Update README.md

README.md

@@ -2,8 +2,70 @@
 library_name: transformers
 tags:
 - llama-factory
+- lora
+- news-classification
+- text-classification
+- chinese
+- deepseek-r1
+- qwen
 ---
 
 # DeepSeek-R1-Distill-Qwen-7B-News-Classifier
 
-![](https://cdn.sa.net/2025/03/07/BlGxYoiQ1XErawb.webp)
+## Model Description
+
+DeepSeek-R1-Distill-Qwen-7B-News-Classifier is a fine-tuned version of [DeepSeek-R1-Distill-Qwen-7B](https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Qwen-7B), specially optimized for news classification tasks. The base model is a distilled version from DeepSeek-R1 using Qwen2.5-Math-7B as its foundation.
+
+## Demo
+
+![](https://cdn.sa.net/2025/03/07/BlGxYoiQ1XErawb.webp)
+
+## Training Details
+
+### Training Data
+
+The model was fine-tuned on a custom dataset of 300 news classification examples in ShareGPT format. Each example contains:
+- A news headline with a classification request prefix (e.g., "新闻分类:" or similar)
+- The expected category output with reasoning chain
+
+### Training Procedure
+
+- **Framework:** LLaMA Factory
+- **Fine-tuning Method:** LoRA with LoRA+ optimizer
+- **LoRA Parameters:**
+  - LoRA+ learning rate ratio: 16
+  - Target modules: all linear layers
+  - Base learning rate: 5e-6
+  - Gradient accumulation steps: 2
+  - Training epochs: 3
+
+## Evaluation Results
+
+The model was evaluated on a test set and achieved the following metrics:
+
+- **BLEU-4:** 29.67
+- **ROUGE-1:** 56.56
+- **ROUGE-2:** 31.31
+- **ROUGE-L:** 39.86
+
+These scores indicate strong performance for the news classification task, with good alignment between model outputs and reference classifications.
+
+## Citation
+
+If you use this model in your research, please cite:
+
+```bibtex
+@misc{deepseekai2025deepseekr1incentivizingreasoningcapability,
+      title={DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning}, 
+      author={DeepSeek-AI},
+      year={2025},
+      eprint={2501.12948},
+      archivePrefix={arXiv},
+      primaryClass={cs.CL},
+      url={https://arxiv.org/abs/2501.12948}, 
+}
+```
+
+## Acknowledgements
+
+This model was fine-tuned using the [LLaMA Factory](https://github.com/hiyouga/LLaMA-Factory) framework. We appreciate the contributions of the DeepSeek AI team for the original distilled model.