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from datasets import DatasetInfo, GeneratorBasedBuilder, Split, SplitGenerator, load_dataset, Features, Value
class GeneutralDataset(GeneratorBasedBuilder):
"""
This dataset filters entries from BookCorpus based on provided indices in the Geneutral dataset.
"""
_CITATION = """
@misc{drechsel2025gradiendmonosemanticfeaturelearning,
title={{GRADIEND}: Monosemantic Feature Learning within Neural Networks Applied to Gender Debiasing of Transformer Models},
author={Jonathan Drechsel and Steffen Herbold},
year={2025},
eprint={2502.01406},
archivePrefix={arXiv},
primaryClass={cs.LG},
url={https://arxiv.org/abs/2502.01406},
}
"""
def _info(self):
return DatasetInfo(
description="This dataset consists of BookCorpus entries containing only gender-neutral words (excluding e.g., he, actor, ...).",
features=Features({
"index": Value("int32"),
"text": Value("string"),
}),
supervised_keys=None,
citation=self._CITATION,
)
def _split_generators(self, dl_manager):
# URL for your indices file hosted on Hugging Face
index_file_url = "https://huggingface.co/datasets/aieng-lab/geneutral/resolve/main/indices.csv"
# Download the indices file
indices_file = dl_manager.download_and_extract(index_file_url)
# Load BookCorpus dataset
print("Loading BookCorpus dataset...")
bookcorpus = load_dataset('bookcorpus', trust_remote_code=True)['train']
print("BookCorpus dataset loaded.")
return [
SplitGenerator(name=Split.TRAIN, gen_kwargs={"indices_file": indices_file, "bookcorpus": bookcorpus}),
]
def _generate_examples(self, indices_file: str, bookcorpus):
"""
Generate examples by filtering the BookCorpus dataset using provided indices.
"""
# Load indices from the file
with open(indices_file, "r", encoding="utf-8") as f:
next(f) # Skip header
indices_set = {int(line.strip().split(",")[0]) for line in f}
# Filter BookCorpus based on indices and yield examples
for idx, sample in enumerate(bookcorpus):
if idx in indices_set:
yield idx, {"index": idx, "text": sample['text']}
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