Datasets:
movie_rationales

	# 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
	"""Movie reviews with human annotated rationales."""

	from __future__ import absolute_import, division, print_function

	import json
	import os

	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	@unpublished{eraser2019,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{zaidan-eisner-piatko-2008:nips,
	author = {Omar F. Zaidan and Jason Eisner and Christine Piatko},
	title = {Machine Learning with Annotator Rationales to Reduce Annotation Cost},
	booktitle = {Proceedings of the NIPS*2008 Workshop on Cost Sensitive Learning},
	month = {December},
	year = {2008}
	}
	"""

	_DESCRIPTION = """
	The movie rationale dataset contains human annotated rationales for movie
	reviews.
	"""

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


	class MovieRationales(datasets.GeneratorBasedBuilder):
	"""Movie reviews with human annotated rationales."""

	VERSION = datasets.Version("0.1.0")

	def _info(self):
	return datasets.DatasetInfo(
	description=_DESCRIPTION,
	features=datasets.Features(
	{
	"review": datasets.Value("string"),
	"label": datasets.features.ClassLabel(names=["NEG", "POS"]),
	"evidences": datasets.features.Sequence(datasets.Value("string")),
	}
	),
	supervised_keys=None,
	homepage="http://www.cs.jhu.edu/~ozaidan/rationales/",
	citation=_CITATION,
	)

	def _split_generators(self, dl_manager):
	"""Returns SplitGenerators."""
	dl_dir = dl_manager.download_and_extract(_DOWNLOAD_URL)
	data_dir = os.path.join(dl_dir, "movies")

	return [
	datasets.SplitGenerator(
	name=datasets.Split.TRAIN,
	gen_kwargs={"data_dir": data_dir, "filepath": os.path.join(data_dir, "train.jsonl")},
	),
	datasets.SplitGenerator(
	name=datasets.Split.VALIDATION,
	gen_kwargs={"data_dir": data_dir, "filepath": os.path.join(data_dir, "val.jsonl")},
	),
	datasets.SplitGenerator(
	name=datasets.Split.TEST,
	gen_kwargs={"data_dir": data_dir, "filepath": os.path.join(data_dir, "test.jsonl")},
	),
	]

	def _generate_examples(self, data_dir, filepath):
	"""Yields examples."""
	reviews_dir = os.path.join(data_dir, "docs")

	with open(filepath, encoding="utf-8") as f:
	for line in f:
	row = json.loads(line)
	doc_id = row["annotation_id"]
	review_file = os.path.join(reviews_dir, doc_id)
	with open(review_file, encoding="utf-8") as f1:
	review_text = f1.read()

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

	yield doc_id, {
	"review": review_text,
	"label": row["classification"],
	"evidences": evidences,
	}