Datasets:
eraser_multi_rc

	# coding=utf-8
	# Copyright 2020 The TensorFlow Datasets Authors and the HuggingFace Datasets Authors.
	#
	# Licensed under the Apache License, Version 2.0 (the "License");
	# you may not use this file except in compliance with the License.
	# You may obtain a copy of the License at
	#
	# http://www.apache.org/licenses/LICENSE-2.0
	#
	# Unless required by applicable law or agreed to in writing, software
	# distributed under the License is distributed on an "AS IS" BASIS,
	# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
	# See the License for the specific language governing permissions and
	# limitations under the License.

	# Lint as: python3
	"""Passage, query, answers and answer classification with explanations."""


	import json

	import datasets


	_CITATION = """
	@unpublished{eraser2019,
	title = {ERASER: A Benchmark to Evaluate Rationalized NLP Models},
	author = {Jay DeYoung and Sarthak Jain and Nazneen Fatema Rajani and Eric Lehman and Caiming Xiong and Richard Socher and Byron C. Wallace}
	}
	@inproceedings{MultiRC2018,
	author = {Daniel Khashabi and Snigdha Chaturvedi and Michael Roth and Shyam Upadhyay and Dan Roth},
	title = {Looking Beyond the Surface:A Challenge Set for Reading Comprehension over Multiple Sentences},
	booktitle = {NAACL},
	year = {2018}
	}
	"""

	_DESCRIPTION = """
	Eraser Multi RC is a dataset for queries over multi-line passages, along with
	answers and a rationalte. Each example in this dataset has the following 5 parts
	1. A Mutli-line Passage
	2. A Query about the passage
	3. An Answer to the query
	4. A Classification as to whether the answer is right or wrong
	5. An Explanation justifying the classification
	"""

	_DOWNLOAD_URL = "http://www.eraserbenchmark.com/zipped/multirc.tar.gz"


	class EraserMultiRc(datasets.GeneratorBasedBuilder):
	"""Multi Sentence Reasoning with Explanations (Eraser Benchmark)."""

	VERSION = datasets.Version("0.1.1")

	def _info(self):
	return datasets.DatasetInfo(
	description=_DESCRIPTION,
	features=datasets.Features(
	{
	"passage": datasets.Value("string"),
	"query_and_answer": datasets.Value("string"),
	"label": datasets.features.ClassLabel(names=["False", "True"]),
	"evidences": datasets.features.Sequence(datasets.Value("string")),
	}
	),
	supervised_keys=None,
	homepage="https://cogcomp.seas.upenn.edu/multirc/",
	citation=_CITATION,
	)

	def _split_generators(self, dl_manager):
	"""Returns SplitGenerators."""

	archive = dl_manager.download(_DOWNLOAD_URL)
	return [
	datasets.SplitGenerator(
	name=datasets.Split.TRAIN,
	# These kwargs will be passed to _generate_examples
	gen_kwargs={"files": dl_manager.iter_archive(archive), "split_file": "multirc/train.jsonl"},
	),
	datasets.SplitGenerator(
	name=datasets.Split.VALIDATION,
	# These kwargs will be passed to _generate_examples
	gen_kwargs={"files": dl_manager.iter_archive(archive), "split_file": "multirc/val.jsonl"},
	),
	datasets.SplitGenerator(
	name=datasets.Split.TEST,
	# These kwargs will be passed to _generate_examples
	gen_kwargs={"files": dl_manager.iter_archive(archive), "split_file": "multirc/test.jsonl"},
	),
	]

	def _generate_examples(self, files, split_file):
	"""Yields examples."""

	multirc_dir = "multirc/docs"
	docs = {}
	for path, f in files:
	docs[path] = f.read().decode("utf-8")
	for line in docs[split_file].splitlines():
	row = json.loads(line)
	evidences = []

	for evidence in row["evidences"][0]:
	docid = evidence["docid"]
	evidences.append(evidence["text"])

	passage_file = "/".join([multirc_dir, docid])
	passage_text = docs[passage_file]

	yield row["annotation_id"], {
	"passage": passage_text,
	"query_and_answer": row["query"],
	"label": row["classification"],
	"evidences": evidences,
	}