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---
license: apache-2.0
dataset_info:
- config_name: arxiv
features:
- name: text
dtype: string
splits:
- name: forget
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- name: approximate
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- name: retain
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download_size: 216767075
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- config_name: general
features:
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dtype: string
splits:
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- name: retain
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- config_name: github
features:
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- name: approximate
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- name: retain
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configs:
- config_name: arxiv
data_files:
- split: forget
path: arxiv/forget-*
- split: approximate
path: arxiv/approximate-*
- split: retain
path: arxiv/retain-*
- config_name: general
data_files:
- split: evaluation
path: general/evaluation-*
- split: retain
path: general/retain-*
- config_name: github
data_files:
- split: forget
path: github/forget-*
- split: approximate
path: github/approximate-*
- split: retain
path: github/retain-*
---
# 📖 unlearn_dataset
The unlearn_dataset serves as a benchmark for evaluating unlearning methodologies in pre-trained large language models across diverse domains, including arXiv, GitHub.
## 🔍 Loading the datasets
To load the dataset:
```python
from datasets import load_dataset
dataset = load_dataset("llmunlearn/unlearn_dataset", name="arxiv", split="forget")
```
* Available configuration names and corresponding splits:
- `arxiv`: `forget, approximate, retain`
- `github`: `forget, approximate, retain`
- `general`: `evaluation, retain`
## 🛠️ Codebase
For evaluating unlearning methods on our datasets, visit our [GitHub repository](https://github.com/yaojin17/Unlearning_LLM).
## ⭐ Citing our Work
If you find our codebase or dataset useful, please consider citing our paper:
```bib
@article{yao2024machine,
title={Machine Unlearning of Pre-trained Large Language Models},
author={Yao, Jin and Chien, Eli and Du, Minxin and Niu, Xinyao and Wang, Tianhao and Cheng, Zezhou and Yue, Xiang},
journal={arXiv preprint arXiv:2402.15159},
year={2024}
}
```
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