Datasets:
Tasks:
Question Answering
Sub-tasks:
extractive-qa
Languages:
English
Size:
10K<n<100K
ArXiv:
Tags:
multi-hop
License:
File size: 4,375 Bytes
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# coding=utf-8
# Copyright 2020 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.
"""WikiHop: Reading Comprehension with Multiple Hops"""
import json
import os
import datasets
_CITATION = """\
@misc{welbl2018constructing,
title={Constructing Datasets for Multi-hop Reading Comprehension Across Documents},
author={Johannes Welbl and Pontus Stenetorp and Sebastian Riedel},
year={2018},
eprint={1710.06481},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
"""
_DESCRIPTION = """\
WikiHop is open-domain and based on Wikipedia articles; the goal is to recover Wikidata information by hopping through documents. \
The goal is to answer text understanding queries by combining multiple facts that are spread across different documents.
"""
_URL = "https://drive.google.com/uc?export=download&id=1ytVZ4AhubFDOEL7o7XrIRIyhU8g9wvKA"
class WikiHopConfig(datasets.BuilderConfig):
"""BuilderConfig for WikiHop."""
def __init__(self, masked=False, **kwargs):
"""BuilderConfig for WikiHop.
Args:
masked: `bool`, original or maksed data.
**kwargs: keyword arguments forwarded to super.
"""
super(WikiHopConfig, self).__init__(**kwargs)
self.masked = masked
class WikiHop(datasets.GeneratorBasedBuilder):
"""WikiHop: Reading Comprehension with Multiple Hops"""
VERSION = datasets.Version("1.0.0")
BUILDER_CONFIGS = [
WikiHopConfig(
name="original",
version=datasets.Version("1.0.0"),
description="The un-maksed WikiHop dataset",
masked=False,
),
WikiHopConfig(
name="masked", version=datasets.Version("1.0.0"), description="Masked WikiHop dataset", masked=True
),
]
BUILDER_CONFIG_CLASS = WikiHopConfig
DEFAULT_CONFIG_NAME = "original"
def _info(self):
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=datasets.Features(
{
"id": datasets.Value("string"),
"question": datasets.Value("string"),
"answer": datasets.Value("string"),
"candidates": datasets.Sequence(datasets.Value("string")),
"supports": datasets.Sequence(datasets.Value("string")),
"annotations": datasets.Sequence(datasets.Sequence(datasets.Value("string"))),
}
),
supervised_keys=None,
homepage="http://qangaroo.cs.ucl.ac.uk/",
citation=_CITATION,
)
def _split_generators(self, dl_manager):
extracted_path = dl_manager.download_and_extract(_URL)
wikihop_path = os.path.join(extracted_path, "qangaroo_v1.1", "wikihop")
train_file = "train.json" if self.config.name == "original" else "train.masked.json"
dev_file = "dev.json" if self.config.name == "original" else "dev.masked.json"
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={"filepath": os.path.join(wikihop_path, train_file), "split": "train"},
),
datasets.SplitGenerator(
name=datasets.Split.VALIDATION,
gen_kwargs={"filepath": os.path.join(wikihop_path, dev_file), "split": "dev"},
),
]
def _generate_examples(self, filepath, split):
with open(filepath, encoding="utf-8") as f:
examples = json.load(f)
for i, example in enumerate(examples):
# there are no annotations for train split, setting it to empty list
if split == "train":
example["annotations"] = []
example["question"] = example.pop("query")
yield example["id"], example
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