Datasets:
Tasks:
Question Answering
Sub-tasks:
open-domain-qa
Languages:
English
Size:
100K<n<1M
ArXiv:
License:
File size: 4,992 Bytes
aee3e40 d082105 aee3e40 |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 |
# coding=utf-8
# Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
#
# 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.
import json
import os
import datasets
_CITATION = """\
@misc{efrat2021cryptonite,
title={Cryptonite: A Cryptic Crossword Benchmark for Extreme Ambiguity in Language},
author={Avia Efrat and Uri Shaham and Dan Kilman and Omer Levy},
year={2021},
eprint={2103.01242},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
"""
_DESCRIPTION = """\
Cryptonite: A Cryptic Crossword Benchmark for Extreme Ambiguity in Language
Current NLP datasets targeting ambiguity can be solved by a native speaker with relative ease. We present Cryptonite,
a large-scale dataset based on cryptic crosswords, which is both linguistically complex and naturally sourced. Each
example in Cryptonite is a cryptic clue, a short phrase or sentence with a misleading surface reading, whose solving
requires disambiguating semantic, syntactic, and phonetic wordplays, as well as world knowledge. Cryptic clues pose a
challenge even for experienced solvers, though top-tier experts can solve them with almost 100% accuracy. Cryptonite
is a challenging task for current models; fine-tuning T5-Large on 470k cryptic clues achieves only 7.6% accuracy, on
par with the accuracy of a rule-based clue solver (8.6%).
"""
_HOMEPAGE = "https://github.com/aviaefrat/cryptonite"
_LICENSE = "cc-by-nc-4.0"
_URL = "https://github.com/aviaefrat/cryptonite/blob/main/data/cryptonite-official-split.zip?raw=true"
class Cryptonite(datasets.GeneratorBasedBuilder):
VERSION = datasets.Version("1.1.0")
BUILDER_CONFIGS = [
datasets.BuilderConfig(name="cryptonite", version=VERSION),
]
def _info(self):
return datasets.DatasetInfo(
# This is the description that will appear on the datasets page.
description=_DESCRIPTION,
# This defines the different columns of the dataset and their types
features=datasets.Features(
{
"clue": datasets.Value("string"),
"answer": datasets.Value("string"),
"enumeration": datasets.Value("string"),
"publisher": datasets.Value("string"),
"date": datasets.Value("int64"),
"quick": datasets.Value("bool"),
"id": datasets.Value("string"),
}
),
supervised_keys=None,
homepage=_HOMEPAGE,
license=_LICENSE,
citation=_CITATION,
)
def _split_generators(self, dl_manager):
"""Returns SplitGenerators."""
data_dir = dl_manager.download_and_extract(_URL)
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={
"filepath": os.path.join(data_dir, "cryptonite-official-split/cryptonite-train.jsonl"),
"split": "train",
},
),
datasets.SplitGenerator(
name=datasets.Split.VALIDATION,
gen_kwargs={
"filepath": os.path.join(data_dir, "cryptonite-official-split/cryptonite-val.jsonl"),
"split": "val",
},
),
datasets.SplitGenerator(
name=datasets.Split.TEST,
gen_kwargs={
"filepath": os.path.join(data_dir, "cryptonite-official-split/cryptonite-test.jsonl"),
"split": "test",
},
),
]
def _generate_examples(self, filepath, split):
"""Yields examples."""
with open(filepath, encoding="utf-8") as f:
for id_, row in enumerate(f):
data = json.loads(row)
publisher = data["publisher"]
crossword_id = data["crossword_id"]
number = data["number"]
orientation = data["orientation"]
d_id = f"{publisher}-{crossword_id}-{number}{orientation}"
yield id_, {
"clue": data["clue"],
"answer": data["answer"],
"enumeration": data["enumeration"],
"publisher": publisher,
"date": data["date"],
"quick": data["quick"],
"id": d_id,
}
|