Datasets:
Tasks:
Text2Text Generation
Languages:
English
Multilinguality:
monolingual
Size Categories:
100K<n<1M
Language Creators:
found
Annotations Creators:
found
Source Datasets:
extended|eli5
Tags:
License:
File size: 4,174 Bytes
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# coding=utf-8
# Lint as: python3
"""ELI5-Category: A categorized open-domain QA dataset."""
import json
import datasets
logger = datasets.logging.get_logger(__name__)
_CITATION = """\
@inproceedings{eli5-category,
author = {Jingsong Gao and
Qingren Zhou and
Rui Qiu},
title = {{ELI5-Category:} A categorized open-domain QA dataset},
year = {2021}
}
"""
_DESCRIPTION = """\
The ELI5-Category dataset is a smaller but newer and categorized version of the original ELI5 dataset. \
After 2017, a tagging system was introduced to this subreddit so that the questions can be categorized \
into different topics according to their tags. Since the training and validation set is built by questions \
in different topics, the dataset is expected to alleviate the train/validation overlapping issue \
in the original ELI5 dataset.
"""
class ELI5CategoryConfig(datasets.BuilderConfig):
"""BuilderConfig for ELI5Category."""
def __init__(self, **kwargs):
"""BuilderConfig for ELI5Category.
Args:
**kwargs: keyword arguments forwarded to super.
"""
super(ELI5CategoryConfig, self).__init__(**kwargs)
class ELI5Category(datasets.GeneratorBasedBuilder):
"""ELI5-Category: A categorized open-domain QA dataset."""
VERSION = datasets.Version("1.0.0")
BUILDER_CONFIGS = [
ELI5CategoryConfig(
name="default",
version=datasets.Version("1.0.0"),
description="Default config",
),
]
DEFAULT_CONFIG_NAME = "default"
def _info(self):
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=datasets.Features(
{
"q_id": datasets.Value("string"),
"title": datasets.Value("string"),
"selftext": datasets.Value("string"),
"category": datasets.Value("string"),
"subreddit": datasets.Value("string"),
"answers": {
"a_id": datasets.features.Sequence(datasets.Value("string")),
"text": datasets.features.Sequence(datasets.Value("string")),
"score": datasets.features.Sequence(datasets.Value("int32")),
"text_urls": datasets.features.Sequence(datasets.features.Sequence(datasets.Value("string"))),
},
"title_urls": datasets.features.Sequence(datasets.Value("string")),
"selftext_urls": datasets.features.Sequence(datasets.Value("string")),
}
),
supervised_keys=None,
citation=_CITATION,
)
def _split_generators(self, dl_manager):
_URL = "https://jingshensn2.github.io/eli5c/datasets/"
downloaded_files = dl_manager.download_and_extract(
{
"train": _URL + "eli5-category-train.json.gz",
"val1": _URL + "eli5-category-validation-1.json.gz",
"val2": _URL + "eli5-category-validation-2.json.gz",
"test": _URL + "eli5-category-test.json.gz",
}
)
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={"filepath": downloaded_files["train"]},
),
datasets.SplitGenerator(
name=datasets.Split("validation1"),
gen_kwargs={"filepath": downloaded_files["val1"]},
),
datasets.SplitGenerator(
name=datasets.Split("validation2"),
gen_kwargs={"filepath": downloaded_files["val2"]},
),
datasets.SplitGenerator(
name=datasets.Split.TEST,
gen_kwargs={"filepath": downloaded_files["test"]},
),
]
def _generate_examples(self, filepath):
logger.info("generating examples from = %s", filepath)
with open(filepath, encoding="utf-8") as f:
example = json.load(f)
for id_, row in enumerate(example):
yield id_, row
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