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import json
import datasets
import os
_CITATION = """\
@article{huggingface:dataset,
title = {ParsiNLU: A Suite of Language Understanding Challenges for Persian},
authors = {Khashabi, Daniel and Cohan, Arman and Shakeri, Siamak and Hosseini, Pedram and Pezeshkpour, Pouya and Alikhani, Malihe and Aminnaseri, Moin and Bitaab, Marzieh and Brahman, Faeze and Ghazarian, Sarik and others},
year={2020}
journal = {arXiv e-prints},
eprint = {2012.06154},
}
"""
# You can copy an official description
_DESCRIPTION = """A Persian multiple choice task."""
_HOMEPAGE = "https://github.com/persiannlp/parsinlu/"
_LICENSE = "CC BY-NC-SA 4.0"
_URL = "https://raw.githubusercontent.com/persiannlp/parsinlu/master/data/multiple-choice/"
_URLs = {
"train": _URL + "train.jsonl",
"val": _URL + "valid.jsonl",
"test": _URL + "test.jsonl",
}
class ParsinluMultipleChoice(datasets.GeneratorBasedBuilder):
"""ParsiNLU Persian multiple choice task."""
VERSION = datasets.Version("1.0.0")
BUILDER_CONFIGS = [
datasets.BuilderConfig(
name="parsinlu-repo", version=VERSION, description="Here the task is to pick a correct answer among 3-5 given candidate answers"
),]
def _info(self):
features = datasets.Features(
{
"answer": datasets.Value("string"),
"candidates": datasets.features.Sequence(feature=datasets.Value(dtype='string', id=None), length=-1),
"category": datasets.Value("string"),
"question": datasets.Value("string"),
"id": datasets.Value("string")
}
)
return datasets.DatasetInfo(
# This is the description that will appear on the datasets page.
description=_DESCRIPTION,
# This defines the different columns of the dataset and their types
features=features, # Here we define them above because they are different between the two configurations
# If there's a common (input, target) tuple from the features,
# specify them here. They'll be used if as_supervised=True in
# builder.as_dataset.
supervised_keys=None,
# Homepage of the dataset for documentation
homepage=_HOMEPAGE,
# License for the dataset if available
license=_LICENSE,
# Citation for the dataset
citation=_CITATION,
)
def _split_generators(self, dl_manager):
data_dir = dl_manager.download_and_extract(_URLs)
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
# These kwargs will be passed to _generate_examples
gen_kwargs={
"filepath": data_dir["train"],
"split": "train",
},
),
datasets.SplitGenerator(
name=datasets.Split.TEST,
# These kwargs will be passed to _generate_examples
gen_kwargs={
"filepath": data_dir["test"],
"split": "test"},
),
datasets.SplitGenerator(
name=datasets.Split.VALIDATION,
# These kwargs will be passed to _generate_examples
gen_kwargs={
"filepath": data_dir["val"],
"split": "validation",
},
),
]
def _generate_examples(self, filepath, split):
def get_answer_index(passage, answer):
return passage.index(answer) if answer in passage else -1
with open(filepath, encoding="utf-8") as f:
for id_, row in enumerate(f):
data = json.loads(row)
yield id_, {
"answer": data["answer"],
"candidates": data["candidates"],
"category": data["category"],
"question": data["question"],
"id": data['id']
} |