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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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_URL = "https://huggingface.co/datasets/allenai/pes2o" |
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_VARIANTS = ["v1", "v2"] |
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_N_SHARDS_PER_SPLIT = { |
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"v1": {"train": {'s2orc': 10, 's2ag': 10}, "valid": {'s2orc': 1, 's2ag': 1}}, |
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"v2": {"train": {'s2orc': 10, 's2ag': 10}, "valid": {'s2orc': 1, 's2ag': 1}}, |
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} |
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_DATA_URL = "\ |
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https://huggingface.co/datasets/allenai/pes2o/resolve/main/\ |
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{name}/{subset}/{split}/{shard:05d}.json.gz\ |
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" |
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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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_CITATION = "" |
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class pes2o(datasets.GeneratorBasedBuilder): |
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"""Pretraining Efficiently on S2ORC!""" |
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BUILDER_CONFIGS = [datasets.BuilderConfig(name) for name in _VARIANTS] |
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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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"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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), |
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supervised_keys=None, |
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homepage=_URL, |
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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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for split in ["train", "validation"]: |
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n_shards = _N_SHARDS_PER_SPLIT[self.config.name][split] |
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data_urls[split] = [ |
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_DATA_URL.format( |
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name=self.config.name, |
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split=split, |
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subset=subset, |
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index=index |
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) |
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for subset, n_shards in n_shards.items() |
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for index in range(n_shards) |
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] |
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train_downloaded_files = dl_manager.download( |
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data_urls["train"] |
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) |
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validation_downloaded_files = dl_manager.download( |
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data_urls["validation"] |
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) |
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return [ |
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datasets.SplitGenerator( |
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name=str(datasets.Split.TRAIN), gen_kwargs={ |
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"filepaths": train_downloaded_files |
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}), |
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datasets.SplitGenerator( |
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name=str(datasets.Split.VALIDATION), gen_kwargs={ |
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"filepaths": validation_downloaded_files |
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} |
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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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