Delete ChinaOpen.py

ChinaOpen.py

@@ -1,139 +0,0 @@
-
-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
-'''