Upload genter.py

genter.py

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+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):
+        # URL for your indices file hosted on Hugging Face
+        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"
+            # Download the indices file
+            indices_file = dl_manager.download_and_extract(index_file_url)
+            indices_files[split] = indices_file
+
+        # 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_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.")
+
+            # Filter BookCorpus based on indices and yield examples
+        for _, sample in df.iterrows():
+            idx = sample['index']
+            name = sample['name']
+            pronoun = sample['pronoun']
+            assert pronoun in {'he', 'she'}
+            label = 'M' if pronoun == 'she' else 'F'
+
+            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,
+            }