Add pipeline tag, library name, and paper link to model card

README.md

@@ -1,13 +1,16 @@
 ---
-license: apache-2.0
-language:
-- en
 base_model:
 - Qwen/Qwen2.5-Omni-7B
+language:
+- en
+license: apache-2.0
+library_name: transformers
+pipeline_tag: audio-text-to-text
 ---
 
 # EmotionThinker: Prosody-Aware Reinforcement Learning for Explainable Speech Emotion Reasoning
 
+This repository contains the model presented in the paper [EmotionThinker: Prosody-Aware Reinforcement Learning for Explainable Speech Emotion Reasoning](https://huggingface.co/papers/2601.15668).
 
 [![ICLR 2026 Oral](https://img.shields.io/badge/ICLR%202026-Oral-gold)](https://arxiv.org/pdf/2601.15668) [![Project](https://img.shields.io/badge/Project-Page-green)](https://github.com/dingdongwang/EmotionThinker)
 
@@ -16,7 +19,7 @@ base_model:
 </p>
 
 ## Introduction
-EmotionThinker is the first RL–enhanced SpeechLLM framework for interpretable speech emotion reasoning. For details, please refer to the [paper](https://arxiv.org/pdf/2601.15668). 
+EmotionThinker is the first RL–enhanced SpeechLLM framework for interpretable speech emotion reasoning. For details, please refer to the [paper](https://huggingface.co/papers/2601.15668). 
 
 Unlike conventional speech emotion recognition (SER) systems that treat emotion as a flat classification problem, EmotionThinker reframes SER as a deep reasoning problem, enabling models to jointly produce accurate emotion labels and structured, human-aligned explanations.
 
@@ -29,7 +32,7 @@ EmotionThinker offers the following advantages:
 
 ## Quickstart
 
-```
+```python
 import torch
 from transformers import Qwen2_5OmniForConditionalGeneration, Qwen2_5OmniProcessor
 from qwen_omni_utils import process_mm_info
@@ -69,12 +72,11 @@ with torch.no_grad():
 
 text = processor.batch_decode(text_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
 print(text)
-
 ```
 
 ## Citation
 If you find this model useful in your research, please kindly cite:
-```
+```bibtex
 @inproceedings{wang2026emotionthinker,
   title={EmotionThinker: Prosody-Aware Reinforcement Learning for Explainable Speech Emotion Reasoning},
   author={Wang, Dingdong and Liu, Shujie and Zhang, Tianhua and Chen, Youjun and Li, Jinyu and Meng, Helen},