Datasets:
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Sub-tasks:
slot-filling
Languages:
English
Size:
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License:
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Parent(s):
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Browse files- youtube_caption_corrections.py +0 -104
youtube_caption_corrections.py
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# coding=utf-8
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# Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""Dataset built from <auto-generated, manually corrected> caption pairs of
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YouTube videos with labels capturing the differences between the two."""
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import json
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import datasets
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_CITATION = ""
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_DESCRIPTION = """\
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Dataset built from pairs of YouTube captions where both 'auto-generated' and
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'manually-corrected' captions are available for a single specified language.
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This dataset labels two-way (e.g. ignoring single-sided insertions) same-length
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token differences in the `diff_type` column. The `default_seq` is composed of
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tokens from the 'auto-generated' captions. When a difference occurs between
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the 'auto-generated' vs 'manually-corrected' captions types, the `correction_seq`
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contains tokens from the 'manually-corrected' captions.
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"""
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_LICENSE = "MIT License"
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_RELEASE_TAG = "v1.0"
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_NUM_FILES = 4
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_URLS = [
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f"https://raw.githubusercontent.com/2dot71mily/youtube_captions_corrections/{_RELEASE_TAG}/data/transcripts/en/split/youtube_caption_corrections_{i}.json"
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for i in range(_NUM_FILES)
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]
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class YoutubeCaptionCorrections(datasets.GeneratorBasedBuilder):
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"""YouTube captions corrections."""
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def _info(self):
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return datasets.DatasetInfo(
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description=_DESCRIPTION,
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features=datasets.Features(
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{
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"video_ids": datasets.Value("string"),
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"default_seq": datasets.Sequence(datasets.Value("string")),
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"correction_seq": datasets.Sequence(datasets.Value("string")),
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"diff_type": datasets.Sequence(
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datasets.features.ClassLabel(
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names=[
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"NO_DIFF",
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"CASE_DIFF",
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"PUNCUATION_DIFF",
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"CASE_AND_PUNCUATION_DIFF",
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"STEM_BASED_DIFF",
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"DIGIT_DIFF",
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"INTRAWORD_PUNC_DIFF",
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"UNKNOWN_TYPE_DIFF",
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"RESERVED_DIFF",
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]
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)
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),
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}
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),
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supervised_keys=("correction_seq", "diff_type"),
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homepage="https://github.com/2dot71mily/youtube_captions_corrections",
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license=_LICENSE,
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)
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def _split_generators(self, dl_manager):
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"""Returns SplitGenerators."""
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downloaded_filepaths = dl_manager.download_and_extract(_URLS)
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return [
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datasets.SplitGenerator(
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name=datasets.Split.TRAIN,
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gen_kwargs={"filepaths": downloaded_filepaths},
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),
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]
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def _generate_examples(self, filepaths):
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"""Yields examples."""
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for file_idx, fp in enumerate(filepaths):
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with open(fp, "r", encoding="utf-8") as json_file:
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json_lists = list(json_file)
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for line_idx, json_list_str in enumerate(json_lists):
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json_list = json.loads(json_list_str)
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for ctr_idx, result in enumerate(json_list):
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response = {
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"video_ids": result["video_ids"],
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"diff_type": result["diff_type"],
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"default_seq": result["default_seq"],
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"correction_seq": result["correction_seq"],
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}
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yield f"{file_idx}_{line_idx}_{ctr_idx}", response
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