Convert dataset to Parquet (#4)

- Convert dataset to Parquet (e786dbed53f517958523ffe25da2545eaa25f29a)
- Delete loading script (8a55af5cff4ea5980563e9637608ac23aa684074)

README.md

@@ -35,16 +35,25 @@ dataset_info:
       dtype: int32
   splits:
   - name: train
-    num_bytes: 15508110
+    num_bytes: 15508062
     num_examples: 10142
   - name: validation
-    num_bytes: 5183809
+    num_bytes: 5183785
     num_examples: 3219
   - name: test
-    num_bytes: 1606931
+    num_bytes: 1606907
     num_examples: 1002
-  download_size: 11508117
-  dataset_size: 22298850
+  download_size: 4896696
+  dataset_size: 22298754
+configs:
+- config_name: default
+  data_files:
+  - split: train
+    path: data/train-*
+  - split: validation
+    path: data/validation-*
+  - split: test
+    path: data/test-*
 ---
 
 # Dataset Card for "cmrc2018"

cmrc2018.py

@@ -1,123 +0,0 @@
-"""TODO(cmrc2018): Add a description here."""
-
-
-import json
-
-import datasets
-from datasets.tasks import QuestionAnsweringExtractive
-
-
-# TODO(cmrc2018): BibTeX citation
-_CITATION = """\
-@inproceedings{cui-emnlp2019-cmrc2018,
-    title = {A Span-Extraction Dataset for {C}hinese Machine Reading Comprehension},
-    author = {Cui, Yiming  and
-      Liu, Ting  and
-      Che, Wanxiang  and
-      Xiao, Li  and
-      Chen, Zhipeng  and
-      Ma, Wentao  and
-      Wang, Shijin  and
-      Hu, Guoping},
-    booktitle = {Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)},
-    month = {nov},
-    year = {2019},
-    address = {Hong Kong, China},
-    publisher = {Association for Computational Linguistics},
-    url = {https://www.aclweb.org/anthology/D19-1600},
-    doi = {10.18653/v1/D19-1600},
-    pages = {5886--5891}}
-"""
-
-# TODO(cmrc2018):
-_DESCRIPTION = """\
-A Span-Extraction dataset for Chinese machine reading comprehension to add language
-diversities in this area. The dataset is composed by near 20,000 real questions annotated
-on Wikipedia paragraphs by human experts. We also annotated a challenge set which
-contains the questions that need comprehensive understanding and multi-sentence
-inference throughout the context.
-"""
-_URL = "https://github.com/ymcui/cmrc2018"
-_TRAIN_FILE = "https://worksheets.codalab.org/rest/bundles/0x15022f0c4d3944a599ab27256686b9ac/contents/blob/"
-_DEV_FILE = "https://worksheets.codalab.org/rest/bundles/0x72252619f67b4346a85e122049c3eabd/contents/blob/"
-_TEST_FILE = "https://worksheets.codalab.org/rest/bundles/0x182c2e71fac94fc2a45cc1a3376879f7/contents/blob/"
-
-
-class Cmrc2018(datasets.GeneratorBasedBuilder):
-    """TODO(cmrc2018): Short description of my dataset."""
-
-    # TODO(cmrc2018): Set up version.
-    VERSION = datasets.Version("0.1.0")
-
-    def _info(self):
-        # TODO(cmrc2018): Specifies the datasets.DatasetInfo object
-        return datasets.DatasetInfo(
-            # This is the description that will appear on the datasets page.
-            description=_DESCRIPTION,
-            # datasets.features.FeatureConnectors
-            features=datasets.Features(
-                {
-                    "id": datasets.Value("string"),
-                    "context": datasets.Value("string"),
-                    "question": datasets.Value("string"),
-                    "answers": datasets.features.Sequence(
-                        {
-                            "text": datasets.Value("string"),
-                            "answer_start": datasets.Value("int32"),
-                        }
-                    ),
-                    # These are the features of your dataset like images, labels ...
-                }
-            ),
-            # If there's a common (input, target) tuple from the features,
-            # specify them here. They'll be used if as_supervised=True in
-            # builder.as_dataset.
-            supervised_keys=None,
-            # Homepage of the dataset for documentation
-            homepage=_URL,
-            citation=_CITATION,
-            task_templates=[
-                QuestionAnsweringExtractive(
-                    question_column="question", context_column="context", answers_column="answers"
-                )
-            ],
-        )
-
-    def _split_generators(self, dl_manager):
-        """Returns SplitGenerators."""
-        # TODO(cmrc2018): Downloads the data and defines the splits
-        # dl_manager is a datasets.download.DownloadManager that can be used to
-        # download and extract URLs
-        urls_to_download = {"train": _TRAIN_FILE, "dev": _DEV_FILE, "test": _TEST_FILE}
-        downloaded_files = dl_manager.download_and_extract(urls_to_download)
-
-        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"]}),
-            datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"filepath": downloaded_files["test"]}),
-        ]
-
-    def _generate_examples(self, filepath):
-        """Yields examples."""
-        # TODO(cmrc2018): Yields (key, example) tuples from the dataset
-        with open(filepath, encoding="utf-8") as f:
-            data = json.load(f)
-            for example in data["data"]:
-                for paragraph in example["paragraphs"]:
-                    context = paragraph["context"].strip()
-                    for qa in paragraph["qas"]:
-                        question = qa["question"].strip()
-                        id_ = qa["id"]
-
-                        answer_starts = [answer["answer_start"] for answer in qa["answers"]]
-                        answers = [answer["text"].strip() for answer in qa["answers"]]
-
-                        yield id_, {
-                            "context": context,
-                            "question": question,
-                            "id": id_,
-                            "answers": {
-                                "answer_start": answer_starts,
-                                "text": answers,
-                            },
-                        }

data/test-00000-of-00001.parquet

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+size 394683

data/train-00000-of-00001.parquet

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+size 3365898

data/validation-00000-of-00001.parquet

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+oid sha256:c3d3116e5845b8d3c44a863c1e2f973817c9f10a52e503b079ac49d4366f78b2
+size 1136115