Add dataset card, link to paper and GitHub

This PR improves the dataset card for the CSRP dataset. It adds relevant YAML metadata (task categories, language, license), links to the research paper and the official GitHub repository, and provides a summary of the 340,000 Chain-of-Thought (CoT) samples included in this repository.

README.md

@@ -1,3 +1,44 @@
----
-license: mit
----
+---
+license: mit
+task_categories:
+- text-generation
+language:
+- zh
+tags:
+- text-correction
+- cgec
+- chain-of-thought
+---
+
+# CSRP: Chain-of-Thought Reasoning for Chinese Text Correction
+
+This dataset contains the 340,000 Chain-of-Thought (CoT) reasoning samples introduced in the paper [CSRP: Chain-of-Thought Reasoning for Chinese Text Correction via Reinforcement Learning with Efficiency-Aware Rewards](https://huggingface.co/papers/2606.00020).
+
+## Links
+- **Paper**: [CSRP: Chain-of-Thought Reasoning for Chinese Text Correction via Reinforcement Learning with Efficiency-Aware Rewards](https://huggingface.co/papers/2606.00020)
+- **GitHub Repository**: [TW-NLP/ChineseErrorCorrector](https://github.com/TW-NLP/ChineseErrorCorrector)
+
+## Dataset Summary
+The CSRP dataset is designed to enhance Chinese Grammatical Error Correction (CGEC) by providing explicit error reasoning (Chain-of-Thought) for corrections. This diagnostic transparency helps models internalize linguistic priors and effectively mitigates over-correction bias.
+
+The dataset includes 340,000 balanced samples covering various error types:
+- Spelling errors (错别字)
+- Word collocation errors (词语搭配错误)
+- Word order errors (语序错误)
+- Component omission or redundancy (成分残缺/赘余)
+- Semantic logic issues (语义逻辑不通)
+
+## Citation
+If you use this dataset, please cite:
+
+```bibtex
+@misc{tian2026csrpchainofthoughtreasoningchinese,
+      title={CSRP: Chain-of-Thought Reasoning for Chinese Text Correction via Reinforcement Learning with Efficiency-Aware Rewards}, 
+      author={Wei Tian and Yuhao Zhou and Man Lan},
+      year={2026},
+      eprint={2606.00020},
+      archivePrefix={arXiv},
+      primaryClass={cs.CL},
+      url={https://arxiv.org/abs/2606.00020}, 
+}
+```