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import os
from pathlib import Path
from typing import Dict, List, Tuple
import datasets
from seacrowd.utils.configs import SEACrowdConfig
from seacrowd.utils.constants import Tasks
from seacrowd.utils import schemas
import json
_CITATION = """\
@misc{quoraFirstQuora,
author = {},
title = {{F}irst {Q}uora {D}ataset {R}elease: {Q}uestion {P}airs --- quoradata.quora.com},
howpublished = {https://quoradata.quora.com/First-Quora-Dataset-Release-Question-Pairs},
year = 2017,
note = {Online},
}
"""
_LANGUAGES = ["ind"] # We follow ISO639-3 language code (https://iso639-3.sil.org/code_tables/639/data)
_LOCAL = False
_DATASETNAME = "id_qqp"
_DESCRIPTION = """\
Quora Question Pairs (QQP) dataset consists of over 400,000 question pairs,
and each question pair is annotated with a binary value indicating whether
the two questions are paraphrase of each other. This dataset is translated
version of QQP to Indonesian Language.
"""
_HOMEPAGE = "https://github.com/louisowen6/quora_paraphrasing_id"
_LICENSE = "Apache License, Version 2.0"
_URLS = {
_DATASETNAME: [
"https://github.com/louisowen6/quora_paraphrasing_id/raw/main/ID_Quora_Paraphrasing_train.json",
"https://github.com/louisowen6/quora_paraphrasing_id/raw/main/ID_Quora_Paraphrasing_val.json",
]
}
_SUPPORTED_TASKS = [Tasks.PARAPHRASING]
_SOURCE_VERSION = "1.0.0"
_SEACROWD_VERSION = "2024.06.20"
class IdQuoraQuestionPairs(datasets.GeneratorBasedBuilder):
"""
Quora Question Pairs (QQP) dataset consists of over 400,000 question pairs,
and each question pair is annotated with a binary value indicating whether
the two questions are paraphrase of each other. This dataset is translated
version of QQP to Indonesian Language.
"""
SOURCE_VERSION = datasets.Version(_SOURCE_VERSION)
SEACROWD_VERSION = datasets.Version(_SEACROWD_VERSION)
BUILDER_CONFIGS = [
SEACrowdConfig(
name="id_qqp_source",
version=SOURCE_VERSION,
description="ID QQP source schema",
schema="source",
subset_id="id_qqp",
),
SEACrowdConfig(
name="id_qqp_seacrowd_t2t",
version=SEACROWD_VERSION,
description="ID QQP Nusantara schema",
schema="seacrowd_t2t",
subset_id="id_qqp",
),
]
DEFAULT_CONFIG_NAME = "id_qqp_source"
def _info(self) -> datasets.DatasetInfo:
if self.config.schema == "source":
features = datasets.Features(
{
"id": datasets.Value("string"),
"question_1": datasets.Value("string"),
"question_2": datasets.Value("string")
}
)
elif self.config.schema == "seacrowd_t2t":
features = schemas.text2text_features
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=features,
homepage=_HOMEPAGE,
license=_LICENSE,
citation=_CITATION,
)
def _split_generators(self, dl_manager: datasets.DownloadManager) -> List[datasets.SplitGenerator]:
urls = _URLS[_DATASETNAME]
data_dir = dl_manager.download_and_extract(urls)
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={
"filepath": data_dir[0],
},
),
datasets.SplitGenerator(
name=datasets.Split.VALIDATION,
gen_kwargs={
"filepath": data_dir[1],
},
),
]
def _generate_examples(self, filepath: Path) -> Tuple[int, Dict]:
with open(filepath, "r") as f:
lines = f.readlines()
if self.config.schema == "source":
for i, line in enumerate(lines):
line = json.loads(line.strip())
sample = {
"id": str(i),
"question_1": line["question_1"],
"question_2": line["question_2"]
}
yield i, sample
elif self.config.schema == "seacrowd_t2t":
for i, line in enumerate(lines):
line = json.loads(line.strip())
sample = {
"id": str(i),
"text_1": line["question_1"],
"text_2": line["question_2"],
"text_1_name": "question_1",
"text_2_name": "question_2"
}
yield i, sample
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