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import csv
import random
import string
from pathlib import Path
from typing import Dict, List, Tuple
import datasets
from seacrowd.utils import schemas
from seacrowd.utils.configs import SEACrowdConfig
from seacrowd.utils.constants import Licenses, Tasks
_CITATION = """
@inproceedings{koto-etal-2022-cloze,
title = "Cloze Evaluation for Deeper Understanding of Commonsense Stories in {I}ndonesian",
author = "Koto, Fajri and
Baldwin, Timothy and
Lau, Jey Han",
editor = "Bosselut, Antoine and
Li, Xiang and
Lin, Bill Yuchen and
Shwartz, Vered and
Majumder, Bodhisattwa Prasad and
Lal, Yash Kumar and
Rudinger, Rachel and
Ren, Xiang and
Tandon, Niket and
Zouhar, Vil{\'e}m",
booktitle = "Proceedings of the First Workshop on Commonsense Representation and Reasoning (CSRR 2022)",
month = may,
year = "2022",
address = "Dublin, Ireland",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.csrr-1.2",
doi = "10.18653/v1/2022.csrr-1.2",
pages = "8--16",
}
"""
_DATASETNAME = "indo_story_cloze"
_DESCRIPTION = """
A Story Cloze Test framework in Indonesian. A story in our dataset consists of four-sentence premise, one-sentence
correct ending, and one-sentence incorrect ending. In total, we have created 2,325 Indonesian stories with the
train/dev/test split 1,000/200/1,135.
"""
_HOMEPAGE = "https://huggingface.co/datasets/indolem/indo_story_cloze"
_LANGUAGES = ["ind"]
_LICENSE = Licenses.CC_BY_SA_4_0.value
_LOCAL = False
_URLS = {
_DATASETNAME: {
"train": "https://huggingface.co/datasets/indolem/indo_story_cloze/resolve/main/train.csv",
"dev": "https://huggingface.co/datasets/indolem/indo_story_cloze/resolve/main/dev.csv",
"test": "https://huggingface.co/datasets/indolem/indo_story_cloze/resolve/main/test.csv",
},
}
_SUPPORTED_TASKS = [Tasks.COMMONSENSE_REASONING]
_SOURCE_VERSION = "1.0.0"
_SEACROWD_VERSION = "2024.06.20"
class IndoStoryClozeDataset(datasets.GeneratorBasedBuilder):
"""IndoStoryCloze is a Story Cloze dataset in Indonesian."""
SOURCE_VERSION = datasets.Version(_SOURCE_VERSION)
SEACROWD_VERSION = datasets.Version(_SEACROWD_VERSION)
BUILDER_CONFIGS = [
SEACrowdConfig(
name=f"{_DATASETNAME}_source",
version=SOURCE_VERSION,
description=f"{_DATASETNAME} source schema",
schema="source",
subset_id=_DATASETNAME,
),
SEACrowdConfig(
name=f"{_DATASETNAME}_seacrowd_qa",
version=SEACROWD_VERSION,
description=f"{_DATASETNAME} SEACrowd schema",
schema="seacrowd_qa",
subset_id=_DATASETNAME,
),
]
DEFAULT_CONFIG_NAME = f"{_DATASETNAME}_source"
def _info(self) -> datasets.DatasetInfo:
if self.config.schema == "source":
features = datasets.Features(
{
"sentence-1": datasets.Value("string"),
"sentence-2": datasets.Value("string"),
"sentence-3": datasets.Value("string"),
"sentence-4": datasets.Value("string"),
"correct_ending": datasets.Value("string"),
"incorrect_ending": datasets.Value("string"),
}
)
elif self.config.schema == "seacrowd_qa":
features = schemas.qa_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]:
"""Returns SplitGenerators."""
urls = _URLS[_DATASETNAME]
data_dir = dl_manager.download_and_extract(urls)
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={"filepath": data_dir, "split": "train"},
),
datasets.SplitGenerator(
name=datasets.Split.TEST,
gen_kwargs={"filepath": data_dir, "split": "test"},
),
datasets.SplitGenerator(
name=datasets.Split.VALIDATION,
gen_kwargs={"filepath": data_dir, "split": "dev"},
),
]
def _generate_examples(self, filepath: Path, split: str) -> Tuple[int, Dict]:
if self.config.schema == "source":
data = csv.DictReader(open(filepath[split], newline="", encoding="utf-8"))
for i, row in enumerate(data):
yield i, {
"sentence-1": row["Kalimat-1"],
"sentence-2": row["Kalimat-2"],
"sentence-3": row["Kalimat-3"],
"sentence-4": row["Kalimat-4"],
"correct_ending": row["Correct Ending"],
"incorrect_ending": row["Incorrect Ending"],
}
elif self.config.schema == "seacrowd_qa":
data = csv.DictReader(open(filepath[split], newline="", encoding="utf-8"))
def build_question(line):
# Concatenate the 4 sentences, this can either be the question of the context. Set is as question for
# now. Some sentences do not have punctuation, hence adding . before concatenation.
sentences = []
for k in ["Kalimat-1", "Kalimat-2", "Kalimat-3", "Kalimat-4"]:
if line[k].strip()[-1] not in string.punctuation:
sentences.append(line[k] + ".")
else:
sentences.append(line[k])
return " ".join(sentences)
for i, row in enumerate(data):
yield i, {
"id": str(i),
"question_id": str(i),
"document_id": str(i),
"question": build_question(row),
"type": "multiple_choice",
# Reorder choices based on the randomly generated labels, avoiding correct answer at the same order.
"choices": [row["Correct Ending"], row["Incorrect Ending"]] if random.randint(0, 1) == 0 else [row["Incorrect Ending"], row["Correct Ending"]],
"context": "",
"answer": [row["Correct Ending"]],
"meta": {},
}
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