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

- Add comprehensive model card for CSC-SQL model (0a0d80548f8f5c165ac902f2ad76447c69679e8a)


Co-authored-by: Niels Rogge <[email protected]>

README.md

@@ -0,0 +1,103 @@
+---
+pipeline_tag: text-generation
+library_name: transformers
+license: apache-2.0
+tags:
+  - text-to-sql
+  - sql
+  - reinforcement-learning
+  - qwen2
+---
+
+# CSC-SQL: Corrective Self-Consistency in Text-to-SQL via Reinforcement Learning
+
+This repository contains the model presented in the paper [CSC-SQL: Corrective Self-Consistency in Text-to-SQL via Reinforcement Learning](https://huggingface.co/papers/2505.13271).
+
+**Code Repository**: [https://github.com/CycloneBoy/csc_sql](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.
+![CSC-SQL Main Results](https://github.com/CycloneBoy/csc_sql/raw/main/data/image/csc_sql_result_main.png)
+
+## Model Checkpoints
+
+The models and datasets related to CSC-SQL are available on Hugging Face and ModelScope:
+
+| **Model and Dataset** | Modelscope | HuggingFace |
+| :------------------------------------ | :---------------------------------------------------------------------------------------------- | :------------------------------------------------------------------------------------------ |
+| bird train and dev dataset | [🤖 Modelscope](https://modelscope.cn/datasets/cycloneboy/bird_train) | [🤗 HuggingFace](https://huggingface.co/datasets/cycloneboy/bird_train) |
+| CscSQL-Merge-Qwen2.5-Coder-3B-Instruct | [🤖 Modelscope](https://modelscope.cn/models/cycloneboy/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 | [🤖 Modelscope](https://modelscope.cn/models/cycloneboy/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 | [🤖 Modelscope](https://modelscope.cn/models/cycloneboy/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 | [🤖 Modelscope](https://modelscope.cn/models/cycloneboy/CscSQL-Grpo-XiYanSQL-QwenCoder-3B-2502) | [🤗 HuggingFace](https://huggingface.co/cycloneboy/CscSQL-Grpo-XiYanSQL-QwenCoder-3B-2502) |
+| CscSQL-Grpo-Qwen2.5-Coder-7B-Instruct | [🤖 Modelscope](https://modelscope.cn/models/cycloneboy/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 | [🤖 Modelscope](https://modelscope.cn/models/cycloneboy/CscSQL-Grpo-XiYanSQL-QwenCoder-7B-2502) | [🤗 HuggingFace](https://huggingface.co/cycloneboy/CscSQL-Grpo-XiYanSQL-QwenCoder-7B-2502) |
+
+## Usage
+
+This model can be loaded and used with the Hugging Face `transformers` library. Below is a simple example for text-to-SQL inference. For more advanced usage, including data processing, training, and evaluation scripts, please refer to the [official GitHub repository](https://github.com/CycloneBoy/csc_sql).
+
+```python
+import torch
+from transformers import AutoTokenizer, AutoModelForCausalLM, GenerationConfig
+
+# The specific model identifier for this repository
+model_id = "cycloneboy/CscSQL-Grpo-Qwen2.5-Coder-7B-Instruct" # Replace with the actual model ID if different
+
+tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)
+model = AutoModelForCausalLM.from_pretrained(model_id, device_map='auto', torch_dtype=torch.bfloat16, trust_remote_code=True)
+model.eval()
+
+# Example: Text-to-SQL inference using the Qwen2 chat template
+# For a real-world text-to-SQL task, you would typically need to provide the database schema or
+# context relevant to the query as part of the prompt.
+question = "What are the names of all employees who work in the 'Sales' department?"
+messages = [
+    {"role": "system", "content": "You are a helpful assistant trained to convert natural language questions into SQL queries."},
+    {"role": "user", "content": f"Translate the following natural language query into SQL: '{question}'"},
+]
+
+# Apply the chat template to format the input according to Qwen2's conventions
+text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
+model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
+
+# Define generation parameters
+generation_config = GenerationConfig(
+    max_new_tokens=256,
+    do_sample=False, # Use greedy decoding for reproducible results
+    temperature=0.7,
+    top_p=0.9,
+    eos_token_id=tokenizer.eos_token_id,
+    pad_token_id=tokenizer.pad_token_id,
+)
+
+with torch.no_grad():
+    output_ids = model.generate(model_inputs.input_ids, generation_config=generation_config)
+
+# Decode the generated SQL query, skipping special tokens
+generated_sql = tokenizer.decode(output_ids[0][len(model_inputs.input_ids[0]):], skip_special_tokens=True)
+print(f"Generated SQL: {generated_sql}")
+```
+
+## Citation
+
+If you find our work helpful or inspiring, please feel free to cite it:
+
+```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}, 
+}
+```