Datasets:
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import gzip
import json
import datasets
logger = datasets.logging.get_logger(__name__)
_URL = "https://huggingface.co/datasets/allenai/pes2o"
_VARIANTS = ["v1", "v2"]
_N_SHARDS_PER_SPLIT = {
"v1": {"train": {'s2orc': 10, 's2ag': 10}, "valid": {'s2orc': 1, 's2ag': 1}},
"v2": {"train": {'s2orc': 10, 's2ag': 10}, "valid": {'s2orc': 1, 's2ag': 1}},
}
_DATA_URL = "\
https://huggingface.co/datasets/allenai/pes2o/resolve/main/\
{name}/{subset}/{split}/{shard:05d}.json.gz\
"
_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.\
"
_CITATION = ""
class pes2o(datasets.GeneratorBasedBuilder):
"""Pretraining Efficiently on S2ORC!"""
BUILDER_CONFIGS = [datasets.BuilderConfig(name) for name in _VARIANTS]
def _info(self):
return datasets.DatasetInfo(
description=_DESCRIPTION,
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")
}
),
supervised_keys=None,
homepage=_URL,
citation=_CITATION,
)
def _split_generators(self, dl_manager):
data_urls = {}
for split in ["train", "validation"]:
n_shards = _N_SHARDS_PER_SPLIT[self.config.name][split]
data_urls[split] = [
_DATA_URL.format(
name=self.config.name,
split=split,
subset=subset,
index=index
)
for subset, n_shards in n_shards.items()
for index in range(n_shards)
]
train_downloaded_files = dl_manager.download(
data_urls["train"]
)
validation_downloaded_files = dl_manager.download(
data_urls["validation"]
)
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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