Update README.md

README.md

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 ---
 datasets:
 - baidu/TARA
+license: mit
+language:
+- en
+library_name: transformers
 ---
+
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+  <a href="https://iclr.cc/Conferences/2024" target="_blank">
+      <img alt="ICLR 2024" src="https://img.shields.io/badge/Proceedings-ICLR2024-red" />
+   </a>
+
+Offical checkpoint for [Tool-Augmented Reward Modeling (ICLR 2024 spotlight)](https://openreview.net/pdf?id=d94x0gWTUX).
+
+
+
 # Model Description
 
 Themis is a tool-augmented preference model to address these limitations by empowering RMs with access to external environments, including calculators and search engines.
-It was introduced in this [paper](https://arxiv.org/pdf/2310.01045.pdf) and first released in this [repository](https://github.com/ernie-research/Tool-Augmented-Reward-Model).
+It was introduced in the [ICLR 2024 paper](https://arxiv.org/pdf/2310.01045.pdf) and first released in this [repository](https://github.com/ernie-research/Tool-Augmented-Reward-Model).
 Themis-7b is trained with [TARA](https://huggingface.co/datasets/baidu/TARA), achieving a noteworthy overall improvement of 17.7% across eight tasks in preference ranking.
 
+## 🔥 News
+* **9 February, 2024:** 🎉 We release the official codebase and model weights of [`baidu/Themis-7b`](https://huggingface.co/baidu/Themis-7b). Stay tuned!🔥
+* **16 January, 2024:** 🎉 Our work has been accepted to [ICLR 2024](https://iclr.cc/Conferences/2024) **spotlight**! ✨
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 # Citation
 ```text
 @inproceedings{tarm-2024-ernie,