Update parquet files

.gitattributes

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-*.7z filter=lfs diff=lfs merge=lfs -text
-*.arrow filter=lfs diff=lfs merge=lfs -text
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-*.parquet filter=lfs diff=lfs merge=lfs -text
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-*.pt filter=lfs diff=lfs merge=lfs -text
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-*tfevents* filter=lfs diff=lfs merge=lfs -text
-train/ filter=lfs diff=lfs merge=lfs -text
-validation/ filter=lfs diff=lfs merge=lfs -text
-dataset_dict.json filter=lfs diff=lfs merge=lfs -text
-korquad_v1.0_train.json filter=lfs diff=lfs merge=lfs -text
-KorQuAD_v1.0_train.json filter=lfs diff=lfs merge=lfs -text
-KorQuAD_v1.0_dev.json filter=lfs diff=lfs merge=lfs -text

KorQuADv1.py

@@ -1,103 +0,0 @@
-import datasets
-import pandas as pd
-
-_DATA_URLS = {
-    "train": "https://korquad.github.io/dataset/KorQuAD_v1.0_train.json",
-    "dev": "https://korquad.github.io/dataset/KorQuAD_v1.0_dev.json",
-}
-_VERSION = "0.0.0"
-_LICENSE = "CC BY-ND 2.0 KR"
-_CITATION = """\
-@misc{lim2019korquad1.0,
-      title={KorQuAD1.0: Korean QA Dataset for Machine Reading Comprehension},
-      author={Seungyoung Lim, Myungji Kim, Jooyoul Lee},
-      year={2019},
-      eprint={1909.07005},
-      archivePrefix={arXiv},
-      primaryClass={cs.CL}
-}
-"""
-_HOMEPAGE = "https://korquad.github.io/KorQuad%201.0/"
-_DESCRIPTION = """\
-KorQuAD 1.0 (Korean Question Answering Dataset v1.0)
-KorQuAD 1.0 is a dataset created for Korean Machine Reading Comprehension.
-The answers to all your questions are made up of some subareas in the corresponding Wikipedia article paragraphs.
-It is structured in the same way as the Stanford Question Answering Dataset (SQuAD) v1.0.
-"""
-
-
-class KorQuADV1Config(datasets.BuilderConfig):
-    def __init__(self, data_url, features, **kwargs):
-        super().__init__(version=datasets.Version(_VERSION, ""), **kwargs)
-        self.features = features
-        self.data_url = data_url
-        
-
-class KorQuADV1(datasets.GeneratorBasedBuilder):
-    DEFAULT_CONFIG_NAME = "korquad"
-    BUILDER_CONFIGS = [
-        KorQuADV1Config(
-            name="korquad",
-            data_url=_DATA_URLS,
-            features=datasets.Features(
-                {
-                    "answers": datasets.Sequence(
-                        feature={
-                            "text": datasets.Value(dtype="string"),
-                            "answer_start": datasets.Value(dtype="int32"),
-                        },
-                    ),
-                    "context": datasets.Value(dtype="string"),
-                    "guid": datasets.Value(dtype="string"),
-                    "question": datasets.Value(dtype="string"),
-                    "title": datasets.Value(dtype="string"),
-                }
-            )
-        ),
-    ]
-    
-    def _info(self):
-        return datasets.DatasetInfo(
-            features=self.config.features,
-            description=_DESCRIPTION,
-            homepage=_HOMEPAGE,
-            citation=_CITATION,
-            license=_LICENSE,
-        )
-    
-    def _split_generators(self, dl_manager):
-        data_path = dl_manager.download(self.config.data_url)
-        return [
-            datasets.SplitGenerator(
-                name=datasets.Split.TRAIN,
-                # These kwargs will be passed to _generate_examples
-                gen_kwargs={"data_file": data_path["train"]}
-            ),
-            datasets.SplitGenerator(
-                name=datasets.Split.VALIDATION,
-                # These kwargs will be passed to _generate_examples
-                gen_kwargs={"data_file": data_path["dev"]}
-            ),
-        ]
-    
-    def _generate_examples(self, data_file: str):
-        idx = 0
-        korquad = pd.read_json(data_file)
-        for example in korquad["data"].tolist():
-            paragraphs = example["paragraphs"]
-            title = example["title"]
-            for paragraph in paragraphs:
-                qas = paragraph["qas"]
-                context = paragraph["context"]
-                for qa in qas:
-                    text = [answers["text"] for answers in qa["answers"]]
-                    answer_start = [answers["answer_start"] for answers in qa["answers"]]
-                    features = {
-                        "guid": qa["id"],
-                        "question": qa["question"],
-                        "answers": {"text": text, "answer_start": answer_start},
-                        "context": context,
-                        "title": title,
-                    }
-                    yield idx, features
-                    idx += 1

dataset_infos.json

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-{"korquad": {"description": "KorQuAD 1.0 (Korean Question Answering Dataset v1.0)\nKorQuAD 1.0 is a dataset created for Korean Machine Reading Comprehension.\nThe answers to all your questions are made up of some subareas in the corresponding Wikipedia article paragraphs.\nIt is structured in the same way as the Stanford Question Answering Dataset (SQuAD) v1.0.\n", "citation": "@misc{lim2019korquad1.0,\n      title={KorQuAD1.0: Korean QA Dataset for Machine Reading Comprehension},\n      author={Seungyoung Lim, Myungji Kim, Jooyoul Lee},\n      year={2019},\n      eprint={1909.07005},\n      archivePrefix={arXiv},\n      primaryClass={cs.CL}\n}\n", "homepage": "https://korquad.github.io/KorQuad%201.0/", "license": "CC BY-ND 2.0 KR", "features": {"answers": {"feature": {"text": {"dtype": "string", "id": null, "_type": "Value"}, "answer_start": {"dtype": "int32", "id": null, "_type": "Value"}}, "length": -1, "id": null, "_type": "Sequence"}, "context": {"dtype": "string", "id": null, "_type": "Value"}, "guid": {"dtype": "string", "id": null, "_type": "Value"}, "question": {"dtype": "string", "id": null, "_type": "Value"}, "title": {"dtype": "string", "id": null, "_type": "Value"}}, "post_processed": null, "supervised_keys": null, "task_templates": null, "builder_name": "kor_qu_adv1", "config_name": "korquad", "version": {"version_str": "0.0.0", "description": "", "major": 0, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 83382906, "num_examples": 60407, "dataset_name": "kor_qu_adv1"}, "validation": {"name": "validation", "num_bytes": 8261968, "num_examples": 5774, "dataset_name": "kor_qu_adv1"}}, "download_checksums": {"https://korquad.github.io/dataset/KorQuAD_v1.0_train.json": {"num_bytes": 38527475, "checksum": "40d5115879a701751781df721d901abfa736d8db5f89000f2619433f39bf2dd2"}, "https://korquad.github.io/dataset/KorQuAD_v1.0_dev.json": {"num_bytes": 3881058, "checksum": "25ffeb51e6c51ec02c071b60a10188e10005c144110f0d876b26079d80a35bdf"}}, "download_size": 42408533, "post_processing_size": null, "dataset_size": 91644874, "size_in_bytes": 134053407}}

KorQuAD_v1.0_train.json → korquad/kor_qu_a_dv1-train.parquet

@@ -1,3 +1,3 @@
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+size 11557113

KorQuAD_v1.0_dev.json → korquad/kor_qu_a_dv1-validation.parquet

@@ -1,3 +1,3 @@
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