Datasets:
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import gzip
import json
import datasets
logger = datasets.logging.get_logger(__name__)
_HOMEPAGE = "https://huggingface.co/datasets/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/v1/validation-00000-of-00002.json.gz",
"data/v1/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}},
type = {},
url = {https://huggingface.co/datasets/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
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