Datasets:
reddit_tifu

	# coding=utf-8
	# Copyright 2020 The TensorFlow Datasets Authors and 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.

	# Lint as: python3
	"""Reddit TIFU dataset using tifu or tldr from subreddit tifu."""

	from __future__ import absolute_import, division, print_function

	import json

	import datasets


	_CITATION = """
	@misc{kim2018abstractive,
	title={Abstractive Summarization of Reddit Posts with Multi-level Memory Networks},
	author={Byeongchang Kim and Hyunwoo Kim and Gunhee Kim},
	year={2018},
	eprint={1811.00783},
	archivePrefix={arXiv},
	primaryClass={cs.CL}
	}
	"""

	_DESCRIPTION = """
	Reddit dataset, where TIFU denotes the name of subbreddit /r/tifu.
	As defined in the publication, styel "short" uses title as summary and
	"long" uses tldr as summary.

	Features includes:
	- document: post text without tldr.
	- tldr: tldr line.
	- title: trimmed title without tldr.
	- ups: upvotes.
	- score: score.
	- num_comments: number of comments.
	- upvote_ratio: upvote ratio.
	"""

	_URL = "https://drive.google.com/uc?export=download&id=1ffWfITKFMJeqjT8loC8aiCLRNJpc_XnF"

	_DOCUMENT = "documents"
	_TITLE = "title"
	_TLDR = "tldr"
	_ADDITIONAL_FEATURES = ["ups", "num_comments", "score", "upvote_ratio"]


	class RedditTifuConfig(datasets.BuilderConfig):
	"""BuilderConfig for RedditTifu."""

	def __init__(self, summary_key=None, **kwargs):
	"""BuilderConfig for RedditTifu.

	Args:
	summary_key: key string of summary in downloaded json file.
	**kwargs: keyword arguments forwarded to super.
	"""
	# Version 1.1.0 remove empty document and summary strings.
	super(RedditTifuConfig, self).__init__(version=datasets.Version("1.1.0"), **kwargs)
	self.summary_key = summary_key


	class RedditTifu(datasets.GeneratorBasedBuilder):
	"""Reddit TIFU Dataset."""

	BUILDER_CONFIGS = [
	RedditTifuConfig(
	name="short",
	summary_key=_TITLE,
	description="Using title as summary.",
	),
	RedditTifuConfig(
	name="long",
	summary_key=_TLDR,
	description="Using TLDR as summary.",
	),
	]

	def _info(self):
	features = {
	"ups": datasets.Value("float32"),
	"num_comments": datasets.Value("float32"),
	"upvote_ratio": datasets.Value("float32"),
	"score": datasets.Value("float32"),
	}
	features.update({k: datasets.Value("string") for k in [_DOCUMENT, _TLDR, _TITLE]})
	return datasets.DatasetInfo(
	description=_DESCRIPTION,
	features=datasets.Features(features),
	supervised_keys=(_DOCUMENT, self.config.summary_key),
	homepage="https://github.com/ctr4si/MMN",
	citation=_CITATION,
	)

	def _split_generators(self, dl_manager):
	"""Returns SplitGenerators."""
	dl_path = dl_manager.download_and_extract(_URL)
	return [
	datasets.SplitGenerator(
	name=datasets.Split.TRAIN,
	gen_kwargs={"path": dl_path},
	)
	]

	def _generate_examples(self, path=None):
	"""Yields examples."""
	with open(path, "rb") as f:
	for i, line in enumerate(f):
	# keys are 'title_tokenized','permalink','title','url','num_comments',
	# 'tldr'(optional),'created_utc','trimmed_title_tokenized','ups',
	# 'selftext_html','score','upvote_ratio','tldr_tokenized'(optional),
	# 'selftext','trimmed_title','selftext_without_tldr_tokenized',
	# 'id','selftext_without_tldr'
	d = json.loads(line)
	r = {
	_DOCUMENT: d["selftext_without_tldr"].strip(),
	_TITLE: d["trimmed_title"].strip(),
	_TLDR: (d["tldr"] or "").strip(),
	}
	r.update({k: d[k] for k in _ADDITIONAL_FEATURES})
	# skip if document or summary is empty
	if r[_DOCUMENT] and r[self.config.summary_key]:
	yield i, r