| import gzip |
| import json |
|
|
| import datasets |
|
|
| logger = datasets.logging.get_logger(__name__) |
|
|
|
|
| _HOMEPAGE = "https://github.com/allenai/peS2o" |
|
|
|
|
| _DESCRIPTION = "\ |
| The peS2o dataset is a collection of ~40M creative commmon licensed academic \ |
| papers, cleaned, filtered, and formatted for pre-training of language models. \ |
| It is derived from the Semantic Scholar Open Research Corpus(Lo et al, 2020), \ |
| or S2ORC.\ |
| " |
|
|
| _LICENSE = "odc-by" |
|
|
| _VARIANTS = { |
| "v1": { |
| "version": "1.0.0", |
| "download_size": 100702002904, |
| "dataset_size": 67787014, |
| "splits": { |
| "train": { |
| "num_bytes": 100145555091, |
| "num_examples": 67624463, |
| "files": [ |
| "data/v1/train-00000-of-00020.json.gz", |
| "data/v1/train-00001-of-00020.json.gz", |
| "data/v1/train-00002-of-00020.json.gz", |
| "data/v1/train-00003-of-00020.json.gz", |
| "data/v1/train-00004-of-00020.json.gz", |
| "data/v1/train-00005-of-00020.json.gz", |
| "data/v1/train-00006-of-00020.json.gz", |
| "data/v1/train-00007-of-00020.json.gz", |
| "data/v1/train-00008-of-00020.json.gz", |
| "data/v1/train-00009-of-00020.json.gz", |
| "data/v1/train-00010-of-00020.json.gz", |
| "data/v1/train-00011-of-00020.json.gz", |
| "data/v1/train-00012-of-00020.json.gz", |
| "data/v1/train-00013-of-00020.json.gz", |
| "data/v1/train-00014-of-00020.json.gz", |
| "data/v1/train-00015-of-00020.json.gz", |
| "data/v1/train-00016-of-00020.json.gz", |
| "data/v1/train-00017-of-00020.json.gz", |
| "data/v1/train-00018-of-00020.json.gz", |
| "data/v1/train-00019-of-00020.json.gz", |
| ], |
| }, |
| "validation": { |
| "num_bytes": 556447813, |
| "num_examples": 162551, |
| "files": [ |
| "data/v1/validation-00000-of-00002.json.gz", |
| "data/v1/validation-00001-of-00002.json.gz", |
| ], |
| }, |
| }, |
| }, |
| "v2": { |
| "version": "1.0.0", |
| "download_size": 87129236480, |
| "dataset_size": 38972211, |
| "splits": { |
| "train": { |
| "num_bytes": 86572382178, |
| "num_examples": 38811179, |
| "files": [ |
| "data/v2/train-00000-of-00020.json.gz", |
| "data/v2/train-00001-of-00020.json.gz", |
| "data/v2/train-00002-of-00020.json.gz", |
| "data/v2/train-00003-of-00020.json.gz", |
| "data/v2/train-00004-of-00020.json.gz", |
| "data/v2/train-00005-of-00020.json.gz", |
| "data/v2/train-00006-of-00020.json.gz", |
| "data/v2/train-00007-of-00020.json.gz", |
| "data/v2/train-00008-of-00020.json.gz", |
| "data/v2/train-00009-of-00020.json.gz", |
| "data/v2/train-00010-of-00020.json.gz", |
| "data/v2/train-00011-of-00020.json.gz", |
| "data/v2/train-00012-of-00020.json.gz", |
| "data/v2/train-00013-of-00020.json.gz", |
| "data/v2/train-00014-of-00020.json.gz", |
| "data/v2/train-00015-of-00020.json.gz", |
| "data/v2/train-00016-of-00020.json.gz", |
| "data/v2/train-00017-of-00020.json.gz", |
| "data/v2/train-00018-of-00020.json.gz", |
| "data/v2/train-00019-of-00020.json.gz", |
| ], |
| }, |
| "validation": { |
| "num_bytes": 556854302, |
| "num_examples": 161032, |
| "files": [ |
| "data/v2/validation-00000-of-00002.json.gz", |
| "data/v2/validation-00001-of-00002.json.gz", |
| ], |
| }, |
| }, |
| }, |
| } |
|
|
| _FEATURES = datasets.Features( |
| added=datasets.Value("string"), |
| created=datasets.Value("string"), |
| id=datasets.Value("string"), |
| source=datasets.Value("string"), |
| text=datasets.Value("string"), |
| version=datasets.Value("string"), |
| ) |
|
|
| _CITATION = """\ |
| @techreport{peS2o, |
| author = {Luca Soldaini and Kyle Lo}, |
| year = 2023, |
| title = {{peS2o (Pretraining Efficiently on S2ORC) Dataset}}, |
| institution = {{Allen Institute for AI}}, |
| note = {ODC-By, \\url{https://github.com/allenai/pes2o}} |
| } |
| """ |
|
|
|
|
| class PeS2o(datasets.GeneratorBasedBuilder): |
| """Pretraining Efficiently on S2ORC!""" |
|
|
| BUILDER_CONFIGS = [ |
| datasets.BuilderConfig(name=name, version=config["version"]) |
| for name, config in _VARIANTS.items() |
| ] |
|
|
| DEFAULT_CONFIG_NAME = "v2" |
|
|
| def _info(self): |
| """Give information and typings for the dataset.""" |
| return datasets.DatasetInfo( |
| description=_DESCRIPTION, |
| features=_FEATURES, |
| supervised_keys=None, |
| homepage=_HOMEPAGE, |
| license=_LICENSE, |
| citation=_CITATION, |
| dataset_size=_VARIANTS[self.config.name]["dataset_size"], |
| download_size=_VARIANTS[self.config.name]["download_size"], |
| ) |
|
|
| def _split_generators(self, dl_manager): |
| train_downloaded_files = dl_manager.download( |
| _VARIANTS[self.config.name]["splits"]["train"]["files"] |
| ) |
| validation_downloaded_files = dl_manager.download( |
| _VARIANTS[self.config.name]["splits"]["validation"]["files"] |
| ) |
| return [ |
| datasets.SplitGenerator( |
| name=str(datasets.Split.TRAIN), |
| gen_kwargs={"filepaths": train_downloaded_files}, |
| ), |
| datasets.SplitGenerator( |
| name=str(datasets.Split.VALIDATION), |
| gen_kwargs={"filepaths": validation_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("generating examples from = %s", 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_, example |
| id_ += 1 |
|
|
