Datasets:
Tasks:
Text Generation
Modalities:
Text
Formats:
webdataset
Languages:
English
Size:
< 1K
Tags:
jira
File size: 1,909 Bytes
67dfbba 46d159a 67dfbba 2df0659 5e9bb0a 67dfbba 42b7c18 08b0d82 42b7c18 d361597 67dfbba 08b0d82 67dfbba 08b0d82 67dfbba 08b0d82 5ff78ef 67dfbba d361597 08b0d82 5ff78ef 41c919b 67dfbba 5ff78ef |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 |
"""The BookCorpus dataset."""
import datasets
import os
_DESCRIPTION = """\
Dataset of jira comments from different projects of Apache and more.
"""
_CITATION = """\
@InProceedings{Zhu_2015_ICCV,
title = {Jira commentaries},
author = {Filipp Abapolov},
month = {Fubruary},
year = {2023}
}
"""
_REPO = "https://huggingface.co/datasets/pheepa/jira-comments/resolve/main"
_URL = f"{_REPO}/data/jira-comments.tar.gz"
class JiraComments(datasets.GeneratorBasedBuilder):
"""JiraComments dataset."""
BUILDER_CONFIGS = [
datasets.BuilderConfig(
name='jira-comments',
version=datasets.Version("1.0.0"),
description=_DESCRIPTION
)
]
def _info(self):
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=datasets.Features(
{
"text": datasets.Value("string"),
}
),
supervised_keys=None,
citation=_CITATION
)
def _split_generators(self, dl_manager):
"""Returns SplitGenerators."""
data_dir = dl_manager.download_and_extract(_URL)
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={"filepath": os.path.join(data_dir, "train-pairs-jira-comments.txt")}
),
datasets.SplitGenerator(
name=datasets.Split.TEST,
gen_kwargs={"filepath": os.path.join(data_dir, "test-pairs-jira-comments.txt")}
)
]
def _generate_examples(self, filepath):
"""Yields examples."""
with open(filepath, 'r') as f:
triples = f.read()
for id_, line in enumerate(range(0, len(triples), 3)):
l, a, b = line.split('\n')
yield id_, {"text": ' '.join([a, b]), 'labels': int(l)}
|