| import json | |
| import datasets | |
| logger = datasets.logging.get_logger(__name__) | |
| _DESCRIPTION = """Dataset for relation mapping task (see [paper](https://arxiv.org/abs/2211.15268)).""" | |
| _NAME = "scientific_and_creative_analogy" | |
| _VERSION = "0.0.0" | |
| _CITATION = """ | |
| @article{czinczoll2022scientific, | |
| title={Scientific and Creative Analogies in Pretrained Language Models}, | |
| author={Czinczoll, Tamara and Yannakoudakis, Helen and Mishra, Pushkar and Shutova, Ekaterina}, | |
| journal={arXiv preprint arXiv:2211.15268}, | |
| year={2022} | |
| } | |
| """ | |
| _HOME_PAGE = "https://github.com/taczin/SCAN_analogies" | |
| _URLS = { | |
| str(datasets.Split.TEST): [f'https://huggingface.co/datasets/relbert/{_NAME}/raw/main/data.jsonl'] | |
| } | |
| class ScientificAndCreativeAnalogyConfig(datasets.BuilderConfig): | |
| """BuilderConfig""" | |
| def __init__(self, **kwargs): | |
| """BuilderConfig. | |
| Args: | |
| **kwargs: keyword arguments forwarded to super. | |
| """ | |
| super(ScientificAndCreativeAnalogyConfig, self).__init__(**kwargs) | |
| class ScientificAndCreativeAnalogy(datasets.GeneratorBasedBuilder): | |
| """Dataset.""" | |
| BUILDER_CONFIGS = [ | |
| ScientificAndCreativeAnalogyConfig(name=_NAME, version=datasets.Version(_VERSION), description=_DESCRIPTION), | |
| ] | |
| def _split_generators(self, dl_manager): | |
| downloaded_file = dl_manager.download_and_extract(_URLS) | |
| return [datasets.SplitGenerator( | |
| name=str(datasets.Split.TEST), gen_kwargs={"filepaths": downloaded_file[str(datasets.Split.TEST)]}) | |
| ] | |
| def _generate_examples(self, filepaths): | |
| _key = 0 | |
| for filepath in filepaths: | |
| logger.info(f"generating examples from = {filepath}") | |
| with open(filepath, encoding="utf-8") as f: | |
| _list = [i for i in f.read().split('\n') if len(i) > 0] | |
| for i in _list: | |
| data = json.loads(i) | |
| yield _key, data | |
| _key += 1 | |
| def _info(self): | |
| return datasets.DatasetInfo( | |
| description=_DESCRIPTION, | |
| features=datasets.Features( | |
| { | |
| "id": datasets.Value("string"), | |
| "type": datasets.Value("string"), | |
| "reference": datasets.Sequence(datasets.Value("string")), | |
| "source": datasets.Sequence(datasets.Value("string")), | |
| "target": datasets.Sequence(datasets.Value("string")), | |
| "target_random": datasets.Sequence(datasets.Value("string")), | |
| } | |
| ), | |
| supervised_keys=None, | |
| homepage=_HOME_PAGE, | |
| citation=_CITATION, | |
| ) | |