Datasets:

cardiffnlp
/

tweet_topic_single

	""" TweetTopic Dataset """
	import json
	from itertools import chain
	import datasets

	logger = datasets.logging.get_logger(__name__)
	_DESCRIPTION = """[TweetTopic](https://arxiv.org/abs/2209.09824)"""

	_VERSION = "1.0.4"
	_CITATION = """
	@inproceedings{dimosthenis-etal-2022-twitter,
	title = "{T}witter {T}opic {C}lassification",
	author = "Antypas, Dimosthenis and
	Ushio, Asahi and
	Camacho-Collados, Jose and
	Neves, Leonardo and
	Silva, Vitor and
	Barbieri, Francesco",
	booktitle = "Proceedings of the 29th International Conference on Computational Linguistics",
	month = oct,
	year = "2022",
	address = "Gyeongju, Republic of Korea",
	publisher = "International Committee on Computational Linguistics"
	}
	"""
	_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 = {
	f"{str(datasets.Split.TEST)}_2020": [f'{_URL}/split_temporal/test_2020.{_LABEL_TYPE}.json'],
	f"{str(datasets.Split.TEST)}_2021": [f'{_URL}/split_temporal/test_2021.{_LABEL_TYPE}.json'],
	f"{str(datasets.Split.TRAIN)}_2020": [f'{_URL}/split_temporal/train_2020.{_LABEL_TYPE}.json'],
	f"{str(datasets.Split.TRAIN)}_2021": [f'{_URL}/split_temporal/train_2021.{_LABEL_TYPE}.json'],
	f"{str(datasets.Split.TRAIN)}_all": [f'{_URL}/split_temporal/train_2020.{_LABEL_TYPE}.json', f'{_URL}/split_temporal/train_2021.{_LABEL_TYPE}.json'],
	f"{str(datasets.Split.VALIDATION)}_2020": [f'{_URL}/split_temporal/validation_2020.{_LABEL_TYPE}.json'],
	f"{str(datasets.Split.VALIDATION)}_2021": [f'{_URL}/split_temporal/validation_2021.{_LABEL_TYPE}.json'],
	f"{str(datasets.Split.TRAIN)}_random": [f'{_URL}/split_random/train_random.{_LABEL_TYPE}.json'],
	f"{str(datasets.Split.VALIDATION)}_random": [f'{_URL}/split_random/validation_random.{_LABEL_TYPE}.json'],
	f"{str(datasets.Split.TEST)}_coling2022_random": [f'{_URL}/split_coling2022_random/test_random.{_LABEL_TYPE}.json'],
	f"{str(datasets.Split.TRAIN)}_coling2022_random": [f'{_URL}/split_coling2022_random/train_random.{_LABEL_TYPE}.json'],
	f"{str(datasets.Split.TEST)}_coling2022": [f'{_URL}/split_coling2022_temporal/test_2021.{_LABEL_TYPE}.json'],
	f"{str(datasets.Split.TRAIN)}_coling2022": [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):
	names = ["arts_&_culture", "business_&_entrepreneurs", "pop_culture", "daily_life", "sports_&_gaming", "science_&_technology"]
	return datasets.DatasetInfo(
	description=_DESCRIPTION,
	features=datasets.Features(
	{
	"text": datasets.Value("string"),
	"date": datasets.Value("string"),
	"label": datasets.features.ClassLabel(names=names),
	"label_name": datasets.Value("string"),
	"id": datasets.Value("string")
	}
	),
	supervised_keys=None,
	homepage=_HOME_PAGE,
	citation=_CITATION,
	)