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# HuggingFace loading script for generic sprucfluo datasets
# This script was automatically generated by convert_hf_to_sprucfluo
import json
import pathlib
import datasets
import fsspec
from datasets import DatasetInfo, Value, Features
logger = datasets.logging.get_logger(__name__)
_INFO = DatasetInfo(
description='Automatically generated for wikitext (wikitext-103-raw-v1), split into 8 shards, detokenized.\n\nOriginal Description:\n The WikiText language modeling dataset is a collection of over 100 million tokens extracted from the set of verified\n Good and Featured articles on Wikipedia. The dataset is available under the Creative Commons Attribution-ShareAlike\n License.\n',
citation='@misc{merity2016pointer,\n title={Pointer Sentinel Mixture Models},\n author={Stephen Merity and Caiming Xiong and James Bradbury and Richard Socher},\n year={2016},\n eprint={1609.07843},\n archivePrefix={arXiv},\n primaryClass={cs.CL}\n}\n',
homepage='https://blog.einstein.ai/the-wikitext-long-term-dependency-language-modeling-dataset/',
license='Creative Commons Attribution-ShareAlike 4.0 International (CC BY-SA 4.0)',
version="1.0.0",
features=Features.from_dict({'text': {'dtype': 'string', 'id': None, '_type': 'Value'}}),
supervised_keys=None)
class AutoDataset(datasets.GeneratorBasedBuilder):
BUILDER_CONFIGS = [datasets.BuilderConfig()]
def __init__(self, **kwargs):
super().__init__(**kwargs)
def _info(self):
return _INFO
@property
def dataset_dir(self):
return pathlib.Path(__file__).parent
def _split_generators(self, dl_manager):
metadata = json.load(open(dl_manager.download("metadata.json"), 'rt'))
return [
datasets.SplitGenerator(
name=split,
gen_kwargs={"filepaths": dl_manager.download(split_metadata["files"])},
)
for split, split_metadata in metadata["splits"].items()
]
def _generate_examples(self, filepaths):
"""This function returns the examples in the raw (text) form by iterating on all the files."""
id_: int = 0
for filepath in filepaths:
logger.info(f"Generating examples from {filepath}")
with fsspec.open(filepath, mode="rt", compression="infer", encoding="utf-8") as f:
for line in f:
if line:
example = json.loads(line)
yield id_, example
id_ += 1
if __name__ == "__main__":
AutoDataset().download_and_prepare() |