Add comprehensive model card for CSC-SQL (#1)

- Add comprehensive model card for CSC-SQL (eed5f5a82f138d70bbe4693f91ec6fdf7080d660)


Co-authored-by: Niels Rogge <nielsr@users.noreply.huggingface.co>

README.md

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+---
+license: cc-by-nc-4.0
+pipeline_tag: text-generation
+library_name: transformers
+tags:
+  - text-to-sql
+  - reinforcement-learning
+  - qwen2
+  - code-generation
+datasets:
+  - cycloneboy/bird_train
+---
+
+# CSC-SQL: Corrective Self-Consistency in Text-to-SQL via Reinforcement Learning
+
+This repository contains the models and code for the paper [CSC-SQL: Corrective Self-Consistency in Text-to-SQL via Reinforcement Learning](https://arxiv.org/abs/2505.13271).
+
+📖 [Paper on arXiv](https://arxiv.org/abs/2505.13271) | 💻 [GitHub Repository](https://github.com/CycloneBoy/csc_sql)
+
+## Introduction
+
+Large language models (LLMs) have demonstrated strong capabilities in translating natural language questions about relational databases into SQL queries. In particular, test-time scaling techniques such as Self-Consistency and Self-Correction can enhance SQL generation accuracy by increasing computational effort during inference. However, these methods have notable limitations: Self-Consistency may select suboptimal outputs despite majority votes, while Self-Correction typically addresses only syntactic errors. To leverage the strengths of both approaches, we propose **CSC-SQL**, a novel method that integrates Self-Consistency and Self-Correction. CSC-SQL selects the two most frequently occurring outputs from parallel sampling and feeds them into a merge revision model for correction. Additionally, we employ the Group Relative Policy Optimization (GRPO) algorithm to fine-tune both the SQL generation and revision models via reinforcement learning, significantly enhancing output quality. Experimental results confirm the effectiveness and generalizability of CSC-SQL. On the BIRD private test set, our 7B model achieves 71.72% execution accuracy, while the 32B model achieves 73.67%.
+
+![CSC-SQL Framework](https://github.com/CycloneBoy/csc_sql/raw/main/data/image/csc_sql_framework.png)
+
+## Main Results
+
+Performance Comparison of different Text-to-SQL methods on BIRD dev and test dataset. On the BIRD private test set, our 7B model achieves 71.72% execution accuracy, while the 32B model achieves 73.67%.
+
+<img src="https://github.com/CycloneBoy/csc_sql/raw/main/data/image/csc_sql_result_main.png" height="500" alt="CSC-SQL Main Results">
+
+## Model Checkpoints
+
+The available models are fine-tuned on Qwen2.5-Coder and are accessible on Hugging Face:
+
+| **Model and Dataset**                  | HuggingFace                                                                                 |
+| :------------------------------------- | :------------------------------------------------------------------------------------------ |
+| CscSQL-Merge-Qwen2.5-Coder-3B-Instruct | [🤗 HuggingFace](https://huggingface.co/cycloneboy/CscSQL-Merge-Qwen2.5-Coder-3B-Instruct)   |
+| CscSQL-Merge-Qwen2.5-Coder-7B-Instruct | [🤗 HuggingFace](https://huggingface.co/cycloneboy/CscSQL-Merge-Qwen2.5-Coder-7B-Instruct)   |
+| CscSQL-Grpo-Qwen2.5-Coder-3B-Instruct  | [🤗 HuggingFace](https://huggingface.co/cycloneboy/CscSQL-Grpo-Qwen2.5-Coder-3B-Instruct)   |
+| CscSQL-Grpo-XiYanSQL-QwenCoder-3B-2502 | [🤗 HuggingFace](https://huggingface.co/cycloneboy/CscSQL-Grpo-XiYanSQL-QwenCoder-3B-2502)  |
+| CscSQL-Grpo-Qwen2.5-Coder-7B-Instruct  | [🤗 HuggingFace](https://huggingface.co/cycloneboy/CscSQL-Grpo-Qwen2.5-Coder-7B-Instruct)   |
+| CscSQL-Grpo-XiYanSQL-QwenCoder-7B-2502 | [🤗 HuggingFace](https://huggingface.co/cycloneboy/CscSQL-Grpo-XiYanSQL-QwenCoder-7B-2502) |
+
+## Quickstart
+
+You can use this model with the `transformers` library. The `config.json` indicates `Qwen2ForCausalLM` as its architecture.
+
+```python
+import torch
+from transformers import AutoTokenizer, AutoModelForCausalLM, GenerationConfig
+
+model_name = "cycloneboy/CscSQL-Grpo-Qwen2.5-Coder-7B-Instruct"
+
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForCausalLM.from_pretrained(
+    model_name,
+    torch_dtype=torch.bfloat16,
+    device_map="auto"
+)
+model.eval()
+
+# Example usage for text-to-SQL generation
+question = "List the names of all employees."
+db_schema = "CREATE TABLE employees (id INT, name TEXT, salary INT);"
+prompt = f"Question: {question}
+Schema: {db_schema}
+SQL: "
+
+inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
+
+generation_config = GenerationConfig(
+    max_new_tokens=128,
+    do_sample=True,
+    temperature=0.7,
+    top_k=20,
+    top_p=0.8,
+    repetition_penalty=1.05,
+    eos_token_id=tokenizer.eos_token_id,
+    pad_token_id=tokenizer.pad_token_id,
+)
+
+with torch.no_grad():
+    outputs = model.generate(
+        **inputs,
+        generation_config=generation_config
+    )
+
+response = tokenizer.decode(outputs[0][inputs.input_ids.shape[1]:], skip_special_tokens=True)
+print(f"Generated SQL: {response}")
+```
+
+## Citation
+
+If you find our work helpful or inspiring, please feel free to cite our paper:
+
+```bibtex
+@misc{sheng2025cscsqlcorrectiveselfconsistencytexttosql,
+      title={CSC-SQL: Corrective Self-Consistency in Text-to-SQL via Reinforcement Learning}, 
+      author={Lei Sheng and Shuai-Shuai Xu},
+      year={2025},
+      eprint={2505.13271},
+      archivePrefix={arXiv},
+      primaryClass={cs.CL},
+      url={https://arxiv.org/abs/2505.13271}, 
+}
+```