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README.md

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+# 📘 BookMIA Datasets
+
+The **BookMIA datasets** serve as a benchmark designed to evaluate membership inference attack (MIA) methods, specifically in detecting pretraining data from OpenAI models that are released before 2023 (such as text-davinci-003). 
+The dataset contains non-member and member data: non-member data consists of text excerpts from books first published in 2023, while member data includes text excerpts from older books, as categorized by Chang et al. in 2023.
+
+### 📌 Applicability
+
+The datasets can be applied to various OpenAI models released before **2023**:
+
+- text-davinci-001
+- text-davinci-002
+- ... and more.
+
+## Loading the datasets
+
+To load the dataset:
+
+```python
+from datasets import load_dataset
+
+LENGTH = 64
+dataset = load_dataset("swj0419/BookMIA")
+```
+* Text Lengths: `512`.
+* *Label 0*: Refers to the unseen data during pretraining. *Label 1*: Refers to the seen data.
+
+## 🛠️ Codebase
+
+For evaluating MIA methods on our datasets, visit our [GitHub repository](https://github.com/swj0419/detect-pretrain-code).
+
+## ⭐ Citing our Work
+
+If you find our codebase and datasets beneficial, kindly cite our work:
+
+```bibtex
+@misc{shi2023detecting,
+    title={Detecting Pretraining Data from Large Language Models},
+    author={Weijia Shi and Anirudh Ajith and Mengzhou Xia and Yangsibo Huang and Daogao Liu and Terra Blevins and Danqi Chen and Luke Zettlemoyer},
+    year={2023},
+    eprint={2310.16789},
+    archivePrefix={arXiv},
+    primaryClass={cs.CL}
+}
+
+[1] Kent K Chang, Mackenzie Cramer, Sandeep Soni, and David Bamman. Speak, memory: An archaeology of books known to chatgpt/gpt-4. arXiv preprint arXiv:2305.00118, 2023.