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](https://arxiv.org/abs/2209.09824)""" | |
_VERSION = "1.0.3" | |
_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): | |
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, | |
) | |