---
base_model:
- meta-llama/Llama-3.2-1B-Instruct
language:
- en
- zh
license: cc-by-nc-nd-4.0
tags:
- instruction-finetuning
inference: false
pipeline_tag: text-generation
library_name: transformers
datasets:
- IAAR-Shanghai/VAR
---

<h1 align="center">
🔍 xVerify-1B-I
</h1>

<p align="center">
  <div style="display: flex; justify-content: center; gap: 10px;">
    <a href="https://github.com/IAAR-Shanghai/xVerify">
      <img src="https://img.shields.io/badge/GitHub-Repository-blue?logo=github" alt="GitHub"/>
    </a>
    <a href="https://huggingface.co/IAAR-Shanghai/xVerify-1B-I">
      <img src="https://img.shields.io/badge/🤗%20Hugging%20Face-xVerify--1B--I-yellow" alt="Hugging Face"/>
    </a>
    <a href="https://huggingface.co/papers/2504.10481">
      <img src="https://img.shields.io/badge/Paper-arXiv-red?logo=arxiv" alt="Paper"/>
    </a>
  </div>
</p>

xVerify is an evaluation tool fine-tuned from a pre-trained large language model, designed specifically for objective questions with a single correct answer. It accurately extracts the final answer from lengthy reasoning processes and efficiently identifies equivalence across different forms of expressions.

The model was presented in the paper [xVerify: Efficient Answer Verifier for Reasoning Model Evaluations](https://huggingface.co/papers/2504.10481).

---

## ✨ Key Features

### 📊 Broad Applicability
Suitable for various objective question evaluation scenarios including math problems, multiple-choice questions, classification tasks, and short-answer questions.

### ⛓️ Handles Long Reasoning Chains
Effectively processes answers with extensive reasoning steps to extract the final answer, regardless of complexity.

### 🌐 Multilingual Support
Primarily handles Chinese and English responses while remaining compatible with other languages.

### 🔄 Powerful Equivalence Judgment
- ✓ Recognizes basic transformations like letter case changes and Greek letter conversions
- ✓ Identifies equivalent mathematical expressions across formats (LaTeX, fractions, scientific notation)
- ✓ Determines semantic equivalence in natural language answers
- ✓ Matches multiple-choice responses by content rather than just option identifiers

---

## 🚀 Sample Usage

According to the [official repository](https://github.com/IAAR-Shanghai/xVerify), you can use the model for evaluation as follows:

```python
# Single sample evaluation test
from src.xVerify.model import Model
from src.xVerify.eval import Evaluator

# initialization
model_name = 'xVerify-1B-I'  # Model name
url = 'IAAR-Shanghai/xVerify-1B-I'  # Model path or URL
inference_mode = 'local'  # Inference mode, 'local' or 'api'
api_key = None  # API key used to access the model via API, if not available, set to None

model = Model(
    model_name=model_name,
    model_path_or_url=url,
    inference_mode=inference_mode,
    api_key=api_key
)
evaluator = Evaluator(model=model)

# input evaluation information
question = "New steel giant includes Lackawanna site A major change is coming to the global steel industry and a galvanized mill in Lackawanna that formerly belonged to Bethlehem Steel Corp.
Classify the topic of the above sentence as World, Sports, Business, or Sci/Tech."
llm_output = "The answer is Business."
correct_answer = "Business"

# evaluation
result = evaluator.single_evaluate(
    question=question,
    llm_output=llm_output,
    correct_answer=correct_answer
)
print(result)
```

---

## 📚 Citation

```bibtex
@article{xVerify,
      title={xVerify: Efficient Answer Verifier for Reasoning Model Evaluations}, 
      author={Ding Chen and Qingchen Yu and Pengyuan Wang and Wentao Zhang and Bo Tang and Feiyu Xiong and Xinchi Li and Minchuan Yang and Zhiyu Li},
      journal={arXiv preprint arXiv:2504.10481},
      year={2025},
}
```