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