Datasets:
cryptonite

	# 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
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	# 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,
	}