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add 2021_39 dedup

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  1. 2021_39/dedup/2021_39_dedup.py +107 -0
  2. 2021_39/dedup/oscar-000000000001.json.gz +3 -0
  3. 2021_39/dedup/oscar-000000000002.json.gz +3 -0
  4. 2021_39/dedup/oscar-000000000003.json.gz +3 -0
  5. 2021_39/dedup/oscar-000000000004.json.gz +3 -0
  6. 2021_39/dedup/oscar-000000000005.json.gz +3 -0
  7. 2021_39/dedup/oscar-000000000006.json.gz +3 -0
  8. 2021_39/dedup/oscar-000000000007.json.gz +3 -0
  9. 2021_39/dedup/oscar-000000000008.json.gz +3 -0
  10. 2021_39/dedup/oscar-000000000009.json.gz +3 -0
  11. 2021_39/dedup/oscar-000000000010.json.gz +3 -0
  12. 2021_39/dedup/oscar-000000000011.json.gz +3 -0
  13. 2021_39/dedup/oscar-000000000012.json.gz +3 -0
  14. 2021_39/dedup/oscar-000000000013.json.gz +3 -0
  15. 2021_39/dedup/oscar-000000000014.json.gz +3 -0
  16. 2021_39/dedup/oscar-000000000015.json.gz +3 -0
  17. 2021_39/dedup/oscar-000000000016.json.gz +3 -0
  18. 2021_39/dedup/oscar-000000000017.json.gz +3 -0
  19. 2021_39/dedup/oscar-000000000018.json.gz +3 -0
  20. 2021_39/dedup/oscar-000000000019.json.gz +3 -0
  21. 2021_39/dedup/oscar-000000000020.json.gz +3 -0
  22. 2021_39/dedup/oscar-000000000021.json.gz +3 -0
  23. 2021_39/dedup/oscar-000000000022.json.gz +3 -0
  24. 2021_39/dedup/oscar-000000000023.json.gz +3 -0
  25. 2021_39/dedup/oscar-000000000024.json.gz +3 -0
  26. 2021_39/dedup/oscar-000000000025.json.gz +3 -0
  27. 2021_39/dedup/oscar-000000000026.json.gz +3 -0
  28. 2021_39/dedup/oscar-000000000027.json.gz +3 -0
  29. 2021_39/dedup/oscar-000000000028.json.gz +3 -0
  30. 2021_39/dedup/oscar-000000000029.json.gz +3 -0
  31. 2021_39/dedup/oscar-000000000030.json.gz +3 -0
  32. 2021_39/dedup/oscar-000000000031.json.gz +3 -0
  33. 2021_39/dedup/oscar-000000000032.json.gz +3 -0
  34. 2021_39/dedup/oscar-000000000033.json.gz +3 -0
  35. 2021_39/dedup/oscar-000000000034.json.gz +3 -0
  36. 2021_39/dedup/oscar-000000000035.json.gz +3 -0
  37. 2021_39/dedup/oscar-000000000036.json.gz +3 -0
  38. 2021_39/dedup/oscar-000000000037.json.gz +3 -0
  39. 2021_39/dedup/oscar-000000000038.json.gz +3 -0
  40. 2021_39/dedup/oscar-000000000039.json.gz +3 -0
  41. 2021_39/dedup/oscar-000000000040.json.gz +3 -0
  42. 2021_39/dedup/oscar-000000000041.json.gz +3 -0
  43. 2021_39/dedup/oscar-000000000042.json.gz +3 -0
  44. 2021_39/dedup/oscar-000000000043.json.gz +3 -0
  45. 2021_39/dedup/oscar-000000000044.json.gz +3 -0
2021_39/dedup/2021_39_dedup.py ADDED
@@ -0,0 +1,107 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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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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+ """Cleaned Indonesian split of the mC4 corpus."""
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+ import json
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+ import glob
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+ import gzip
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+ import textwrap
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+ import datasets
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+ logger = datasets.logging.get_logger(__name__)
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+
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+ file = sorted(glob.glob('/data/KoPI-CC/2021_39/dedup/*.gz'))
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+ _CITATION = """
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+ @article{JMLR:v21:20-074,
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+ author = {Colin Raffel and Noam Shazeer and Adam Roberts and Katherine Lee and Sharan Narang and Michael Matena and Yanqi Zhou and Wei Li and Peter J. Liu},
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+ title = {Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer},
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+ journal = {Journal of Machine Learning Research},
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+ year = {2020},
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+ volume = {21},
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+ number = {140},
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+ pages = {1-67},
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+ url = {http://jmlr.org/papers/v21/20-074.html}
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+ }
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+ """
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+ _DESCRIPTION = """\
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+ A thoroughly cleaned version of the Italian portion of the multilingual
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+ colossal, cleaned version of Common Crawl's web crawl corpus (mC4) by AllenAI.
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+ Based on Common Crawl dataset: "https://commoncrawl.org".
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+ This is the processed version of Google's mC4 dataset by AllenAI, with further cleaning
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+ detailed in the repository README file.
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+ """
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+ _HOMEPAGE = "https://github.com/allenai/allennlp/discussions/5056"
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+ _LICENSE = "Open Data Commons Attribution License (ODC-By) v1.0"
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+ _BASE_URL = "https://huggingface.co/datasets/munggok/mc4-id/resolve/main/mc4-id-filter/c4-id{split_suffix}.tfrecord-{index:05d}-of-{n_shards:05d}.json.gz"
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+ _CONFIGS = {
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+ "tiny": {"train": 100, "validation": 1},
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+ "small": {"train": 250, "validation": 2},
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+ "medium": {"train": 500, "validation": 4},
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+ "large": {"train": 750, "validation": 6},
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+ "full": {"train": 1016, "validation": 8}
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+ }
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+ class OscarConfig(datasets.BuilderConfig):
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+ """BuilderConfig for the Clean mC4 Italian."""
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+ def __init__(self, **kwargs):
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+ """BuilderConfig for Clean mC4 Italian.
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+ Args:
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+ **kwargs: keyword arguments forwarded to super.
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+ """
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+ super().__init__(**kwargs)
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+ class Oscar(datasets.GeneratorBasedBuilder):
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+ """mC4, a colossal, cleaned version of Common Crawl's web crawl corpus."""
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+ BUILDER_CONFIGS = [
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+ OscarConfig(
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+ name="full",
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+ version=datasets.Version("1.0.0"),
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+ description=textwrap.dedent(
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+ f"""\
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+ The full cleaned version of the Italian portion of the multilingual C4 corpus.
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+ Estimated size of compressed files: 103GB
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+ """
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+ )
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+ )
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+ ]
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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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+ "text": datasets.Value("string"),
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+ "url": datasets.Value("string"),
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+ "timestamp": datasets.Value("string"),
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+ "meta": datasets.Value("string"),
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+ }
85
+ ),
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+ supervised_keys=None,
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+ homepage=_HOMEPAGE,
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+ license=_LICENSE,
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+ citation=_CITATION,
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+ )
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+ def _split_generators(self, dl_manager):
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+ data_urls = {}
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+ train_downloaded_files = file
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+ return [
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+ datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepaths": train_downloaded_files}),
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+ ]
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+ def _generate_examples(self, filepaths):
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+ """This function returns the examples in the raw (text) form by iterating on all the files."""
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+ id_ = 0
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+ for filepath in filepaths:
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+ logger.info(f"Generating examples from {filepath}")
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+ with gzip.open(open(filepath, "rb"), "rt", encoding="utf-8") as f:
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+ for line in f:
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+ if line:
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+ example = json.loads(line)
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+ yield id_, {'text':example['text'],'url':example['url'],'timestamp':example['timestamp'],'meta': example['meta']}
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+ id_ += 1
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