Upload biasneutral.py

biasneutral.py

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+from datasets import DatasetInfo, GeneratorBasedBuilder, Split, SplitGenerator, load_dataset, Features, Value
+
+
+class BiasneutralDataset(GeneratorBasedBuilder):
+    """
+    This dataset filters entries from BookCorpus based on provided indices in the BIASNEUTRAL dataset.
+    """
+
+    _CITATION = """
+@misc{drechsel2025gradiendmonosemanticfeaturelearning,
+  title={{GRADIEND}: Feature Learning within Neural Networks Exemplified through Biases}, 
+  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/biasneutral/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']}