---
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.