Datasets:
youtube_caption_corrections

	# 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 built from <auto-generated, manually corrected> caption pairs of
	YouTube videos with labels capturing the differences between the two."""


	import json

	import datasets


	_CITATION = ""

	_DESCRIPTION = """\
	Dataset built from pairs of YouTube captions where both 'auto-generated' and
	'manually-corrected' captions are available for a single specified language.
	This dataset labels two-way (e.g. ignoring single-sided insertions) same-length
	token differences in the `diff_type` column. The `default_seq` is composed of
	tokens from the 'auto-generated' captions. When a difference occurs between
	the 'auto-generated' vs 'manually-corrected' captions types, the `correction_seq`
	contains tokens from the 'manually-corrected' captions.
	"""

	_LICENSE = "MIT License"

	_RELEASE_TAG = "v1.0"
	_NUM_FILES = 4
	_URLS = [
	f"https://raw.githubusercontent.com/2dot71mily/youtube_captions_corrections/{_RELEASE_TAG}/data/transcripts/en/split/youtube_caption_corrections_{i}.json"
	for i in range(_NUM_FILES)
	]


	class YoutubeCaptionCorrections(datasets.GeneratorBasedBuilder):
	"""YouTube captions corrections."""

	def _info(self):
	return datasets.DatasetInfo(
	description=_DESCRIPTION,
	features=datasets.Features(
	{
	"video_ids": datasets.Value("string"),
	"default_seq": datasets.Sequence(datasets.Value("string")),
	"correction_seq": datasets.Sequence(datasets.Value("string")),
	"diff_type": datasets.Sequence(
	datasets.features.ClassLabel(
	names=[
	"NO_DIFF",
	"CASE_DIFF",
	"PUNCUATION_DIFF",
	"CASE_AND_PUNCUATION_DIFF",
	"STEM_BASED_DIFF",
	"DIGIT_DIFF",
	"INTRAWORD_PUNC_DIFF",
	"UNKNOWN_TYPE_DIFF",
	"RESERVED_DIFF",
	]
	)
	),
	}
	),
	supervised_keys=("correction_seq", "diff_type"),
	homepage="https://github.com/2dot71mily/youtube_captions_corrections",
	license=_LICENSE,
	)

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

	def _generate_examples(self, filepaths):
	"""Yields examples."""
	for file_idx, fp in enumerate(filepaths):
	with open(fp, "r", encoding="utf-8") as json_file:
	json_lists = list(json_file)
	for line_idx, json_list_str in enumerate(json_lists):
	json_list = json.loads(json_list_str)

	for ctr_idx, result in enumerate(json_list):
	response = {
	"video_ids": result["video_ids"],
	"diff_type": result["diff_type"],
	"default_seq": result["default_seq"],
	"correction_seq": result["correction_seq"],
	}
	yield f"{file_idx}_{line_idx}_{ctr_idx}", response