Datasets:
mocha

	# 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
	"""MOCHA: A Dataset for Training and Evaluating Generative Reading Comprehension Metrics"""


	import json

	import datasets


	logger = datasets.logging.get_logger(__name__)


	_CITATION = """\
	@inproceedings{Chen2020MOCHAAD,
	author={Anthony Chen and Gabriel Stanovsky and Sameer Singh and Matt Gardner},
	title={MOCHA: A Dataset for Training and Evaluating Generative Reading Comprehension Metrics},
	booktitle={EMNLP},
	year={2020}
	}
	"""

	_DESCRIPTION = """\
	Posing reading comprehension as a generation problem provides a great deal of flexibility, allowing for \
	open-ended questions with few restrictions on possible answers. However, progress is impeded by existing \
	generation metrics, which rely on token overlap and are agnostic to the nuances of reading comprehension. \
	To address this, we introduce a benchmark for training and evaluating generative reading comprehension metrics: \
	MOdeling Correctness with Human Annotations. MOCHA contains 40K human judgement scores on model outputs from \
	6 diverse question answering datasets and an additional set of minimal pairs for evaluation. Using MOCHA, \
	we train an evaluation metric: LERC, a Learned Evaluation metric for Reading Comprehension, to mimic human \
	judgement scores.
	"""

	_HOMEPAGE = "https://allennlp.org/mocha"

	_LICENSE = "https://creativecommons.org/licenses/by-sa/4.0/legalcode"

	_URL = "https://github.com/anthonywchen/MOCHA/raw/main/data/mocha.tar.gz"

	_MINIMAL_PAIRS_SPLIT = "minimal_pairs"

	SPLIT_FILENAMES = {
	datasets.Split.TRAIN: "train.json",
	datasets.Split.VALIDATION: "dev.json",
	datasets.Split.TEST: "test_no_labels.json",
	_MINIMAL_PAIRS_SPLIT: "minimal_pairs.json",
	}


	class Mocha(datasets.GeneratorBasedBuilder):
	"""MOCHA: A Dataset for Training and Evaluating Generative Reading Comprehension Metrics"""

	def _info(self):
	return datasets.DatasetInfo(
	description=_DESCRIPTION,
	features=datasets.Features(
	{
	"constituent_dataset": datasets.Value("string"),
	"id": datasets.Value("string"),
	"context": datasets.Value("string"),
	"question": datasets.Value("string"),
	"reference": datasets.Value("string"),
	"candidate": datasets.Value("string"),
	"score": datasets.Value("float"),
	"metadata": {
	"scores": datasets.features.Sequence(datasets.Value("int32")),
	"source": datasets.Value("string"),
	},
	# features for minimal pairs
	"candidate2": datasets.Value("string"),
	"score2": datasets.Value("float"),
	}
	),
	supervised_keys=None,
	homepage=_HOMEPAGE,
	license=_LICENSE,
	citation=_CITATION,
	)

	def _split_generators(self, dl_manager):
	archive = dl_manager.download(_URL)

	return [
	datasets.SplitGenerator(
	name=split,
	gen_kwargs={
	"filepath": "mocha/" + SPLIT_FILENAMES[split],
	"split": split,
	"files": dl_manager.iter_archive(archive),
	},
	)
	for split in SPLIT_FILENAMES
	]

	def _generate_examples(self, filepath, split, files):
	"""This function returns the examples in the raw (text) form."""
	for path, f in files:
	if path == filepath:
	mocha = json.load(f)
	for constituent_dataset, samples in mocha.items():
	for id_, sample in samples.items():
	sample["id"] = id_
	sample["constituent_dataset"] = constituent_dataset

	# Add default values
	if split == _MINIMAL_PAIRS_SPLIT:
	sample["candidate"] = sample["candidate1"]
	sample["score"] = sample["score1"]
	del sample["candidate1"], sample["score1"]
	sample["metadata"] = {"scores": [], "source": ""}
	else:
	if "score" not in sample:
	sample["score"] = -1.0
	sample["metadata"]["scores"] = []
	sample["candidate2"] = ""
	sample["score2"] = -1.0

	yield id_, sample
	break