Datasets:
Tasks:
Question Answering
Sub-tasks:
extractive-qa
Languages:
English
Size:
1K<n<10K
ArXiv:
Tags:
multi-hop
License:
File size: 4,030 Bytes
dfa76dd |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 |
# 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.
"""MedHop: 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 = """\
MedHop is based on research paper abstracts from PubMed, and the queries are about interactions between pairs of drugs. \
The correct answer has to be inferred by combining information from a chain of reactions of drugs and proteins.
"""
_URL = "https://drive.google.com/uc?export=download&id=1ytVZ4AhubFDOEL7o7XrIRIyhU8g9wvKA"
class MedHopConfig(datasets.BuilderConfig):
"""BuilderConfig for MedHop."""
def __init__(self, masked=False, **kwargs):
"""BuilderConfig for MedHop.
Args:
masked: `bool`, original or maksed data.
**kwargs: keyword arguments forwarded to super.
"""
super(MedHopConfig, self).__init__(**kwargs)
self.masked = masked
class MedHop(datasets.GeneratorBasedBuilder):
"""MedHop: Reading Comprehension with Multiple Hops"""
VERSION = datasets.Version("1.0.0")
BUILDER_CONFIGS = [
MedHopConfig(
name="original",
version=datasets.Version("1.0.0"),
description="The un-maksed MedHop dataset",
masked=False,
),
MedHopConfig(
name="masked", version=datasets.Version("1.0.0"), description="Masked MedHop dataset", masked=True
),
]
BUILDER_CONFIG_CLASS = MedHopConfig
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")),
}
),
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)
medhop_path = os.path.join(extracted_path, "qangaroo_v1.1", "medhop")
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(medhop_path, train_file)},
),
datasets.SplitGenerator(
name=datasets.Split.VALIDATION,
gen_kwargs={"filepath": os.path.join(medhop_path, dev_file)},
),
]
def _generate_examples(self, filepath):
with open(filepath, encoding="utf-8") as f:
examples = json.load(f)
for i, example in enumerate(examples):
example["question"] = example.pop("query")
yield example["id"], example
|