def METHOD_NAME(self):
    """
    Opens the SpaCy output and gets ze entities.
    """
    # Validate whether the user enabled the right parameters.
    if "ner" not in self.source_dataset.parameters["enable"]:
        self.dataset.update_status("Enable \"Named entity recognition\" in previous module")
        self.dataset.finish(0)
        return
    else:
        # Extract the SpaCy docs first
        self.dataset.update_status("Unzipping SpaCy docs")
        # Store all the entities in this list
        li_entities = []
        nlp = spacy.load("en_core_web_sm")  # Load model
        for doc_file in self.iterate_archive_contents(self.source_file):
            with doc_file.open("rb") as pickle_file:
                # Load DocBin
                file = pickle.load(pickle_file)
                doc_bin = DocBin().from_bytes(file)
                docs = list(doc_bin.get_docs(nlp.vocab))
            for doc in docs:
                post_entities = []
                # stop processing if worker has been asked to stop
                if self.interrupted:
                    raise ProcessorInterruptedException("Interrupted while processing documents")
                for ent in doc.ents:
                    if ent.label_ in self.parameters["entities"]:
                        post_entities.append((ent.text, ent.label_))  # Add a tuple
                li_entities.append(post_entities)
        results = []
        if li_entities:
            # Also add the data to the original file, if indicated.
            if self.parameters.get("overwrite"):
                self.add_field_to_parent(field_name='named_entities',
                                         # Format like "Apple:ORG, Gates:PERSON, ..." and add to the row
                                         new_data=[", ".join([":".join(post_entities) for post_entities in entity]) for entity in li_entities],
                                         which_parent=self.dataset.top_parent(),
                                         update_existing=True)
            all_entities = []
            # Convert to lower and filter out one-letter words. Join the words with the entities so we can group easily.
            for post_ents in li_entities:
                for pair in post_ents:
                    if pair and len(pair[0]) > 1:
                        pair = pair[0].lower() + " |#| " + pair[1]
                        all_entities.append(pair)
            # Group and rank
            count_nouns = Counter(all_entities).most_common()
            # Unsplit and list the count.
            results = [{"word": tpl[0].split(" |#| ")[0], "entity": tpl[0].split(" |#| ")[1], "count": tpl[1]} for
                       tpl in count_nouns]
        # done!
        if results:
            self.dataset.update_status("Finished")
            self.write_csv_items_and_finish(results)
        else:
            self.dataset.update_status("Finished, but no entities were extracted.")
            self.dataset.finish(0)