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

- Add comprehensive model card for CSC-SQL (316a73fb71716c82144143ee197f7510b8fab841)


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
+- sql-generation
+- reinforcement-learning
+- qwen
+---
+
+# CSC-SQL: Corrective Self-Consistency in Text-to-SQL via Reinforcement Learning
+
+The model presented in the paper [CSC-SQL: Corrective Self-Consistency in Text-to-SQL via Reinforcement Learning](https://huggingface.co/papers/2505.13271).
+
+**Abstract:** 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%. The code has been open sourced at this https URL.
+
+**Code:** The code for CSC-SQL is open-sourced at [https://github.com/CycloneBoy/csc_sql](https://github.com/CycloneBoy/csc_sql).
+
+## Introduction
+
+CSC-SQL is a novel method that integrates Self-Consistency and Self-Correction for improved Text-to-SQL generation. It addresses limitations of prior methods by selecting optimal outputs and handling both syntactic and semantic errors. The approach employs Group Relative Policy Optimization (GRPO) to fine-tune SQL generation and revision models, leading to significant enhancements in output quality.
+
+![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_result_main](https://github.com/CycloneBoy/csc_sql/raw/main/data/image/csc_sql_result_main.png)
+
+## Models
+
+A collection of CSC-SQL models can be found on Hugging Face: [CSC-SQL Hugging Face Collection](https://huggingface.co/collections/cycloneboy/csc-sql-6835c4a52da10c54bbe14f8e).
+
+| **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) |
+
+## Dataset
+
+The BIRD training and development datasets used can be found here: [BIRD Train Dataset](https://huggingface.co/datasets/cycloneboy/bird_train).
+
+## Quickstart
+
+This section provides instructions on how to use the pre-trained CSC-SQL models.
+
+```python
+import torch
+from transformers import AutoTokenizer, AutoModelForCausalLM, GenerationConfig
+
+model_dir = "cycloneboy/CscSQL-Grpo-Qwen2.5-Coder-7B-Instruct" # Or other released models
+
+def load_model_tokenizer(model_path):
+    tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)
+    tokenizer.eos_token = "<|im_end|>"
+    tokenizer.pad_token = "<|endoftext|>"
+    tokenizer.eos_token_id = tokenizer.convert_tokens_to_ids(tokenizer.eos_token)
+    tokenizer.pad_token_id = tokenizer.convert_tokens_to_ids(tokenizer.pad_token)
+    tokenizer.padding_side = "left"
+
+    model = AutoModelForCausalLM.from_pretrained(model_path, device_map='auto',torch_dtype=torch.bfloat16, trust_remote_code=True)
+    return model, tokenizer
+
+# Example usage for a natural language question (Text-to-SQL)
+# Make sure your input string ends with "<|im_start|>assistant
+" for generation
+text_list = ["""
+<|im_start|>user
+Your task is to write a SQLite query given a natural language question and a database schema.
+You need to generate the SQL query that answers the question correctly.
+
+For example, to find out the names of all the songs, given:
+CREATE TABLE songs (
+  song_id INTEGER PRIMARY KEY,
+  song_name TEXT
+);
+Question: What are the names of all the songs?
+SQL: SELECT song_name FROM songs
+
+To find the artist of the song 'Yesterday', given:
+CREATE TABLE songs (
+  song_id INTEGER PRIMARY KEY,
+  song_name TEXT,
+  artist_id INTEGER
+);
+CREATE TABLE artists (
+  artist_id INTEGER PRIMARY KEY,
+  artist_name TEXT
+);
+Question: Who is the artist of the song 'Yesterday'?
+SQL: SELECT T2.artist_name FROM songs AS T1 JOIN artists AS T2 ON T1.artist_id = T2.artist_id WHERE T1.song_name = 'Yesterday'
+
+Now, answer the following question.
+Question: How many records are there in the table 'songs'?
+SQL:
+<|im_end|>
+<|im_start|>assistant
+"""]
+
+model, tokenizer = load_model_tokenizer(model_dir)
+inputs = tokenizer(text_list, return_tensors='pt', padding=True, add_special_tokens=False).to('cuda')
+input_ids = inputs["input_ids"]
+attention_mask = inputs["attention_mask"]
+generation_config = GenerationConfig(
+        eos_token_id=tokenizer.eos_token_id,
+        pad_token_id=tokenizer.pad_token_id,
+        temperature=0.1,
+        max_new_tokens=512,
+        num_return_sequences=1,
+        num_beams=1,
+        top_p=0.95,
+        do_sample=False
+)
+outputs = model.generate(
+        inputs= input_ids,
+        attention_mask=attention_mask,
+        **generation_config.to_dict()
+)
+gen_text = tokenizer.batch_decode(outputs[:, input_ids.shape[1]:], skip_special_tokens=True)
+print(gen_text[0])
+
+# Expected output: SELECT count(*) FROM songs
+```
+
+## Citation
+
+If you find our work useful or helpful for your R&D works, please feel free to cite our paper as below.
+
+```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}, 
+}
+```