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wiqa / wiqa.py

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Update files from the datasets library (from 1.6.0)

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	"""TODO(wiqa): Add a description here."""


	import json
	import os

	import datasets


	# TODO(wiqa): BibTeX citation
	_CITATION = """\
	@article{wiqa,
	author = {Niket Tandon and Bhavana Dalvi Mishra and Keisuke Sakaguchi and Antoine Bosselut and Peter Clark}
	title = {WIQA: A dataset for "What if..." reasoning over procedural text},
	journal = {arXiv:1909.04739v1},
	year = {2019},
	}
	"""

	# TODO(wiqa):
	_DESCRIPTION = """\
	The WIQA dataset V1 has 39705 questions containing a perturbation and a possible effect in the context of a paragraph.
	The dataset is split into 29808 train questions, 6894 dev questions and 3003 test questions.
	"""
	_URL = "https://public-aristo-processes.s3-us-west-2.amazonaws.com/wiqa_dataset_no_explanation_v2/wiqa-dataset-v2-october-2019.zip"
	URl = "s3://ai2-s2-research-public/open-corpus/2020-04-10/"


	class Wiqa(datasets.GeneratorBasedBuilder):
	"""TODO(wiqa): Short description of my dataset."""

	# TODO(wiqa): Set up version.
	VERSION = datasets.Version("0.1.0")

	def _info(self):
	# TODO(wiqa): 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(
	{
	# These are the features of your dataset like images, labels ...
	"question_stem": datasets.Value("string"),
	"question_para_step": datasets.features.Sequence(datasets.Value("string")),
	"answer_label": datasets.Value("string"),
	"answer_label_as_choice": datasets.Value("string"),
	"choices": datasets.features.Sequence(
	{"text": datasets.Value("string"), "label": datasets.Value("string")}
	),
	"metadata_question_id": datasets.Value("string"),
	"metadata_graph_id": datasets.Value("string"),
	"metadata_para_id": datasets.Value("string"),
	"metadata_question_type": datasets.Value("string"),
	"metadata_path_len": datasets.Value("int32"),
	}
	),
	# 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="https://allenai.org/data/wiqa",
	citation=_CITATION,
	)

	def _split_generators(self, dl_manager):
	"""Returns SplitGenerators."""
	# TODO(wiqa): Downloads the data and defines the splits
	# dl_manager is a datasets.download.DownloadManager that can be used to
	# download and extract URLs
	dl_dir = dl_manager.download_and_extract(_URL)

	return [
	datasets.SplitGenerator(
	name=datasets.Split.TRAIN,
	# These kwargs will be passed to _generate_examples
	gen_kwargs={"filepath": os.path.join(dl_dir, "train.jsonl")},
	),
	datasets.SplitGenerator(
	name=datasets.Split.TEST,
	# These kwargs will be passed to _generate_examples
	gen_kwargs={"filepath": os.path.join(dl_dir, "test.jsonl")},
	),
	datasets.SplitGenerator(
	name=datasets.Split.VALIDATION,
	# These kwargs will be passed to _generate_examples
	gen_kwargs={"filepath": os.path.join(dl_dir, "dev.jsonl")},
	),
	]

	def _generate_examples(self, filepath):
	"""Yields examples."""
	# TODO(wiqa): Yields (key, example) tuples from the dataset
	with open(filepath, encoding="utf-8") as f:
	for id_, row in enumerate(f):
	data = json.loads(row)

	yield id_, {
	"question_stem": data["question"]["stem"],
	"question_para_step": data["question"]["para_steps"],
	"answer_label": data["question"]["answer_label"],
	"answer_label_as_choice": data["question"]["answer_label_as_choice"],
	"choices": {
	"text": [choice["text"] for choice in data["question"]["choices"]],
	"label": [choice["label"] for choice in data["question"]["choices"]],
	},
	"metadata_question_id": data["metadata"]["ques_id"],
	"metadata_graph_id": data["metadata"]["graph_id"],
	"metadata_para_id": data["metadata"]["para_id"],
	"metadata_question_type": data["metadata"]["question_type"],
	"metadata_path_len": data["metadata"]["path_len"],
	}