = False
 </s> add     to_read_kb = True
                                                        limit=train_limit)

        print("Training on", len(train_data), "articles")
        print()

        if not train_data:
            print("Did not find any training data")

        else:
            for itn in range(EPOCHS):
                random.shuffle(train_data)
                losses = {}
                batches = minibatch(train_data, size=compounding(4.0, 128.0, 1.001))
                batchnr = 0

                with nlp_2.disable_pipes(*other_pipes):
                    for batch in batches:
                        try:
                            docs, golds = zip(*batch)
                            nlp_2.update(
                                docs,
                                golds,
                                drop=DROPOUT,
                                losses=losses,
                            )
                            batchnr += 1
                        except Exception as e:
                            print("Error updating batch", e)

                losses['entity_linker'] = losses['entity_linker'] / batchnr
                print("Epoch, train loss", itn, round(losses['entity_linker'], 2))
  
          train_data = training_set_creator.read_training(nlp=nlp_2,
                                                          training_dir=TRAINING_DIR,
                                                          dev=False,
                                                        limit=train_limit)

        print("Training on", len(train_data), "articles")
        print()

        if not train_data:
            print("Did not find any training data")

        else:
            for itn in range(EPOCHS):
                random.shuffle(train_data)
                losses = {}
                batches = minibatch(train_data, size=compounding(4.0, 128.0, 1.001))
                batchnr = 0

                with nlp_2.disable_pipes(*other_pipes):
                    for batch in batches:
                        try:
                            docs, golds = zip(*batch)
                            nlp_2.update(
                                docs,
                                golds,
                                drop=DROPOUT,
                                losses=losses,
                            )
                            batchnr += 1
                        except Exception as e:
                            print("Error updating batch", e)

                losses['entity_linker'] = losses['entity_linker'] / batchnr
                print("Epoch, train loss", itn, round(losses['entity_linker'], 2))
                                                        limit=train_limit)

        print("Training on", len(train_data), "articles")
        print()

        if not train_data:
            print("Did not find any training data")

        else:
            for itn in range(EPOCHS):
                random.shuffle(train_data)
                losses = {}
                batches = minibatch(train_data, size=compounding(4.0, 128.0, 1.001))
                batchnr = 0

                with nlp_2.disable_pipes(*other_pipes):
                    for batch in batches:
                        try:
                            docs, golds = zip(*batch)
                            nlp_2.update(
                                docs,
                                golds,
                                drop=DROPOUT,
                                losses=losses,
                            )
                            batchnr += 1
                        except Exception as e:
                            print("Error updating batch", e)

                losses['entity_linker'] = losses['entity_linker'] / batchnr
                print("Epoch, train loss", itn, round(losses['entity_linker'], 2))
  
          dev_data = training_set_creator.read_training(nlp=nlp_2,
                                                        training_dir=TRAINING_DIR,
                                                        dev=True,
                                                        limit=dev_limit,
keep keep keep keep replace replace keep keep keep keep keep
 reprocessing all of wikipedia for training data 
examples/pipeline/wiki_entity_linking/wiki_nel_pipeline.py