Datasets:
irc_disentangle

	# 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.
	"""Dataset of disentangled IRC"""

	from __future__ import absolute_import, division, print_function

	import glob
	import os
	from pathlib import Path

	import datasets


	_CITATION = """\
	@InProceedings{acl19disentangle,
	author = {Jonathan K. Kummerfeld and Sai R. Gouravajhala and Joseph Peper and Vignesh Athreya and Chulaka Gunasekara and Jatin Ganhotra and Siva Sankalp Patel and Lazaros Polymenakos and Walter S. Lasecki},
	title = {A Large-Scale Corpus for Conversation Disentanglement},
	booktitle = {Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics},
	location = {Florence, Italy},
	month = {July},
	year = {2019},
	doi = {10.18653/v1/P19-1374},
	pages = {3846--3856},
	url = {https://aclweb.org/anthology/papers/P/P19/P19-1374/},
	arxiv = {https://arxiv.org/abs/1810.11118},
	software = {https://jkk.name/irc-disentanglement},
	data = {https://jkk.name/irc-disentanglement},
	}
	"""

	_DESCRIPTION = """\
	Disentangling conversations mixed together in a single stream of messages is
	a difficult task, made harder by the lack of large manually annotated
	datasets. This new dataset of 77,563 messages manually annotated with
	reply-structure graphs that both disentangle conversations and define
	internal conversation structure. The dataset is 16 times larger than all
	previously released datasets combined, the first to include adjudication of
	annotation disagreements, and the first to include context.
	"""

	_HOMEPAGE = "https://jkk.name/irc-disentanglement/"

	_LICENSE = "Creative Commons Attribution 4.0 International Public License"

	_URL = "https://github.com/jkkummerfeld/irc-disentanglement/tarball/master"


	class IRCDisentangle(datasets.GeneratorBasedBuilder):
	"""IRCDisentangle dataset"""

	VERSION = datasets.Version("1.0.0")

	BUILDER_CONFIGS = [
	datasets.BuilderConfig(
	name="ubuntu",
	version=VERSION,
	description="This part of the dataset is the annotated conversations from the Ubuntu channel",
	),
	datasets.BuilderConfig(
	name="channel_two",
	version=VERSION,
	description="This part of the dataset is the annotated conversations from the Channel Two",
	),
	]

	DEFAULT_CONFIG_NAME = "ubuntu"

	def _info(self):
	if self.config.name == "ubuntu":
	features = datasets.Features(
	{
	"id": datasets.Value("int32"),
	"raw": datasets.Value("string"),
	"ascii": datasets.Value("string"),
	"tokenized": datasets.Value("string"),
	"date": datasets.Value("string"),
	"connections": datasets.features.Sequence(datasets.Value("int32")),
	}
	)
	elif self.config.name == "channel_two":
	features = datasets.Features(
	{
	"id": datasets.Value("int32"),
	"raw": datasets.Value("string"),
	"ascii": datasets.Value("string"),
	"tokenized": datasets.Value("string"),
	"connections": datasets.features.Sequence(datasets.Value("int32")),
	}
	)
	return datasets.DatasetInfo(
	description=_DESCRIPTION,
	features=features,
	supervised_keys=None,
	homepage=_HOMEPAGE,
	license=_LICENSE,
	citation=_CITATION,
	)

	def _split_generators(self, dl_manager):
	"""Returns SplitGenerators."""
	my_urls = _URL
	dl_dir = dl_manager.download_and_extract(my_urls)

	files = dict()
	if self.config.name == "ubuntu":
	for split in ["train", "dev", "test"]:
	files[split] = os.path.join(dl_dir, "jkkummerfeld-irc-disentanglement-fd379e9", "data", split)

	return [
	datasets.SplitGenerator(
	name=datasets.Split.TRAIN,
	gen_kwargs={
	"filepath": files["train"],
	"split": "train",
	},
	),
	datasets.SplitGenerator(
	name=datasets.Split.TEST,
	gen_kwargs={
	"filepath": files["test"],
	"split": "test",
	},
	),
	datasets.SplitGenerator(
	name=datasets.Split.VALIDATION,
	gen_kwargs={
	"filepath": files["dev"],
	"split": "dev",
	},
	),
	]

	elif self.config.name == "channel_two":
	filepath = os.path.join(dl_dir, "jkkummerfeld-irc-disentanglement-fd379e9", "data", "channel-two")
	return [
	datasets.SplitGenerator(
	name="dev",
	gen_kwargs={
	"filepath": filepath,
	"split": "dev",
	},
	),
	datasets.SplitGenerator(
	name="pilot",
	gen_kwargs={
	"filepath": filepath,
	"split": "pilot",
	},
	),
	datasets.SplitGenerator(
	name="test",
	gen_kwargs={
	"filepath": filepath,
	"split": "test",
	},
	),
	datasets.SplitGenerator(
	name="pilot_dev",
	gen_kwargs={
	"filepath": filepath,
	"split": "pilot-dev",
	},
	),
	datasets.SplitGenerator(
	name="all_",
	gen_kwargs={
	"filepath": filepath,
	"split": "all",
	},
	),
	]

	def _generate_examples(self, filepath, split):
	""" Yields examples. """

	if self.config.name == "ubuntu":
	# run loop for each date
	all_files = sorted(glob.glob(os.path.join(filepath, "*.annotation.txt")))
	all_dates = [Path(file).name[:10] for file in all_files]
	all_info = [Path(file).name[10:-15] for file in all_files]

	elif self.config.name == "channel_two":
	# run loop once (there are no dates for this config)
	all_dates = ["_"]
	all_info = ["_"]

	last_id = 0
	id_ = 0

	for date, info in zip(all_dates, all_info):

	if self.config.name == "ubuntu":
	# load file of given date and additional info for each split
	raw_path = os.path.join(filepath, f"{date}{info}.raw.txt")
	ascii_path = os.path.join(filepath, f"{date}{info}.ascii.txt")
	tok_path = os.path.join(filepath, f"{date}{info}.tok.txt")
	annot_path = os.path.join(filepath, f"{date}{info}.annotation.txt")

	elif self.config.name == "channel_two":
	# load files of different splits
	raw_path = os.path.join(filepath, f"channel-two.{split}.raw.txt")
	ascii_path = os.path.join(filepath, f"channel-two.{split}.ascii.txt")
	tok_path = os.path.join(filepath, f"channel-two.{split}.tok.txt")
	annot_path = os.path.join(filepath, f"channel-two.{split}.annotation.txt")

	with open(raw_path, encoding="utf-8") as f_raw, open(ascii_path, encoding="utf-8") as f_ascii, open(
	tok_path, encoding="utf-8"
	) as f_tok, open(annot_path, encoding="utf-8") as f_annot:

	# tokenize txt file
	raw_sentences = f_raw.read().split("\n")
	ascii_sentences = f_ascii.read().split("\n")
	tok_sentences = f_tok.read().split("\n")
	annot_lines = f_annot.read().split("\n")

	assert (
	len(raw_sentences) == len(ascii_sentences) == len(tok_sentences)
	), "Sizes do not match: %d vs %d vs %d for Raw Sentences vs Ascii Sentences vs Tokenized Sentences." % (
	len(raw_sentences),
	len(ascii_sentences),
	len(tok_sentences),
	)

	annotation_pairs = []

	# for annotation lines, make annotation pairs
	for annot in annot_lines:
	line = annot.split(" ")
	if len(line) > 1:
	annotation_pairs.append((int(line[0]), int(line[1])))

	annotations = dict()
	for row in range(last_id, last_id + len(raw_sentences)):
	annotations[row] = set()

	for (a, b) in annotation_pairs:
	# required for dummy data creation
	if last_id + a not in annotations:
	annotations[last_id + a] = set()
	if last_id + b not in annotations:
	annotations[last_id + b] = set()

	# add annotation 'b' to a's annotation set, and vice versa
	annotations[last_id + a].add(last_id + b)
	annotations[last_id + b].add(last_id + a)

	for i in range(len(raw_sentences)):
	# return all 3 kinds of chat messages, the date (if applicable), and the annotation set for that sentece
	if self.config.name == "ubuntu":
	yield id_, {
	"id": id_,
	"raw": raw_sentences[i],
	"ascii": ascii_sentences[i],
	"tokenized": tok_sentences[i],
	"date": date,
	"connections": sorted(annotations[id_]),
	}
	elif self.config.name == "channel_two":
	yield id_, {
	"id": id_,
	"raw": raw_sentences[i],
	"ascii": ascii_sentences[i],
	"tokenized": tok_sentences[i],
	"connections": sorted(annotations[i]),
	}
	id_ += 1

	# continue counting from position last left off
	last_id = id_