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Update files from the datasets library (from 1.0.0)

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Release notes: https://github.com/huggingface/datasets/releases/tag/1.0.0

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  1. .gitattributes +27 -0
  2. dataset_infos.json +1 -0
  3. dummy/0.1.0/dummy_data.zip +3 -0
  4. quoref.py +117 -0
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+ *.7z filter=lfs diff=lfs merge=lfs -text
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dataset_infos.json ADDED
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+ {"default": {"description": "Quoref is a QA dataset which tests the coreferential reasoning capability of reading comprehension systems. In this \nspan-selection benchmark containing 24K questions over 4.7K paragraphs from Wikipedia, a system must resolve hard \ncoreferences before selecting the appropriate span(s) in the paragraphs for answering questions.\n", "citation": "@article{allenai:quoref,\n author = {Pradeep Dasigi and Nelson F. Liu and Ana Marasovic and Noah A. Smith and Matt Gardner},\n title = {Quoref: A Reading Comprehension Dataset with Questions Requiring Coreferential Reasoning},\n journal = {arXiv:1908.05803v2 },\n year = {2019},\n}\n", "homepage": "https://leaderboard.allenai.org/quoref/submissions/get-started", "license": "", "features": {"id": {"dtype": "string", "id": null, "_type": "Value"}, "question": {"dtype": "string", "id": null, "_type": "Value"}, "context": {"dtype": "string", "id": null, "_type": "Value"}, "title": {"dtype": "string", "id": null, "_type": "Value"}, "url": {"dtype": "string", "id": null, "_type": "Value"}, "answers": {"feature": {"answer_start": {"dtype": "int32", "id": null, "_type": "Value"}, "text": {"dtype": "string", "id": null, "_type": "Value"}}, "length": -1, "id": null, "_type": "Sequence"}}, "supervised_keys": null, "builder_name": "quoref", "config_name": "default", "version": {"version_str": "0.1.0", "description": null, "datasets_version_to_prepare": null, "major": 0, "minor": 1, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 44377729, "num_examples": 19399, "dataset_name": "quoref"}, "validation": {"name": "validation", "num_bytes": 5442031, "num_examples": 2418, "dataset_name": "quoref"}}, "download_checksums": {"https://quoref-dataset.s3-us-west-2.amazonaws.com/train_and_dev/quoref-train-dev-v0.1.zip": {"num_bytes": 5078438, "checksum": "aacde0863c04ba6e9ab46995ea844a5b0c6cea58a77ab6fd86a128e33a3ad8fb"}}, "download_size": 5078438, "dataset_size": 49819760, "size_in_bytes": 54898198}}
dummy/0.1.0/dummy_data.zip ADDED
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+ size 3274
quoref.py ADDED
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+ """TODO(quoref): Add a description here."""
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+
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+ from __future__ import absolute_import, division, print_function
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+
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+ import json
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+ import os
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+
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+ import datasets
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+
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+
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+ # TODO(quoref): BibTeX citation
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+ _CITATION = """\
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+ @article{allenai:quoref,
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+ author = {Pradeep Dasigi and Nelson F. Liu and Ana Marasovic and Noah A. Smith and Matt Gardner},
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+ title = {Quoref: A Reading Comprehension Dataset with Questions Requiring Coreferential Reasoning},
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+ journal = {arXiv:1908.05803v2 },
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+ year = {2019},
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+ }
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+ """
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+
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+ # TODO(quoref):
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+ _DESCRIPTION = """\
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+ Quoref is a QA dataset which tests the coreferential reasoning capability of reading comprehension systems. In this
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+ span-selection benchmark containing 24K questions over 4.7K paragraphs from Wikipedia, a system must resolve hard
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+ coreferences before selecting the appropriate span(s) in the paragraphs for answering questions.
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+ """
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+
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+ _URL = "https://quoref-dataset.s3-us-west-2.amazonaws.com/train_and_dev/quoref-train-dev-v0.1.zip"
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+
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+
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+ class Quoref(datasets.GeneratorBasedBuilder):
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+ """TODO(quoref): Short description of my dataset."""
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+
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+ # TODO(quoref): Set up version.
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+ VERSION = datasets.Version("0.1.0")
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+
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+ def _info(self):
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+ # TODO(quoref): 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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+ "question": datasets.Value("string"),
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+ "context": datasets.Value("string"),
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+ "title": datasets.Value("string"),
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+ "url": datasets.Value("string"),
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+ "answers": datasets.features.Sequence(
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+ {
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+ "answer_start": datasets.Value("int32"),
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+ "text": datasets.Value("string"),
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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://leaderboard.allenai.org/quoref/submissions/get-started",
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+ citation=_CITATION,
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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(quoref): 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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+ dl_dir = dl_manager.download_and_extract(_URL)
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+ data_dir = os.path.join(dl_dir, "quoref-train-dev-v0.1")
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+ return [
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+ datasets.SplitGenerator(
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+ name=datasets.Split.TRAIN,
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+ # These kwargs will be passed to _generate_examples
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+ gen_kwargs={"filepath": os.path.join(data_dir, "quoref-train-v0.1.json")},
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+ ),
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+ datasets.SplitGenerator(
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+ name=datasets.Split.VALIDATION,
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+ # These kwargs will be passed to _generate_examples
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+ gen_kwargs={"filepath": os.path.join(data_dir, "quoref-dev-v0.1.json")},
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+ ),
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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(quoref): Yields (key, example) tuples from the dataset
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+ with open(filepath, encoding="utf-8") as f:
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+ data = json.load(f)
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+ for article in data["data"]:
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+ title = article.get("title", "").strip()
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+ url = article.get("url", "").strip()
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+ for paragraph in article["paragraphs"]:
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+ context = paragraph["context"].strip()
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+ for qa in paragraph["qas"]:
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+ question = qa["question"].strip()
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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"].strip() 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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+ "url": url,
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+ }