Datasets:
Sub-tasks:
slot-filling
Languages:
English
Multilinguality:
monolingual
Size Categories:
10K<n<100K
Language Creators:
machine-generated
Source Datasets:
original
License:
File size: 4,174 Bytes
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# 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
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