Datasets:
Tasks:
Text Classification
Modalities:
Text
Sub-tasks:
sentiment-classification
Languages:
English
Size:
10K - 100K
ArXiv:
License:
| """ TweetTopic Dataset """ | |
| import json | |
| from itertools import chain | |
| import datasets | |
| logger = datasets.logging.get_logger(__name__) | |
| _DESCRIPTION = """[TweetTopic](TBA)""" | |
| _VERSION = "1.0.1" | |
| _CITATION = """ | |
| TBA | |
| """ | |
| _HOME_PAGE = "https://cardiffnlp.github.io" | |
| _LABEL_TYPE = "single" | |
| _NAME = f"tweet_topic_{_LABEL_TYPE}" | |
| _URL = f'https://huggingface.co/datasets/cardiffnlp/{_NAME}/raw/main/dataset' | |
| _URLS = { | |
| str(datasets.Split.TEST): [f'{_URL}/split_temporal/test_2021.{_LABEL_TYPE}.json'], | |
| str(datasets.Split.TRAIN): [f'{_URL}/split_temporal/train_2021.{_LABEL_TYPE}.json'], | |
| str(datasets.Split.VALIDATION): [f'{_URL}/split_temporal/validation_2021.{_LABEL_TYPE}.json'], | |
| f"temporal_2020_{str(datasets.Split.TEST)}": [f'{_URL}/split_temporal/test_2020.{_LABEL_TYPE}.json'], | |
| f"temporal_2021_{str(datasets.Split.TEST)}": [f'{_URL}/split_temporal/test_2021.{_LABEL_TYPE}.json'], | |
| f"temporal_2020_{str(datasets.Split.TRAIN)}": [f'{_URL}/split_temporal/train_2020.{_LABEL_TYPE}.json'], | |
| f"temporal_2021_{str(datasets.Split.TRAIN)}": [f'{_URL}/split_temporal/train_2021.{_LABEL_TYPE}.json'], | |
| f"temporal_2020_{str(datasets.Split.VALIDATION)}": [f'{_URL}/split_temporal/validation_2020.{_LABEL_TYPE}.json'], | |
| f"temporal_2021_{str(datasets.Split.VALIDATION)}": [f'{_URL}/split_temporal/validation_2021.{_LABEL_TYPE}.json'], | |
| f"random_{str(datasets.Split.TRAIN)}": [f'{_URL}/split_random/train_random.{_LABEL_TYPE}.json'], | |
| f"random_{str(datasets.Split.VALIDATION)}": [f'{_URL}/split_random/validation_random.{_LABEL_TYPE}.json'], | |
| f"coling2022_random_{str(datasets.Split.TEST)}": [f'{_URL}/split_coling2022_random/test_random.{_LABEL_TYPE}.json'], | |
| f"coling2022_random_{str(datasets.Split.TRAIN)}": [f'{_URL}/split_coling2022_random/train_random.{_LABEL_TYPE}.json'], | |
| f"coling2022_temporal_{str(datasets.Split.TEST)}": [f'{_URL}/split_coling2022_temporal/test_2021.{_LABEL_TYPE}.json'], | |
| f"coling2022_temporal_{str(datasets.Split.TRAIN)}": [f'{_URL}/split_coling2022_temporal/train_2020.{_LABEL_TYPE}.json'], | |
| } | |
| class TweetTopicSingleConfig(datasets.BuilderConfig): | |
| """BuilderConfig""" | |
| def __init__(self, **kwargs): | |
| """BuilderConfig. | |
| Args: | |
| **kwargs: keyword arguments forwarded to super. | |
| """ | |
| super(TweetTopicSingleConfig, self).__init__(**kwargs) | |
| class TweetTopicSingle(datasets.GeneratorBasedBuilder): | |
| """Dataset.""" | |
| BUILDER_CONFIGS = [ | |
| TweetTopicSingleConfig(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=i, gen_kwargs={"filepaths": downloaded_file[i]}) for i in _URLS.keys()] | |
| 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( | |
| { | |
| "text": datasets.Value("string"), | |
| "date": datasets.Value("string"), | |
| "label": datasets.Value("int32"), | |
| "label_name": datasets.Value("string"), | |
| "id": datasets.Value("string") | |
| } | |
| ), | |
| supervised_keys=None, | |
| homepage=_HOME_PAGE, | |
| citation=_CITATION, | |
| ) | |