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Delete testsq.py

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- """TODO(squad_v2): Add a description here."""
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-
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-
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- import json
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-
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- import datasets
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- # from datasets.tasks import QuestionAnsweringExtractive
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-
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-
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- # TODO(squad_v2): BibTeX citation
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- _CITATION = """\
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- @article{2016arXiv160605250R,
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- author = {{Rajpurkar}, Pranav and {Zhang}, Jian and {Lopyrev},
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- Konstantin and {Liang}, Percy},
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- title = "{SQuAD: 100,000+ Questions for Machine Comprehension of Text}",
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- journal = {arXiv e-prints},
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- year = 2016,
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- eid = {arXiv:1606.05250},
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- pages = {arXiv:1606.05250},
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- archivePrefix = {arXiv},
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- eprint = {1606.05250},
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- }
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- """
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-
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- _DESCRIPTION = """\
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- combines the 100,000 questions in SQuAD1.1 with over 50,000 unanswerable questions written adversarially by crowdworkers
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- to look similar to answerable ones. To do well on SQuAD2.0, systems must not only answer questions when possible, but
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- also determine when no answer is supported by the paragraph and abstain from answering.
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- """
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-
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- _URL = "https://huggingface.co/datasets/prk/testsq/blob/main/"
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- _URLS = {
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- "train": _URL + "answers.json",
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- "dev": _URL + "answers_test.json",
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- }
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-
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-
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- class SquadV2Config(datasets.BuilderConfig):
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- """BuilderConfig for SQUAD."""
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-
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- def __init__(self, **kwargs):
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- """BuilderConfig for SQUADV2.
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- Args:
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- **kwargs: keyword arguments forwarded to super.
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- """
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- super(SquadV2Config, self).__init__(**kwargs)
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-
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-
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- class SquadV2(datasets.GeneratorBasedBuilder):
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- """TODO(squad_v2): Short description of my dataset."""
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-
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- # TODO(squad_v2): Set up version.
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- BUILDER_CONFIGS = [
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- SquadV2Config(name="squad_v2", version=datasets.Version("2.0.0"), description="SQuAD plaint text version 2"),
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- ]
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-
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- def _info(self):
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- # TODO(squad_v2): Specifies the datasets.DatasetInfo object
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- return datasets.DatasetInfo(
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- # This is the description that will appear on the datasets page.
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- description=_DESCRIPTION,
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- # datasets.features.FeatureConnectors
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- features=datasets.Features(
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- {
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- "id": datasets.Value("string"),
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- "title": datasets.Value("string"),
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- "context": datasets.Value("string"),
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- "question": datasets.Value("string"),
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- "answers": datasets.features.Sequence(
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- {
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- "text": datasets.Value("string"),
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- "answer_start": datasets.Value("int32"),
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- }
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- ),
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- # These are the features of your dataset like images, labels ...
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- }
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- ),
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- # If there's a common (input, target) tuple from the features,
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- # specify them here. They'll be used if as_supervised=True in
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- # builder.as_dataset.
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- supervised_keys=None,
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- # Homepage of the dataset for documentation
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- homepage="https://rajpurkar.github.io/SQuAD-explorer/",
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- citation=_CITATION,
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- # task_templates=[
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- # QuestionAnsweringExtractive(
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- # question_column="question", context_column="context", answers_column="answers"
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- # )
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- # ],
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- )
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-
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- def _split_generators(self, dl_manager):
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- """Returns SplitGenerators."""
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- # TODO(squad_v2): Downloads the data and defines the splits
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- # dl_manager is a datasets.download.DownloadManager that can be used to
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- # download and extract URLs
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- urls_to_download = _URLS
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- downloaded_files = dl_manager.download_and_extract(urls_to_download)
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-
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- return [
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- datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": downloaded_files["train"]}),
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- datasets.SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs={"filepath": downloaded_files["dev"]}),
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- ]
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-
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- def _generate_examples(self, filepath):
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- """Yields examples."""
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- # TODO(squad_v2): Yields (key, example) tuples from the dataset
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- with open(filepath, encoding="utf-8") as f:
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- squad = json.load(f)
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- for example in squad["data"]:
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- title = example.get("title", "")
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- for paragraph in example["paragraphs"]:
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- context = paragraph["context"] # do not strip leading blank spaces GH-2585
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- for qa in paragraph["qas"]:
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- question = qa["question"]
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- id_ = qa["id"]
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-
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- answer_starts = [answer["answer_start"] for answer in qa["answers"]]
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- answers = [answer["text"] for answer in qa["answers"]]
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-
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- # Features currently used are "context", "question", and "answers".
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- # Others are extracted here for the ease of future expansions.
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- yield id_, {
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- "title": title,
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- "context": context,
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- "question": question,
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- "id": id_,
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- "answers": {
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- "answer_start": answer_starts,
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- "text": answers,
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- },
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- }