---
license: apache-2.0
dataset_info:
- config_name: arxiv
  features:
  - name: text
    dtype: string
  splits:
  - name: forget
    num_bytes: 22127152
    num_examples: 500
  - name: approximate
    num_bytes: 371246809
    num_examples: 6155
  - name: retain
    num_bytes: 84373706
    num_examples: 2000
  download_size: 216767075
  dataset_size: 477747667
- config_name: general
  features:
  - name: content
    dtype: string
  splits:
  - name: evaluation
    num_bytes: 4628036
    num_examples: 1000
  - name: retain
    num_bytes: 24472399
    num_examples: 5000
  download_size: 17206382
  dataset_size: 29100435
- config_name: github
  features:
  - name: text
    dtype: string
  splits:
  - name: forget
    num_bytes: 14069535
    num_examples: 2000
  - name: approximate
    num_bytes: 82904771
    num_examples: 15815
  - name: retain
    num_bytes: 28749659
    num_examples: 4000
  download_size: 43282163
  dataset_size: 125723965
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}
}
```