| import json |
| from typing import List |
| import datasets |
| import re |
|
|
| ENTITY = 'entity' |
| ENTITY_PATTERN = r'<entity>{}</entity>' |
|
|
|
|
| def markup_entity(utt: str, entities: List[str]): |
| |
| entities = sorted(list(set(entities)), key=lambda x: len(x), reverse=True) |
| for i, entity in enumerate(entities): |
| valid = entity not in ENTITY |
| for prev in entities[:i]: |
| if entity in prev: |
| valid = False |
| if valid: |
| utt = re.sub(entity, ENTITY_PATTERN.format(entity), utt) |
| return utt |
|
|
| logger = datasets.logging.get_logger(__name__) |
|
|
|
|
| _URL = "./" |
| _URLS = { |
| "train": _URL + "train_data_dbpedia.jsonl", |
| "valid": _URL + "valid_data_dbpedia.jsonl", |
| "test": _URL + "test_data_dbpedia.jsonl", |
| "entity2id": _URL + "entity2id.json" |
| } |
|
|
|
|
| class InspiredConfig(datasets.BuilderConfig): |
| def __init__(self, features, |
| initiator_prefix='User: ', |
| respondent_prefix='System: ', |
| **kwargs): |
| """BuilderConfig for Inspired (used in UniCRS). |
| |
| Args: |
| features: *list[string]*, list of the features that will appear in the |
| feature dict. Should not include "label". |
| **kwargs: keyword arguments forwarded to super. |
| """ |
| super().__init__(version=datasets.Version("0.0.1"), **kwargs) |
| self.features = features |
| self.initiator_prefix = initiator_prefix |
| self.respondent_prefix = respondent_prefix |
|
|
| class Inspired(datasets.GeneratorBasedBuilder): |
| DEFAULT_CONFIG_NAME = "unrolled" |
| BUILDER_CONFIGS = [ |
| InspiredConfig( |
| name="unrolled", |
| description="The processed Inspired dataset in UniCRS. Each conversation yields multiple samples", |
| features={ |
| "messages": datasets.Sequence(datasets.Value("string")), |
| "rec": datasets.Sequence(datasets.Value("int32")), |
| "recNames": datasets.Sequence(datasets.Value("string")), |
| } |
| ) |
| ] |
|
|
|
|
| def __init__(self, **kwargs): |
| super().__init__(**kwargs) |
|
|
| def _info(self): |
| return datasets.DatasetInfo( |
| description=self.config.description, |
| features=datasets.Features(self.config.features), |
| ) |
|
|
| def _split_generators(self, dl_manager: datasets.DownloadManager) -> List[datasets.SplitGenerator]: |
| urls_to_download = _URLS |
| downloaded_files = dl_manager.download_and_extract(urls_to_download) |
| entity2id_file = downloaded_files["entity2id"] |
| return [ |
| datasets.SplitGenerator(name=datasets.Split.TRAIN, |
| gen_kwargs={"filepath": downloaded_files["train"], "entity2id": entity2id_file}), |
| datasets.SplitGenerator(name=datasets.Split.VALIDATION, |
| gen_kwargs={"filepath": downloaded_files["valid"], "entity2id": entity2id_file}), |
| datasets.SplitGenerator(name=datasets.Split.TEST, |
| gen_kwargs={"filepath": downloaded_files["test"], "entity2id": entity2id_file}), |
| ] |
|
|
|
|
|
|
| def _generate_examples(self, filepath, entity2id): |
| """This function returns the examples in the raw (text) form.""" |
| logger.info("generating examples from = %s", filepath) |
|
|
| with open(entity2id, 'r', encoding='utf-8') as f: |
| entity2id = json.load(f) |
| if "unrolled" in self.config.name: |
| Idx = 0 |
| with open(filepath, encoding="utf-8") as f: |
| for line in f: |
| dialog = json.loads(line) |
| context = [] |
|
|
| for turn in dialog: |
| resp = turn['text'] |
| movie_turn = [entity2id[movie] for movie in turn['movie_link'] if movie in entity2id] |
|
|
| resp = markup_entity(resp, turn['entity_name']+turn['movie_name']) |
| prefix = self.config.initiator_prefix if turn['role'] == 'SEEKER' else self.config.respondent_prefix |
| resp = prefix + resp |
| context.append(resp) |
|
|
| yield Idx, { |
| 'messages': context, |
| 'rec': movie_turn, |
| 'recNames': turn['movie_name'] |
| } |
|
|
| Idx += 1 |
|
|
|
|
|
|
