ChinaOpen.py

import json

import datasets
from datasets.tasks import QuestionAnsweringExtractive


logger = datasets.logging.get_logger(__name__)

_CITATION = """\
@inproceedings{10.1145/3581783.3612156,
    author = {Chen, Aozhu and Wang, Ziyuan and Dong, Chengbo and Tian, Kaibin and Zhao, Ruixiang and Liang, Xun and Kang, Zhanhui and Li, Xirong},
    title = {ChinaOpen: A Dataset for Open-World Multimodal Learning},
    year = {2023},
    isbn = {9798400701085},
    publisher = {Association for Computing Machinery},
    address = {New York, NY, USA},
    url = {https://doi.org/10.1145/3581783.3612156},
    doi = {10.1145/3581783.3612156},
    booktitle = {Proceedings of the 31st ACM International Conference on Multimedia},
    pages = {6432–6440},
    numpages = {9},
    keywords = {chinese video dataset, multi-task evaluation, multimodal learning},
    location = {Ottawa ON, Canada},
    series = {MM '23}
}
"""



_DESCRIPTION = """\
ChinaOpen is a dataset sourced from Bilibili, a popular Chinese video-sharing website, \
for open-world multimodal learning. While the state-of-the-art multimodal learning \
networks have shown impressive performance in automated video annotation and \
cross-modal video retrieval, their training and evaluation are primarily conducted on \
YouTube videos with English text. Their effectiveness on Chinese data remains to be \
verified. For a multi-faceted evaluation, we build ChinaOpen-1k, a manually labeled \
test set of 1k videos. Each test video is accompanied with a manually checked user \
title and a manually written caption. Besides, each video is manually tagged to describe \
objects / actions / scenes shown in the visual content. The original user tags are also \
manually checked. Moreover, with all the Chinese text translated into English, \
ChinaOpen-1k is also suited for evaluating models trained on English data. \   

"""



#_URL = "./ChinaOpen-1k.zip"
_URLS = {
    "train": "https://huggingface.co/datasets/AIMClab/ChinaOpen/resolve/main/ChinaOpen-1k.zip"
}



class ChinaOpenConfig(datasets.BuilderConfig):
    """BuilderConfig for SQUAD."""

    def __init__(self, **kwargs):
        """BuilderConfig for SQUAD.
        Args:
          **kwargs: keyword arguments forwarded to super.
        """
        super(ChinaOpenConfig, self).__init__(**kwargs)


class ChinaOpen(datasets.GeneratorBasedBuilder):
    """SQUAD: The Stanford Question Answering Dataset. Version 1.1."""

    BUILDER_CONFIGS = [
        ChinaOpenConfig(
            name="plain_text",
            version=datasets.Version("1.0.0", ""),
            description="Plain text",
        ),
    ]

    def _info(self):
        return datasets.DatasetInfo(
            description=_DESCRIPTION,
            features=datasets.Features(
                {
                    "id": datasets.Value("string"),
                    "title": datasets.Value("string"),
                    "context": datasets.Value("string"),
                    "question": datasets.Value("string"),
                    "answers": datasets.features.Sequence(
                        {
                            "text": datasets.Value("string"),
                            "answer_start": datasets.Value("int32"),
                        }
                    ),
                }
            ),
            # No default supervised_keys (as we have to pass both question
            # and context as input).
            supervised_keys=None,
            citation=_CITATION,
            task_templates=[
                QuestionAnsweringExtractive(
                    question_column="question", context_column="context", answers_column="answers"
                )
            ],
        )

    def _split_generators(self, dl_manager):
        downloaded_files = dl_manager.download_and_extract(_URLS)

        return [
            datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": downloaded_files["train"]}),
            #datasets.SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs={"filepath": downloaded_files["dev"]}),
        ]
'''
    def _generate_examples(self, filepath):
        """This function returns the examples in the raw (text) form."""
        logger.info("generating examples from = %s", filepath)
        key = 0
        with open(filepath, encoding="utf-8") as f:
            squad = json.load(f)
            for article in squad["data"]:
                title = article.get("title", "")
                for paragraph in article["paragraphs"]:
                    context = paragraph["context"]  # do not strip leading blank spaces GH-2585
                    for qa in paragraph["qas"]:
                        answer_starts = [answer["answer_start"] for answer in qa["answers"]]
                        answers = [answer["text"] for answer in qa["answers"]]
                        # Features currently used are "context", "question", and "answers".
                        # Others are extracted here for the ease of future expansions.
                        yield key, {
                            "title": title,
                            "context": context,
                            "question": qa["question"],
                            "id": qa["id"],
                            "answers": {
                                "answer_start": answer_starts,
                                "text": answers,
                            },
                        }
                        key += 1
'''