Convert dataset to Parquet (#3)

- Convert dataset to Parquet (78c90c57c34c341dd1cd1ddf3bb874c7904be2ed)
- Delete loading script (745bd03e6ac83d41bf4c7eb697f741581367da43)

README.md

@@ -46,10 +46,15 @@ dataset_info:
           '8': RESERVED_DIFF
   splits:
   - name: train
-    num_bytes: 355978939
+    num_bytes: 355978891
     num_examples: 10769
-  download_size: 222479455
-  dataset_size: 355978939
+  download_size: 49050406
+  dataset_size: 355978891
+configs:
+- config_name: default
+  data_files:
+  - split: train
+    path: data/train-*
 ---
 
 # Dataset Card for YouTube Caption Corrections

data/train-00000-of-00001.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:2325b0cebcbf84526cb2bdba9e4550b2fac6bc0dc832ef7b4ac08711b1ab6682
+size 49050406

youtube_caption_corrections.py

@@ -1,104 +0,0 @@
-# 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