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import gzip |
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import json |
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import datasets |
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logger = datasets.logging.get_logger(__name__) |
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_HOMEPAGE = "https://github.com/allenai/peS2o" |
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_DESCRIPTION = "\ |
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The peS2o dataset is a collection of ~40M creative commmon licensed academic \ |
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papers, cleaned, filtered, and formatted for pre-training of language models. \ |
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It is derived from the Semantic Scholar Open Research Corpus(Lo et al, 2020), \ |
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or S2ORC.\ |
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" |
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_LICENSE = "odc-by" |
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_VARIANTS = { |
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"v1": { |
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"version": "1.0.0", |
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"download_size": 100702002904, |
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"dataset_size": 67787014, |
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"splits": { |
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"train": { |
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"num_bytes": 100145555091, |
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"num_examples": 67624463, |
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"files": [ |
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"data/v1/train-00000-of-00020.json.gz", |
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"data/v1/train-00001-of-00020.json.gz", |
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"data/v1/train-00002-of-00020.json.gz", |
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"data/v1/train-00003-of-00020.json.gz", |
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"data/v1/train-00004-of-00020.json.gz", |
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"data/v1/train-00005-of-00020.json.gz", |
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"data/v1/train-00006-of-00020.json.gz", |
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"data/v1/train-00007-of-00020.json.gz", |
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"data/v1/train-00008-of-00020.json.gz", |
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"data/v1/train-00009-of-00020.json.gz", |
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"data/v1/train-00010-of-00020.json.gz", |
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"data/v1/train-00011-of-00020.json.gz", |
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"data/v1/train-00012-of-00020.json.gz", |
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"data/v1/train-00013-of-00020.json.gz", |
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"data/v1/train-00014-of-00020.json.gz", |
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"data/v1/train-00015-of-00020.json.gz", |
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"data/v1/train-00016-of-00020.json.gz", |
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"data/v1/train-00017-of-00020.json.gz", |
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"data/v1/train-00018-of-00020.json.gz", |
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"data/v1/train-00019-of-00020.json.gz", |
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], |
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}, |
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"validation": { |
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"num_bytes": 556447813, |
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"num_examples": 162551, |
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"files": [ |
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"data/v1/validation-00000-of-00002.json.gz", |
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"data/v1/validation-00001-of-00002.json.gz", |
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], |
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}, |
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}, |
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}, |
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"v2": { |
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"version": "1.0.0", |
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"download_size": 87129236480, |
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"dataset_size": 38972211, |
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"splits": { |
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"train": { |
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"num_bytes": 86572382178, |
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"num_examples": 38811179, |
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"files": [ |
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"data/v2/train-00000-of-00020.json.gz", |
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"data/v2/train-00001-of-00020.json.gz", |
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"data/v2/train-00002-of-00020.json.gz", |
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"data/v2/train-00003-of-00020.json.gz", |
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"data/v2/train-00004-of-00020.json.gz", |
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"data/v2/train-00005-of-00020.json.gz", |
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"data/v2/train-00006-of-00020.json.gz", |
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"data/v2/train-00007-of-00020.json.gz", |
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"data/v2/train-00008-of-00020.json.gz", |
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"data/v2/train-00009-of-00020.json.gz", |
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"data/v2/train-00010-of-00020.json.gz", |
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"data/v2/train-00011-of-00020.json.gz", |
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"data/v2/train-00012-of-00020.json.gz", |
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"data/v2/train-00013-of-00020.json.gz", |
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"data/v2/train-00014-of-00020.json.gz", |
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"data/v2/train-00015-of-00020.json.gz", |
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"data/v2/train-00016-of-00020.json.gz", |
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"data/v2/train-00017-of-00020.json.gz", |
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"data/v2/train-00018-of-00020.json.gz", |
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"data/v2/train-00019-of-00020.json.gz", |
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], |
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}, |
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"validation": { |
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"num_bytes": 556854302, |
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"num_examples": 161032, |
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"files": [ |
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"data/v2/validation-00000-of-00002.json.gz", |
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"data/v2/validation-00001-of-00002.json.gz", |
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], |
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}, |
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}, |
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}, |
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} |
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_FEATURES = datasets.Features( |
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added=datasets.Value("string"), |
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created=datasets.Value("string"), |
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id=datasets.Value("string"), |
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source=datasets.Value("string"), |
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text=datasets.Value("string"), |
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version=datasets.Value("string"), |
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) |
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_CITATION = """\ |
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@techreport{peS2o, |
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author = {Luca Soldaini and Kyle Lo}, |
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year = 2023, |
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title = {{peS2o (Pretraining Efficiently on S2ORC) Dataset}}, |
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institution = {{Allen Institute for AI}}, |
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note = {ODC-By, \\url{https://github.com/allenai/pes2o}} |
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} |
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""" |
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class PeS2o(datasets.GeneratorBasedBuilder): |
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"""Pretraining Efficiently on S2ORC!""" |
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BUILDER_CONFIGS = [ |
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datasets.BuilderConfig(name=name, version=config["version"]) |
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for name, config in _VARIANTS.items() |
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] |
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DEFAULT_CONFIG_NAME = "v2" |
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def _info(self): |
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"""Give information and typings for the dataset.""" |
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return datasets.DatasetInfo( |
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description=_DESCRIPTION, |
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features=_FEATURES, |
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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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dataset_size=_VARIANTS[self.config.name]["dataset_size"], |
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download_size=_VARIANTS[self.config.name]["download_size"], |
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) |
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def _split_generators(self, dl_manager): |
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train_downloaded_files = dl_manager.download( |
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_VARIANTS[self.config.name]["splits"]["train"]["files"] |
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) |
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validation_downloaded_files = dl_manager.download( |
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_VARIANTS[self.config.name]["splits"]["validation"]["files"] |
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) |
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return [ |
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datasets.SplitGenerator( |
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name=str(datasets.Split.TRAIN), |
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gen_kwargs={"filepaths": train_downloaded_files}, |
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), |
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datasets.SplitGenerator( |
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name=str(datasets.Split.VALIDATION), |
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gen_kwargs={"filepaths": validation_downloaded_files}, |
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), |
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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 |
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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("generating examples from = %s", 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_, example |
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id_ += 1 |
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