| import re |
|
|
| from datasets import DatasetInfo, GeneratorBasedBuilder, Split, SplitGenerator, load_dataset, Features, Value |
|
|
|
|
| class GenterDataset(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 template sentences associating first names ([NAME]) with third-person singular pronouns ([PRONOUN])", |
| features=Features({ |
| "index": Value("int32"), |
| "text": Value("string"), |
| "masked": Value("string"), |
| "label": Value("string"), |
| "name": Value("string"), |
| "pronoun": Value("string"), |
| "pronoun_count": Value("uint8"), |
| }), |
| supervised_keys=None, |
| citation=self._CITATION, |
| ) |
|
|
| def _split_generators(self, dl_manager): |
| |
| indices_files = {} |
| for split in ["train", "val", "test"]: |
| index_file_url = f"https://huggingface.co/datasets/aieng-lab/genter/resolve/main/genter_indices_{split}.csv" |
| |
| indices_file = dl_manager.download_and_extract(index_file_url) |
| indices_files[split] = indices_file |
|
|
| |
| 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_files['train'], "bookcorpus": bookcorpus}), |
| SplitGenerator(name=Split.VALIDATION, gen_kwargs={"indices_file": indices_files['val'], "bookcorpus": bookcorpus}), |
| SplitGenerator(name=Split.TEST, gen_kwargs={"indices_file": indices_files['test'], "bookcorpus": bookcorpus}), |
| ] |
|
|
| def _generate_examples(self, indices_file: str, bookcorpus): |
| """ |
| Generate examples by filtering the BookCorpus dataset using provided indices. |
| """ |
| try: |
| import pandas as pd |
| df = pd.read_csv(indices_file) |
| except ImportError: |
| raise ImportError("Please install pandas to generate GENTER.") |
|
|
| |
| for _, sample in df.iterrows(): |
| idx = sample['index'] |
| name = sample['name'] |
| pronoun = sample['pronoun'] |
| assert pronoun in {'he', 'she'} |
| label = 'F' if pronoun == 'she' else 'M' |
|
|
| text = bookcorpus[idx]['text'] |
|
|
| masked = re.sub(rf'\b{name}\b', '[NAME]', text) |
|
|
| pronoun_count = len(re.findall(rf'\b{pronoun}\b', text)) |
| masked = re.sub(rf'\b{pronoun}\b', '[PRONOUN]', masked) |
|
|
| yield idx, { |
| "index": idx, |
| "text": text, |
| "masked": masked, |
| "label": label, |
| "name": name, |
| "pronoun": pronoun, |
| "pronoun_count": pronoun_count, |
| } |
