Add pipeline tag and library name, add usage example and missing sections

README.md

@@ -1,10 +1,13 @@
 ---
-license: mit
-language:
-- en
 base_model:
 - Qwen/Qwen2.5-14B-Instruct
+language:
+- en
+license: mit
+pipeline_tag: text-ranking
+library_name: transformers
 ---
+
 ![image/png](https://cdn-uploads.huggingface.co/production/uploads/654d784d71a30c4bca09a319/Q7MVJfIHDerQ24c1zwZwK.png)
 
 <font size=3><div align='center' >  
@@ -13,7 +16,7 @@ base_model:
 [[**📖 Paper**](https://arxiv.org/abs/2505.02387)]
 </div></font>
 
-# 🚀 Can we cast reward modeling as a reasoning task?
+# 🚀 Can we cast reward modeling as a reasoning task?\
 
 **RM-R1** is a training framework for *Reasoning Reward Model* (ReasRM) that judges two candidate answers by first **thinking out loud**—generating rubrics or reasoning traces—then emitting its preference.  
 Compared with prior scalar or vanilla generative reward models, RM-R1 delivers up to **+13.8 % absolute accuracy gains** on public reward model benchmarks while providing *fully interpretable* critiques.
@@ -28,4 +31,48 @@ Compared with prior scalar or vanilla generative reward models, RM-R1 delivers u
 ## Intended uses
 * **RLHF / RLAIF**: plug-and-play reward function for policy optimisation.  
 * **Automated evaluation**: LLM-as-a-judge for open-domain QA, chat, and reasoning.  
-* **Research**: study process supervision, chain-of-thought verification, or rubric generation.
+* **Research**: study process supervision, chain-of-thought verification, or rubric generation.
+
+```python
+from transformers import pipeline
+
+classifier = pipeline("text-classification", model="your_model", return_all_scores=True)
+results = classifier("This is a great model!", candidate_labels=["positive", "negative"])
+print(results)
+```
+
+## Training
+- coming soon 
+
+## Evaluation 
+- coming soon
+
+## Use Our Model 
+- coming soon
+
+## Build Your Own Dataset 
+- coming soon 
+
+## Features 
+- Open release of trained model and the full accompanying datasets. ✔️ 
+- End-to-end pipelines for both supervised fine-tuning (SFT) and reinforcement learning (RL). ✔️ 
+- Support different RL frameworks. ✔️   
+- Support Slurm v.s. Interactive Training. ✔️ 
+- Support multi-node, multi-gpu training. ✔️ 
+- Support different LLMs. ✔️ 
+- Support building your own custom dataset. 
+- One-command evaluation on public RM benchmarks for quick, reproducible reporting.
+
+## Acknowledgement 
+The concept of RM-R1 is inspired by [Deepseek-R1](https://github.com/deepseek-ai/DeepSeek-R1). Its implementation is built upon [veRL](https://github.com/volcengine/verl) and [OpenRLHF](https://github.com/OpenRLHF/OpenRLHF). We sincerely appreciate the efforts of these teams for their contributions to open-source research and development.
+
+## Citations
+
+```bibtex
+@misc{2505.02387,
+Author = {Xiusi Chen and Gaotang Li and Ziqi Wang and Bowen Jin and Cheng Qian and Yu Wang and Hongru Wang and Yu Zhang and Denghui Zhang and Tong Zhang and Hanghang Tong and Heng Ji},
+Title = {RM-R1: Reward Modeling as Reasoning},
+Year = {2025},
+Eprint = {arXiv:2505.02387},
+}
+```