| --- |
| license: cc-by-sa-4.0 |
| task_categories: |
| - question-answering |
| - text-generation |
| language: |
| - en |
| configs: |
| - config_name: category1_forget |
| data_files: |
| - split: train |
| path: category1_forget-train.json |
| - config_name: category1_retain |
| data_files: |
| - split: train |
| path: category1_retain-train.json |
| - split: validation |
| path: category1_retain-validation.json |
| - split: test |
| path: category1_retain-test.json |
| - config_name: category2_forget |
| data_files: |
| - split: train |
| path: category2_forget-train.json |
| - config_name: category2_retain |
| data_files: |
| - split: train |
| path: category2_retain-train.json |
| - split: validation |
| path: category2_retain-validation.json |
| - split: test |
| path: category2_retain-test.json |
| tags: |
| - llm-unlearning |
| - entity-level-unlearning |
| - wikipedia |
| size_categories: |
| - 1K<n<10K |
| --- |
| # DSEnt : Domain-Specific Entity Dataset |
| DSEnt is a domain-specific entity dataset built from English Wikipedia pages and automatically generated using GPT-5.5. |
| The dataset spans multiple domains (categories). For each domain, it includes five real-world target entities, and each target entity is paired with five neighboring entities selected from related Wikipedia pages. |
| 1. Category 1 : MLB Players. |
| 2. Category 2 : Writers. |
|
|
| # Usage in Python |
| ```python |
| from datasets import load_dataset |
| |
| # Load forget data |
| train_data = load_dataset("yaopaul/DSEnt", name="category1_forget", split="train") |
| |
| # Load retain data |
| retain_train_data = load_dataset("yaopaul/DSEnt", name="category1_retain", split="train") |
| retain_valid_data = load_dataset("yaopaul/DSEnt", name="category1_retain", split="validation") |
| retain_test_data = load_dataset("yaopaul/DSEnt", name="category1_retain", split="test") |
| ``` |
|
|
| # Acknowledgement |
| The construction procedure of DSEnt was inspired by [6rightjade/ELUDe](https://huggingface.co/datasets/6rightjade/ELUDe), particularly its LLM-assisted workflow for generating entity-level question-answer pairs. |
| DSEnt extends this idea to domain-specific entity settings by collecting English Wikipedia-based target and neighboring entities across multiple different categories. |
