Datasets:
Tasks:
Multiple Choice
Sub-tasks:
multiple-choice-qa
Languages:
English
Multilinguality:
monolingual
Size Categories:
10K<n<100K
Language Creators:
found
Annotations Creators:
crowdsourced
Source Datasets:
original
License:
File size: 4,472 Bytes
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# 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 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@unpublished{eraser2019,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{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."""
dl_dir = dl_manager.download_and_extract(_DOWNLOAD_URL)
data_dir = os.path.join(dl_dir, "multirc")
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
# These kwargs will be passed to _generate_examples
gen_kwargs={"data_dir": data_dir, "filepath": os.path.join(data_dir, "train.jsonl")},
),
datasets.SplitGenerator(
name=datasets.Split.VALIDATION,
# These kwargs will be passed to _generate_examples
gen_kwargs={"data_dir": data_dir, "filepath": os.path.join(data_dir, "val.jsonl")},
),
datasets.SplitGenerator(
name=datasets.Split.TEST,
# These kwargs will be passed to _generate_examples
gen_kwargs={"data_dir": data_dir, "filepath": os.path.join(data_dir, "test.jsonl")},
),
]
def _generate_examples(self, data_dir, filepath):
"""Yields examples."""
multirc_dir = os.path.join(data_dir, "docs")
with open(filepath, encoding="utf-8") as f:
for line in f:
row = json.loads(line)
evidences = []
for evidence in row["evidences"][0]:
docid = evidence["docid"]
evidences.append(evidence["text"])
passage_file = os.path.join(multirc_dir, docid)
with open(passage_file, encoding="utf-8") as f1:
passage_text = f1.read()
yield row["annotation_id"], {
"passage": passage_text,
"query_and_answer": row["query"],
"label": row["classification"],
"evidences": evidences,
}
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