Delete loading script

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,
- },
- }