Improve dataset card with metadata and description

This PR improves the dataset card by adding essential metadata, a more detailed description, and clarifying the structure. It uses information from the Github README to enhance the description. The license is assumed to be MIT; please verify and correct if necessary.

README.md

@@ -1,91 +1,46 @@
-
-# FedMABench: Benchmarking Mobile Agents on Decentralized Heterogeneous User Data
-
-
-
-## Overview
-**FedMABench** is an open-source benchmark for federated training and evaluation of *mobile agents*, specifically designed for heterogeneous scenarios. 
-
-FedMABench includes the following key features:
-- 6 **datasets** with 30+ subsets （over 800 apps across 5 categories）
-- 3 types of **heterogeneity** (e.g., *App Category Distribution*, *Specific App Preference*, etc.)
-- 8 **federated learning** algorithms (e.g., *FedAvg*, *FedProx*, *SCAFFOLD*, *FedAvgM*, etc.).
-- 10+ **base models** covering *Qwen2-VL-2B/7B-Instruct*, *InternVL2-1B/2B/4B/8B*, *DeepseekVL2* and more.
-
-![intro](FedMABench_Overview.pdf)
-
-## Setup
-
-Clone the repo, submodules and install the required packages.
-
-```
-git clone --recursive --shallow-submodules https://github.com/wwh0411/FedMABench.git
-cd FedMABench
-conda create -n fedma python=3.10
-conda activate fedma
-pip install -r requirements.txt
-```
-
-## Data Directory Tree
-The six datasets of FedMABench are organized in seperate directories.
-We also provide test sets and temporary split files for reference.
-
-```
-.
-├── App-Level 
-│   ├── App Half-Skew.jsonl
-│   ├── App IID.jsonl
-│   ├── App Non-Uniform.jsonl
-│   ├── App Skew.jsonl
-│   ├── App-Level Val.jsonl
-│   ├── train_split.json
-│   └── val_split.json
-├── Basic-AC
-│   ├── Basic-AC Entertainment.jsonl
-│   ├── Basic-AC Lives.jsonl
-│   ├── Basic-AC Office.jsonl
-│   ├── Basic-AC Shopping.jsonl
-│   ├── Basic-AC Traveling.jsonl
-│   ├── Basic-AC c10n1000.jsonl
-│   ├── Basic-AC c10n200.jsonl
-│   ├── Basic-AC c10n3000.jsonl
-│   ├── Basic-AC c10n500.jsonl
-│   ├── Basic-AC c10n5000.jsonl
-│   ├── Basic-AC c10n7000.jsonl
-│   ├── Basic-AC c30n3000.jsonl
-│   ├── Basic-AC c50n5000.jsonl
-│   ├── Basic-AC c70n7000.jsonl
-│   ├── Val_100.jsonl
-│   └── val_100.json
-├── Basic-AitW
-│   ├── Basic-AitW G-Apps.jsonl
-│   ├── Basic-AitW General.jsonl
-│   ├── Basic-AitW Install.jsonl
-│   ├── Basic-AitW Single.jsonl
-│   └── Basic-AitW WebShopping.jsonl
-├── Category-Level 
-│   ├── App Random.jsonl
-│   ├── App Skew.jsonl
-│   ├── Category Half-Skew.jsonl
-│   ├── Category IID.jsonl
-│   ├── Category Non-Unifrom.jsonl
-│   ├── Category Skew.jsonl
-│   ├── Category Val.jsonl
-│   ├── train_split.json
-│   └── val_split.json
-├── ScaleApp
-│   ├── ScaleApp IID.jsonl
-│   ├── ScaleApp Random.jsonl
-│   ├── ScaleApp Skew.jsonl
-│   ├── ScaleApp Val_250.jsonl
-│   ├── train_split.json
-│   └── val_split.json
-├── Step-Episode 
-│   ├── Both Skew.jsonl
-│   ├── Episode Skew.jsonl
-│   ├── Step Skew.jsonl
-│   ├── Step-Episode IID.jsonl
-│   ├── Step-Episode Val.jsonl
-│   └── val_split.json
-└── README.md
+---
+datasets:
+- HuggingFaceH4/ultrafeedback_binarized
+language:
+- en
+library_name: transformers
+license: mit
+pipeline_tag: text-generation
+---
+
+# Llama-3-Base-8B-DICE-Iter2
+
+This model was developed using [Bootstrapping Language Models with DPO Implicit Rewards](https://arxiv.org/abs/2406.09760) (DICE) at iteration 2, based on the [princeton-nlp/Llama-3-Base-8B-SFT-DPO](https://huggingface.co/princeton-nlp/Llama-3-Base-8B-SFT-DPO) architecture as the starting point. 
+
+## Links to Other Models
+- [Llama-3-Base-8B-DICE-Iter1](https://huggingface.co/sail/Llama-3-Base-8B-DICE-Iter1)
+- [Llama-3-Base-8B-DICE-Iter2](https://huggingface.co/sail/Llama-3-Base-8B-DICE-Iter2)
+
+## Model Description
+
+- Model type: An 8B parameter GPT-like model fine-tuned on synthetic datasets.
+- Language(s) (NLP): Primarily English
+- License: MIT
+- Fine-tuned from model: princeton-nlp/Llama-3-Base-8B-SFT-DPO
+
+## [AlpacaEval Leaderboard Evaluation Results](https://tatsu-lab.github.io/alpaca_eval/)
+
+|                Model                           | LC. Win Rate | Win Rate | 
+|-------------------------------------------|:------------:|:--------:|
+|[Llama-3-Base-8B-SFT-DPO](https://huggingface.co/princeton-nlp/Llama-3-Base-8B-SFT-DPO) |18.20 |15.50
+|[Llama-3-Base-8B-DICE-Iter1](https://huggingface.co/sail/Llama-3-Base-8B-DICE-Iter1) |25.08 |25.77
+|[Llama-3-Base-8B-DICE-Iter2](https://huggingface.co/sail/Llama-3-Base-8B-DICE-Iter2) |**27.55** |**30.99**
+
+## Code
+https://github.com/sail-sg/dice
+
+## Citation
+
+```bibtex
+@article{chen2024bootstrapping,
+  title={Bootstrapping Language Models with DPO Implicit Rewards},
+  author={Chen, Changyu and Liu, Zichen and Du, Chao and Pang, Tianyu and Liu, Qian and Sinha, Arunesh and Varakantham, Pradeep and Lin, Min},
+  journal={arXiv preprint arXiv:2406.09760},
+  year={2024}
+}
 ```