---
license: cc-by-sa-4.0
---

# Compare-Answer Model

Welcome to the repository for the Compare-Answer Model, an innovative tool designed to enhance the accuracy and efficiency of mathematical answer comparison tasks. This model leverages advanced techniques to provide precise comparisons across a wide range of mathematical problems.

## Features

- **High Accuracy**: Utilizes state-of-the-art technology to ensure high reliability in answer comparison.
- **Broad Compatibility**: Supports a variety of mathematical problem types and formats.
- **Easy Integration**: Designed for easy integration with existing systems and workflows.

## Installation

To get started with the Compare-Answer Model, clone this repository and load model with Transformers.

# Quick Start
To use the model, import it and call the main comparison function with the required parameters:
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
device = "cuda"  # the device to load the model onto
model = AutoModelForCausalLM.from_pretrained(
    model_path, torch_dtype="auto", device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained(model_path)

def build_user_query(question, pred_answer, answer, base_prompt):
    input_text = base_prompt.replace("{{question}}", question)
    input_text = input_text.replace("{{pred_step}}", pred_answer)
    input_text = input_text.replace("{{answer}}", answer)
    input_text = input_text.replace("{{analysis}}", "") # default set analysis to blank, if exist, you can pass in the corresponding parameter.
    return input_text

chat_prompt = """<|im_start|>system
You are a helpful assistant.<|im_end|>
<|im_start|>human
{}<|im_end|>
<|im_start|>gpt
"""

basic_prompt = """## 任务描述\n \n你是一个数学老师，学生提交了题目的解题步骤，你需要参考`题干`，`解析`和`答案`，判断`学生解题步骤`的结果是否正确。忽略`学生解题步骤`中的错误，只关注最后的答案。答案可能出现在`解析`中，也可能出现在`答案`中。\n \n## 输入内容\n \n题干:\n \n```\n{{question}}\n```\n \n解析:\n \n```\n{{analysis}}\n \n```\n \n答案:\n \n```\n{{answer}}\n```\n \n学生解题步骤:\n \n```\n{{pred_step}}\n```\n \n输出:"""

base_prompt = chat_prompt.format(basic_prompt)

def build_user_query(question, pred_answer, answer, base_prompt):
    input_text = base_prompt.replace("{{question}}", question)
    input_text = input_text.replace("{{pred_step}}", pred_answer)
    input_text = input_text.replace("{{answer}}", answer)
    input_text = input_text.replace("{{analysis}}", "") # default set analysis to blank, if exist, you can pass in the corresponding parameter.
    return input_text

prompt = build_user_query("1+1=", "3", "2", base_prompt)

model_inputs = tokenizer([prompt], return_tensors="pt").to(device)

generated_ids = model.generate(model_inputs.input_ids, temperature=0, max_new_tokens=16, eos_token_id=100005)
generated_ids = [
    output_ids[len(input_ids) :]
    for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]

response = tokenizer.batch_decode(generated_ids, skip_special_tokens=False)[0]
```

## Documentation
For more detailed information about the model's API and functionalities, please contact us.

# Contributing
Contributions to the Compare-Answer Model are welcome! If you have suggestions or improvements, please fork the repository and submit a pull request.

# License
This project is licensed under the MIT License - see the LICENSE.md file for details.

# Acknowledgements
Thanks to all contributors who have helped in developing this model.
Special thanks to MathEval for providing the datasets and challenges that inspired this project.

# Contact
For any inquiries, please reach out via email at liutianqiao1@tal.com or open an issue in this repository.

Thank you for using or contributing to the Compare-Answer Model!