Update README.md

README.md

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----
-license: apache-2.0
----
+---
+license: apache-2.0
+language:
+- en
+metrics:
+- name: accuracy
+  valud: 75.39
+base_model:
+- Qwen/Qwen3-Reranker-0.6B
+pipeline_tag: text-classification
+---
+
+# EduBenchEvaluator
+
+This is a fine-tuned evaluator designed to assess LLM on the **EduBench** benchmark. 
+
+- 📄 **[Paper](https://arxiv.org/abs/2505.16160)**
+- 💻 **[GitHub Repository](https://github.com/DIRECT-BIT/EduBench)**
+
+## Model Details
+
+* **Model Name**: EduBenchEvaluator
+* **Model Type**: Fine-tuned language model (0.6B parameters)
+* **Base Model**: [Qwen3-Reranker-0.6B](https://huggingface.co/Qwen/Qwen3-Reranker-0.6B)
+
+## Training & Methodology
+
+The base model, `Qwen3-Reranker-0.6B`, was fine-tuned to align with human evaluations on the EduBench dataset. 
+
+We approached the fine-tuning process as a text classification task. The model evaluates a given response by taking a `<question, answer, metric>` triplet as input. Based on this context, it is trained to output a precise evaluation score ranging from **1 to 5**. 
+
+This evaluator is specifically constructed to measure an LLM's capability across the diverse educational tasks presented in EduBench. 
+
+## Performance
+
+* **Accuracy**: The model achieves a satisfactory accuracy of **75.28%** on the test set.
+* **Human Alignment**: In addition to standard accuracy, we calculated the correlation between the model's predictions and actual human scorers, demonstrating that the model closely mirrors human judgment.
+
+*Note: Further evaluation results and comparisons are reported on our [GitHub](https://github.com/DIRECT-BIT/EduBench).*
+
+## 🫣 Citation
+
+If you find our benchmark, evaluation pipeline, or models useful or interesting, please cite our paper:
+
+```bibtex
+@misc{xu2025edubenchcomprehensivebenchmarkingdataset,
+      title={EduBench: A Comprehensive Benchmarking Dataset for Evaluating Large Language Models in Diverse Educational Scenarios}, 
+      author={Bin Xu and Yu Bai and Huashan Sun and Yiguan Lin and Siming Liu and Xinyue Liang and Yaolin Li and Yang Gao and Heyan Huang},
+      year={2025},
+      eprint={2505.16160},
+      archivePrefix={arXiv},
+      primaryClass={cs.CL},
+      url={[https://arxiv.org/abs/2505.16160](https://arxiv.org/abs/2505.16160)}, 
+}