Update README.md

README.md

@@ -1,3 +1,54 @@
 ---
 license: mit
+language:
+- en
+tags:
+- membership inference
+- privacy
+pretty_name: MIMIR
+size_categories:
+- 1K<n<10K
 ---
+
+# MIMIR
+
+These datasets serve as a benchmark designed to evaluate membership inference attack (MIA) methods, specifically in detecting pretraining data from extensive large language models.
+
+## 📌 Applicability
+
+The datasets can be applied to any model trained on The Pile, including (but not limited to):
+- GPTNeo
+- Pythia
+- OPT
+
+## Loading the datasets
+To load the dataset:
+
+```python
+from datasets import load_dataset
+
+dataset = load_dataset("iamgroot42/mimir", split=f"pile_cc")
+```
+
+- Available Splits: `arxiv`, `wikipedia_en`, `pile_cc`, `dm_mathematics`, `pubmed_central`, `full_pile`, ...
+- Labels:
+  - _0_: Refers to the unseen data during pretraining.
+  - _1_: Refers to the seen data.
+
+## 🛠️ Codebase
+For evaluating MIA methods on our datasets, visit our [GitHub repository](http://github.com/iamgroot42/mimir).
+
+## ⭐ Citing our Work
+
+If you find our codebase and datasets beneficial, kindly cite our work:
+
+```bibtex
+@inproceedings{duan2024disentangling,
+  title={Disentangling Challenges in Membership Inference for Large Language Models},
+  author={},
+  booktitle={,
+  year={2024}
+}
+```
+
+