JGLUE.py

"""
This code is licensed under CC-BY-4.0 from the original work by shunk031.
The code is adapted from https://huggingface.co/datasets/shunk031/JGLUE/blob/main/JGLUE.py
with minor modifications to the code structure.
"""


import json
from typing import Optional

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

from .preprocess_marc_ja import preprocess_marc_ja, MarcJaConfig

_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.DatasetInfo:
    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.DatasetInfo:
    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.DatasetInfo:
    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.DatasetInfo:
    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(remove_netural: bool) -> ds.DatasetInfo:
    labels = ["positive", "negative"] if remove_netural else ["positive", "negative", "neutral"]
    features = ds.Features(
        {
            "sentence": ds.Value("string"),
            "label": ds.ClassLabel(num_classes=len(labels), names=labels),
            "review_id": ds.Value("string"),
        }
    )
    return ds.DatasetInfo(
        description=_DESCRIPTION,
        citation=_CITATION,
        homepage=_HOMEPAGE,
        license=_LICENSE,
        features=features,
    )


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(self.config.remove_netural)
        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"]

        split_dfs = preprocess_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,
        )
        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") 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") 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):
        if file_path is None:
            raise ValueError(f"Invalid argument for {self.config.name}")

        with open(file_path, "r") 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)