cryptonite / cryptonite.py
# 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,
}