Datasets:

shunk031
/

JGLUE

	import json
	import random
	import string
	import warnings
	from typing import Dict, List, Optional, Union

	import datasets as ds
	import pandas as pd
	from datasets.tasks import QuestionAnsweringExtractive
	import logging

	logger = logging.getLogger(__name__)

	_CITATION = """\
	@inproceedings{kurihara-etal-2022-jglue,
	title = "{JGLUE}: {J}apanese General Language Understanding Evaluation",
	author = "Kurihara, Kentaro and
	Kawahara, Daisuke and
	Shibata, Tomohide",
	booktitle = "Proceedings of the Thirteenth Language Resources and Evaluation Conference",
	month = jun,
	year = "2022",
	address = "Marseille, France",
	publisher = "European Language Resources Association",
	url = "https://aclanthology.org/2022.lrec-1.317",
	pages = "2957--2966",
	abstract = "To develop high-performance natural language understanding (NLU) models, it is necessary to have a benchmark to evaluate and analyze NLU ability from various perspectives. While the English NLU benchmark, GLUE, has been the forerunner, benchmarks are now being released for languages other than English, such as CLUE for Chinese and FLUE for French; but there is no such benchmark for Japanese. We build a Japanese NLU benchmark, JGLUE, from scratch without translation to measure the general NLU ability in Japanese. We hope that JGLUE will facilitate NLU research in Japanese.",
	}

	@InProceedings{Kurihara_nlp2022,
	author = "栗原健太郎 and 河原大輔 and 柴田知秀",
	title = "JGLUE: 日本語言語理解ベンチマーク",
	booktitle = "言語処理学会第28回年次大会",
	year = "2022",
	url = "https://www.anlp.jp/proceedings/annual_meeting/2022/pdf_dir/E8-4.pdf"
	note= "in Japanese"
	}
	"""

	_DESCRIPTION = """\
	JGLUE, Japanese General Language Understanding Evaluation, is built to measure the general NLU ability in Japanese. JGLUE has been constructed from scratch without translation. We hope that JGLUE will facilitate NLU research in Japanese.
	"""

	_HOMEPAGE = "https://github.com/yahoojapan/JGLUE"

	_LICENSE = """\
	This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
	"""

	_DESCRIPTION_CONFIGS = {
	"MARC-ja": "MARC-ja is a dataset of the text classification task. This dataset is based on the Japanese portion of Multilingual Amazon Reviews Corpus (MARC) (Keung+, 2020).",
	"JSTS": "JSTS is a Japanese version of the STS (Semantic Textual Similarity) dataset. STS is a task to estimate the semantic similarity of a sentence pair.",
	"JNLI": "JNLI is a Japanese version of the NLI (Natural Language Inference) dataset. NLI is a task to recognize the inference relation that a premise sentence has to a hypothesis sentence.",
	"JSQuAD": "JSQuAD is a Japanese version of SQuAD (Rajpurkar+, 2016), one of the datasets of reading comprehension.",
	"JCommonsenseQA": "JCommonsenseQA is a Japanese version of CommonsenseQA (Talmor+, 2019), which is a multiple-choice question answering dataset that requires commonsense reasoning ability.",
	}

	_URLS = {
	"MARC-ja": {
	"data": "https://s3.amazonaws.com/amazon-reviews-pds/tsv/amazon_reviews_multilingual_JP_v1_00.tsv.gz",
	"filter_review_id_list": {
	"valid": "https://raw.githubusercontent.com/yahoojapan/JGLUE/main/preprocess/marc-ja/data/filter_review_id_list/valid.txt"
	},
	"label_conv_review_id_list": {
	"valid": "https://raw.githubusercontent.com/yahoojapan/JGLUE/main/preprocess/marc-ja/data/label_conv_review_id_list/valid.txt"
	},
	},
	"JSTS": {
	"train": "https://raw.githubusercontent.com/yahoojapan/JGLUE/main/datasets/jsts-v1.1/train-v1.1.json",
	"valid": "https://raw.githubusercontent.com/yahoojapan/JGLUE/main/datasets/jsts-v1.1/valid-v1.1.json",
	},
	"JNLI": {
	"train": "https://raw.githubusercontent.com/yahoojapan/JGLUE/main/datasets/jnli-v1.1/train-v1.1.json",
	"valid": "https://raw.githubusercontent.com/yahoojapan/JGLUE/main/datasets/jnli-v1.1/valid-v1.1.json",
	},
	"JSQuAD": {
	"train": "https://raw.githubusercontent.com/yahoojapan/JGLUE/main/datasets/jsquad-v1.1/train-v1.1.json",
	"valid": "https://raw.githubusercontent.com/yahoojapan/JGLUE/main/datasets/jsquad-v1.1/valid-v1.1.json",
	},
	"JCommonsenseQA": {
	"train": "https://raw.githubusercontent.com/yahoojapan/JGLUE/main/datasets/jcommonsenseqa-v1.1/train-v1.1.json",
	"valid": "https://raw.githubusercontent.com/yahoojapan/JGLUE/main/datasets/jcommonsenseqa-v1.1/valid-v1.1.json",
	},
	}


	def dataset_info_jsts() -> ds.Features:
	features = ds.Features(
	{
	"sentence_pair_id": ds.Value("string"),
	"yjcaptions_id": ds.Value("string"),
	"sentence1": ds.Value("string"),
	"sentence2": ds.Value("string"),
	"label": ds.Value("float"),
	}
	)
	return ds.DatasetInfo(
	description=_DESCRIPTION,
	citation=_CITATION,
	homepage=_HOMEPAGE,
	license=_LICENSE,
	features=features,
	)


	def dataset_info_jnli() -> ds.Features:
	features = ds.Features(
	{
	"sentence_pair_id": ds.Value("string"),
	"yjcaptions_id": ds.Value("string"),
	"sentence1": ds.Value("string"),
	"sentence2": ds.Value("string"),
	"label": ds.ClassLabel(
	num_classes=3, names=["entailment", "contradiction", "neutral"]
	),
	}
	)
	return ds.DatasetInfo(
	description=_DESCRIPTION,
	citation=_CITATION,
	homepage=_HOMEPAGE,
	license=_LICENSE,
	features=features,
	supervised_keys=None,
	)


	def dataset_info_jsquad() -> ds.Features:
	features = ds.Features(
	{
	"id": ds.Value("string"),
	"title": ds.Value("string"),
	"context": ds.Value("string"),
	"question": ds.Value("string"),
	"answers": ds.Sequence(
	{"text": ds.Value("string"), "answer_start": ds.Value("int32")}
	),
	"is_impossible": ds.Value("bool"),
	}
	)
	return ds.DatasetInfo(
	description=_DESCRIPTION,
	citation=_CITATION,
	homepage=_HOMEPAGE,
	license=_LICENSE,
	features=features,
	supervised_keys=None,
	task_templates=[
	QuestionAnsweringExtractive(
	question_column="question",
	context_column="context",
	answers_column="answers",
	)
	],
	)


	def dataset_info_jcommonsenseqa() -> ds.Features:
	features = ds.Features(
	{
	"q_id": ds.Value("int64"),
	"question": ds.Value("string"),
	"choice0": ds.Value("string"),
	"choice1": ds.Value("string"),
	"choice2": ds.Value("string"),
	"choice3": ds.Value("string"),
	"choice4": ds.Value("string"),
	"label": ds.ClassLabel(
	num_classes=5,
	names=["choice0", "choice1", "choice2", "choice3", "choice4"],
	),
	}
	)
	return ds.DatasetInfo(
	description=_DESCRIPTION,
	citation=_CITATION,
	homepage=_HOMEPAGE,
	license=_LICENSE,
	features=features,
	)


	def dataset_info_marc_ja() -> ds.Features:
	features = ds.Features(
	{
	"sentence": ds.Value("string"),
	"label": ds.ClassLabel(
	num_classes=3, names=["positive", "negative", "neutral"]
	),
	"review_id": ds.Value("string"),
	}
	)
	return ds.DatasetInfo(
	description=_DESCRIPTION,
	citation=_CITATION,
	homepage=_HOMEPAGE,
	license=_LICENSE,
	features=features,
	)


	class MarcJaConfig(ds.BuilderConfig):
	def __init__(
	self,
	name: str = "MARC-ja",
	is_han_to_zen: bool = False,
	max_instance_num: Optional[int] = None,
	max_char_length: int = 500,
	is_pos_neg: bool = True,
	train_ratio: float = 0.94,
	val_ratio: float = 0.03,
	test_ratio: float = 0.03,
	output_testset: bool = False,
	filter_review_id_list_valid: bool = True,
	label_conv_review_id_list_valid: bool = True,
	version: Optional[Union[ds.utils.Version, str]] = ds.utils.Version("0.0.0"),
	data_dir: Optional[str] = None,
	data_files: Optional[ds.data_files.DataFilesDict] = None,
	description: Optional[str] = None,
	) -> None:
	super().__init__(
	name=name,
	version=version,
	data_dir=data_dir,
	data_files=data_files,
	description=description,
	)
	assert train_ratio + val_ratio + test_ratio == 1.0

	self.train_ratio = train_ratio
	self.val_ratio = val_ratio
	self.test_ratio = test_ratio

	self.is_han_to_zen = is_han_to_zen
	self.max_instance_num = max_instance_num
	self.max_char_length = max_char_length
	self.is_pos_neg = is_pos_neg
	self.output_testset = output_testset

	self.filter_review_id_list_valid = filter_review_id_list_valid
	self.label_conv_review_id_list_valid = label_conv_review_id_list_valid


	def get_label(rating: int, is_pos_neg: bool = False) -> Optional[str]:
	if rating >= 4:
	return "positive"
	elif rating <= 2:
	return "negative"
	else:
	if is_pos_neg:
	return None
	else:
	return "neutral"


	def is_filtered_by_ascii_rate(text: str, threshold: float = 0.9) -> bool:
	ascii_letters = set(string.printable)
	rate = sum(c in ascii_letters for c in text) / len(text)
	return rate >= threshold


	def shuffle_dataframe(df: pd.DataFrame) -> pd.DataFrame:
	instances = df.to_dict(orient="records")
	random.seed(1)
	random.shuffle(instances)
	return pd.DataFrame(instances)


	def get_filter_review_id_list(
	filter_review_id_list_paths: Dict[str, str],
	) -> Dict[str, List[str]]:
	filter_review_id_list_valid = filter_review_id_list_paths.get("valid")
	filter_review_id_list_test = filter_review_id_list_paths.get("test")

	filter_review_id_list = {}

	if filter_review_id_list_valid is not None:
	with open(filter_review_id_list_valid, "r", encoding="utf-8") as rf:
	filter_review_id_list["valid"] = [line.rstrip() for line in rf]

	if filter_review_id_list_test is not None:
	with open(filter_review_id_list_test, "r", encoding="utf-8") as rf:
	filter_review_id_list["test"] = [line.rstrip() for line in rf]

	return filter_review_id_list


	def get_label_conv_review_id_list(
	label_conv_review_id_list_paths: Dict[str, str],
	) -> Dict[str, Dict[str, str]]:
	import csv

	label_conv_review_id_list_valid = label_conv_review_id_list_paths.get("valid")
	label_conv_review_id_list_test = label_conv_review_id_list_paths.get("test")

	label_conv_review_id_list: Dict[str, Dict[str, str]] = {}

	if label_conv_review_id_list_valid is not None:
	with open(label_conv_review_id_list_valid, "r", encoding="utf-8") as rf:
	label_conv_review_id_list["valid"] = {
	row[0]: row[1] for row in csv.reader(rf)
	}

	if label_conv_review_id_list_test is not None:
	with open(label_conv_review_id_list_test, "r", encoding="utf-8") as rf:
	label_conv_review_id_list["test"] = {
	row[0]: row[1] for row in csv.reader(rf)
	}

	return label_conv_review_id_list


	def output_data(
	df: pd.DataFrame,
	train_ratio: float,
	val_ratio: float,
	test_ratio: float,
	output_testset: bool,
	filter_review_id_list_paths: Dict[str, str],
	label_conv_review_id_list_paths: Dict[str, str],
	) -> Dict[str, pd.DataFrame]:
	instance_num = len(df)
	split_dfs: Dict[str, pd.DataFrame] = {}
	length1 = int(instance_num * train_ratio)
	split_dfs["train"] = df.iloc[:length1]

	length2 = int(instance_num * (train_ratio + val_ratio))
	split_dfs["valid"] = df.iloc[length1:length2]
	split_dfs["test"] = df.iloc[length2:]

	filter_review_id_list = get_filter_review_id_list(
	filter_review_id_list_paths=filter_review_id_list_paths,
	)
	label_conv_review_id_list = get_label_conv_review_id_list(
	label_conv_review_id_list_paths=label_conv_review_id_list_paths,
	)

	for eval_type in ("valid", "test"):
	if filter_review_id_list.get(eval_type):
	df = split_dfs[eval_type]
	df = df[~df["review_id"].isin(filter_review_id_list[eval_type])]
	split_dfs[eval_type] = df

	for eval_type in ("valid", "test"):
	if label_conv_review_id_list.get(eval_type):
	df = split_dfs[eval_type]
	df = df.assign(
	converted_label=df["review_id"].map(label_conv_review_id_list["valid"])
	)
	df = df.assign(
	label=df[["label", "converted_label"]].apply(
	lambda xs: xs["label"]
	if pd.isnull(xs["converted_label"])
	else xs["converted_label"],
	axis=1,
	)
	)
	df = df.drop(columns=["converted_label"])
	split_dfs[eval_type] = df

	return {
	"train": split_dfs["train"],
	"valid": split_dfs["valid"],
	}


	def preprocess_for_marc_ja(
	config: MarcJaConfig,
	data_file_path: str,
	filter_review_id_list_paths: Dict[str, str],
	label_conv_review_id_list_paths: Dict[str, str],
	) -> Dict[str, pd.DataFrame]:
	try:
	import mojimoji

	def han_to_zen(text: str) -> str:
	return mojimoji.han_to_zen(text)

	except ImportError:
	warnings.warn(
	"can't import `mojimoji`, failing back to method that do nothing. "
	"We recommend running `pip install mojimoji` to reproduce the original preprocessing.",
	UserWarning,
	)

	def han_to_zen(text: str) -> str:
	return text

	try:
	from bs4 import BeautifulSoup

	def cleanup_text(text: str) -> str:
	return BeautifulSoup(text, "html.parser").get_text()

	except ImportError:
	warnings.warn(
	"can't import `beautifulsoup4`, failing back to method that do nothing."
	"We recommend running `pip install beautifulsoup4` to reproduce the original preprocessing.",
	UserWarning,
	)

	def cleanup_text(text: str) -> str:
	return text

	from tqdm import tqdm

	df = pd.read_csv(data_file_path, delimiter="\t")
	df = df[["review_body", "star_rating", "review_id"]]

	# rename columns
	df = df.rename(columns={"review_body": "text", "star_rating": "rating"})

	# convert the rating to label
	tqdm.pandas(dynamic_ncols=True, desc="Convert the rating to the label")
	df = df.assign(
	label=df["rating"].progress_apply(
	lambda rating: get_label(rating, config.is_pos_neg)
	)
	)

	# remove rows where the label is None
	df = df[~df["label"].isnull()]

	# remove html tags from the text
	tqdm.pandas(dynamic_ncols=True, desc="Remove html tags from the text")
	df = df.assign(text=df["text"].progress_apply(cleanup_text))

	# filter by ascii rate
	tqdm.pandas(dynamic_ncols=True, desc="Filter by ascii rate")
	df = df[~df["text"].progress_apply(is_filtered_by_ascii_rate)]

	if config.max_char_length is not None:
	df = df[df["text"].str.len() <= config.max_char_length]

	if config.is_han_to_zen:
	df = df.assign(text=df["text"].apply(han_to_zen))

	df = df[["text", "label", "review_id"]]
	df = df.rename(columns={"text": "sentence"})

	# shuffle dataset
	df = shuffle_dataframe(df)

	split_dfs = output_data(
	df=df,
	train_ratio=config.train_ratio,
	val_ratio=config.val_ratio,
	test_ratio=config.test_ratio,
	output_testset=config.output_testset,
	filter_review_id_list_paths=filter_review_id_list_paths,
	label_conv_review_id_list_paths=label_conv_review_id_list_paths,
	)
	return split_dfs


	class JGLUE(ds.GeneratorBasedBuilder):
	VERSION = ds.Version("1.1.0")
	BUILDER_CONFIGS = [
	MarcJaConfig(
	name="MARC-ja",
	version=VERSION,
	description=_DESCRIPTION_CONFIGS["MARC-ja"],
	),
	ds.BuilderConfig(
	name="JSTS",
	version=VERSION,
	description=_DESCRIPTION_CONFIGS["JSTS"],
	),
	ds.BuilderConfig(
	name="JNLI",
	version=VERSION,
	description=_DESCRIPTION_CONFIGS["JNLI"],
	),
	ds.BuilderConfig(
	name="JSQuAD",
	version=VERSION,
	description=_DESCRIPTION_CONFIGS["JSQuAD"],
	),
	ds.BuilderConfig(
	name="JCommonsenseQA",
	version=VERSION,
	description=_DESCRIPTION_CONFIGS["JCommonsenseQA"],
	),
	]

	def _info(self) -> ds.DatasetInfo:
	if self.config.name == "JSTS":
	return dataset_info_jsts()
	elif self.config.name == "JNLI":
	return dataset_info_jnli()
	elif self.config.name == "JSQuAD":
	return dataset_info_jsquad()
	elif self.config.name == "JCommonsenseQA":
	return dataset_info_jcommonsenseqa()
	elif self.config.name == "MARC-ja":
	return dataset_info_marc_ja()
	else:
	raise ValueError(f"Invalid config name: {self.config.name}")

	def __split_generators_marc_ja(self, dl_manager: ds.DownloadManager):
	file_paths = dl_manager.download_and_extract(_URLS[self.config.name])

	filter_review_id_list = file_paths["filter_review_id_list"]
	label_conv_review_id_list = file_paths["label_conv_review_id_list"]

	try:
	split_dfs = preprocess_for_marc_ja(
	config=self.config,
	data_file_path=file_paths["data"],
	filter_review_id_list_paths=filter_review_id_list,
	label_conv_review_id_list_paths=label_conv_review_id_list,
	)
	except KeyError as err:
	from urllib.parse import urljoin

	logger.warning(err)

	base_url = "https://huggingface.co/datasets/shunk031/JGLUE/resolve/refs%2Fconvert%2Fparquet/MARC-ja/"
	marcja_parquet_urls = {
	"train": urljoin(base_url, "jglue-train.parquet"),
	"valid": urljoin(base_url, "jglue-validation.parquet"),
	}
	file_paths = dl_manager.download_and_extract(marcja_parquet_urls)
	split_dfs = {k: pd.read_parquet(v) for k, v in file_paths.items()}

	return [
	ds.SplitGenerator(
	name=ds.Split.TRAIN,
	gen_kwargs={"split_df": split_dfs["train"]},
	),
	ds.SplitGenerator(
	name=ds.Split.VALIDATION,
	gen_kwargs={"split_df": split_dfs["valid"]},
	),
	]

	def __split_generators(self, dl_manager: ds.DownloadManager):
	file_paths = dl_manager.download_and_extract(_URLS[self.config.name])
	return [
	ds.SplitGenerator(
	name=ds.Split.TRAIN,
	gen_kwargs={"file_path": file_paths["train"]},
	),
	ds.SplitGenerator(
	name=ds.Split.VALIDATION,
	gen_kwargs={"file_path": file_paths["valid"]},
	),
	]

	def _split_generators(self, dl_manager: ds.DownloadManager):
	if self.config.name == "MARC-ja":
	return self.__split_generators_marc_ja(dl_manager)
	else:
	return self.__split_generators(dl_manager)

	def __generate_examples_marc_ja(self, split_df: Optional[pd.DataFrame] = None):
	if split_df is None:
	raise ValueError(f"Invalid preprocessing for {self.config.name}")

	instances = split_df.to_dict(orient="records")
	for i, data_dict in enumerate(instances):
	yield i, data_dict

	def __generate_examples_jsquad(self, file_path: Optional[str] = None):
	if file_path is None:
	raise ValueError(f"Invalid argument for {self.config.name}")

	with open(file_path, "r", encoding="utf-8") as rf:
	json_data = json.load(rf)

	for json_dict in json_data["data"]:
	title = json_dict["title"]
	paragraphs = json_dict["paragraphs"]

	for paragraph in paragraphs:
	context = paragraph["context"]
	questions = paragraph["qas"]

	for question_dict in questions:
	q_id = question_dict["id"]
	question = question_dict["question"]
	answers = question_dict["answers"]
	is_impossible = question_dict["is_impossible"]

	example_dict = {
	"id": q_id,
	"title": title,
	"context": context,
	"question": question,
	"answers": answers,
	"is_impossible": is_impossible,
	}

	yield q_id, example_dict

	def __generate_examples_jcommonsenseqa(self, file_path: Optional[str] = None):
	if file_path is None:
	raise ValueError(f"Invalid argument for {self.config.name}")

	with open(file_path, "r", encoding="utf-8") as rf:
	for i, line in enumerate(rf):
	json_dict = json.loads(line)
	json_dict["label"] = f"choice{json_dict['label']}"
	yield i, json_dict

	def __generate_examples(self, file_path: Optional[str] = None):
	if file_path is None:
	raise ValueError(f"Invalid argument for {self.config.name}")

	with open(file_path, "r", encoding="utf-8") as rf:
	for i, line in enumerate(rf):
	json_dict = json.loads(line)
	yield i, json_dict

	def _generate_examples(
	self,
	file_path: Optional[str] = None,
	split_df: Optional[pd.DataFrame] = None,
	):
	if self.config.name == "MARC-ja":
	yield from self.__generate_examples_marc_ja(split_df)

	elif self.config.name == "JSQuAD":
	yield from self.__generate_examples_jsquad(file_path)

	elif self.config.name == "JCommonsenseQA":
	yield from self.__generate_examples_jcommonsenseqa(file_path)

	else:
	yield from self.__generate_examples(file_path)