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0000000000000000000000000000000000000000..d4df2419c86a3ab770dc6fe95b04c740a6374bc8 --- /dev/null +++ b/2022_27/dedup/oscar-000000000038.json.gz @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:8879785440663a00687db81023d551d006c3a83dd1449623e944760bd6134ae4 +size 12284845 diff --git a/2022_27/dedup/oscar_2202_27_dedup.py b/2022_27/dedup/oscar_2202_27_dedup.py new file mode 100644 index 0000000000000000000000000000000000000000..503f71365eeae89ed2f3d15db182dd6ed33d2b2e --- /dev/null +++ b/2022_27/dedup/oscar_2202_27_dedup.py @@ -0,0 +1,107 @@ +# 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. +"""Cleaned Indonesian split of the mC4 corpus.""" +import json +import glob +import gzip +import textwrap +import datasets +logger = datasets.logging.get_logger(__name__) + +file = sorted(glob.glob('/data/june_2202_27_dedup/data/*')) +_CITATION = """ +@article{JMLR:v21:20-074, + 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}, + title = {Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer}, + journal = {Journal of Machine Learning Research}, + year = {2020}, + volume = {21}, + number = {140}, + pages = {1-67}, + url = {http://jmlr.org/papers/v21/20-074.html} +} +""" +_DESCRIPTION = """\ +A thoroughly cleaned version of the Italian portion of the multilingual +colossal, cleaned version of Common Crawl's web crawl corpus (mC4) by AllenAI. +Based on Common Crawl dataset: "https://commoncrawl.org". +This is the processed version of Google's mC4 dataset by AllenAI, with further cleaning +detailed in the repository README file. +""" +_HOMEPAGE = "https://github.com/allenai/allennlp/discussions/5056" +_LICENSE = "Open Data Commons Attribution License (ODC-By) v1.0" +_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" +_CONFIGS = { + "tiny": {"train": 100, "validation": 1}, + "small": {"train": 250, "validation": 2}, + "medium": {"train": 500, "validation": 4}, + "large": {"train": 750, "validation": 6}, + "full": {"train": 1016, "validation": 8} +} +class OscarConfig(datasets.BuilderConfig): + """BuilderConfig for the Clean mC4 Italian.""" + def __init__(self, **kwargs): + """BuilderConfig for Clean mC4 Italian. + Args: + **kwargs: keyword arguments forwarded to super. + """ + super().__init__(**kwargs) +class Oscar(datasets.GeneratorBasedBuilder): + """mC4, a colossal, cleaned version of Common Crawl's web crawl corpus.""" + BUILDER_CONFIGS = [ + OscarConfig( + name="full", + version=datasets.Version("1.0.0"), + description=textwrap.dedent( + f"""\ + The full cleaned version of the Italian portion of the multilingual C4 corpus. + Estimated size of compressed files: 103GB + """ + ) + ) + ] + def _info(self): + return datasets.DatasetInfo( + description=_DESCRIPTION, + features=datasets.Features( + { + "text": datasets.Value("string"), + "url": datasets.Value("string"), + "timestamp": datasets.Value("string"), + "meta": datasets.Value("string"), + } + ), + supervised_keys=None, + homepage=_HOMEPAGE, + license=_LICENSE, + citation=_CITATION, + ) + def _split_generators(self, dl_manager): + data_urls = {} + train_downloaded_files = file + return [ + datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepaths": train_downloaded_files}), + ] + def _generate_examples(self, filepaths): + """This function returns the examples in the raw (text) form by iterating on all the files.""" + id_ = 0 + for filepath in filepaths: + logger.info(f"Generating examples from {filepath}") + with gzip.open(open(filepath, "rb"), "rt", encoding="utf-8") as f: + for line in f: + if line: + example = json.loads(line) + yield id_, {'text':example['text'],'url':example['url'],'timestamp':example['timestamp'],'meta': example['meta']} + id_ += 1 diff --git a/2022_27/neardup/oscar-neardup-000000000001.json.gz 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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. +"""Cleaned Indonesian split of the mC4 corpus.""" +import json +import glob +import gzip +import textwrap +import datasets +logger = datasets.logging.get_logger(__name__) + +file = sorted(glob.glob('/data/oscar_id/2022_27/neardup_clean/*.gz')) +_CITATION = """ +@article{JMLR:v21:20-074, + 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}, + title = {Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer}, + journal = {Journal of Machine Learning Research}, + year = {2020}, + volume = {21}, + number = {140}, + pages = {1-67}, + url = {http://jmlr.org/papers/v21/20-074.html} +} +""" +_DESCRIPTION = """\ +A thoroughly cleaned version of the Italian portion of the multilingual +colossal, cleaned version of Common Crawl's web crawl corpus (mC4) by AllenAI. +Based on Common Crawl dataset: "https://commoncrawl.org". +This is the processed version of Google's mC4 dataset by AllenAI, with further cleaning +detailed in the repository README file. +""" +_HOMEPAGE = "https://github.com/allenai/allennlp/discussions/5056" +_LICENSE = "Open Data Commons Attribution License (ODC-By) v1.0" +_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" +_CONFIGS = { + "tiny": {"train": 100, "validation": 1}, + "small": {"train": 250, "validation": 2}, + "medium": {"train": 500, "validation": 4}, + "large": {"train": 750, "validation": 6}, + "full": {"train": 1016, "validation": 8} +} +class OscarConfig(datasets.BuilderConfig): + """BuilderConfig for the Clean mC4 Italian.""" + def __init__(self, **kwargs): + """BuilderConfig for Clean mC4 Italian. + Args: + **kwargs: keyword arguments forwarded to super. + """ + super().__init__(**kwargs) +class Oscar(datasets.GeneratorBasedBuilder): + """mC4, a colossal, cleaned version of Common Crawl's web crawl corpus.""" + BUILDER_CONFIGS = [ + OscarConfig( + name="full", + version=datasets.Version("1.0.0"), + description=textwrap.dedent( + f"""\ + The full cleaned version of the Italian portion of the multilingual C4 corpus. + Estimated size of compressed files: 103GB + """ + ) + ) + ] + def _info(self): + return datasets.DatasetInfo( + description=_DESCRIPTION, + features=datasets.Features( + { + "text": datasets.Value("string"), + "url": datasets.Value("string"), + "timestamp": datasets.Value("string"), + "meta": datasets.Value("string"), + } + ), + supervised_keys=None, + homepage=_HOMEPAGE, + license=_LICENSE, + citation=_CITATION, + ) + def _split_generators(self, dl_manager): + data_urls = {} + train_downloaded_files = file + return [ + datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepaths": train_downloaded_files}), + ] + def _generate_examples(self, filepaths): + """This function returns the examples in the raw (text) form by iterating on all the files.""" + id_ = 0 + for filepath in filepaths: + logger.info(f"Generating examples from {filepath}") + with gzip.open(open(filepath, "rb"), "rt", encoding="utf-8") as f: + for line in f: + if line: + example = json.loads(line) + yield id_, {'text':example['text'],'url':example['url'],'timestamp':example['timestamp'],'meta': example['meta']} + id_ += 1