README.md

---
library_name: peft
base_model: Qwen/Qwen2.5-7B-Instruct
license: apache-2.0
datasets:
- shibing624/chinese_text_correction
language:
- zh
metrics:
- f1
tags:
- text-generation-inference
widget:
- text: "文本纠错：\n少先队员因该为老人让坐。"
---



# Chinese Text Correction Model
中文文本纠错模型chinese-text-correction-7b-lora：用于拼写纠错、语法纠错

`shibing624/chinese-text-correction-7b-lora` evaluate test data：

The overall performance of CSC **test**:

|input_text|predict_text|
|:--- |:--- |
|文本纠错：\n少先队员因该为老人让坐。|少先队员应该为老人让座。|

# Models

| Name            |  Base Model   | Download                        |
|-----------------|-------------------|-----------------------------------------------------------------------|
| chinese-text-correction-1.5b  |  Qwen/Qwen2.5-1.5B-Instruct             | [🤗 Hugging Face](https://huggingface.co/shibing624/chinese-text-correction-1.5b)  |
| chinese-text-correction-1.5b-lora  |  Qwen/Qwen2.5-1.5B-Instruct             | [🤗 Hugging Face](https://huggingface.co/shibing624/chinese-text-correction-1.5b-lora)  |
| chinese-text-correction-7b   |  Qwen/Qwen2.5-7B-Instruct             | [🤗 Hugging Face](https://huggingface.co/shibing624/chinese-text-correction-7b)  |
| chinese-text-correction-7b-lora  |  Qwen/Qwen2.5-7B-Instruct             | [🤗 Hugging Face](https://huggingface.co/shibing624/chinese-text-correction-7b-lora)  |



## Usage (pycorrector)

本项目开源在`pycorrector`项目：[pycorrector](https://github.com/shibing624/pycorrector)，可支持大模型微调后用于文本纠错，通过如下命令调用：

Install package:
```shell
pip install -U pycorrector
```

```python
from pycorrector.gpt.gpt_corrector import GptCorrector

if __name__ == '__main__':
    error_sentences = [
        '真麻烦你了。希望你们好好的跳无',
        '少先队员因该为老人让坐',
        '机七学习是人工智能领遇最能体现智能的一个分知',
        '一只小鱼船浮在平净的河面上',
        '我的家乡是有明的渔米之乡',
    ]
    m = GptCorrector("shibing624/chinese-text-correction-7b")

    batch_res = m.correct_batch(error_sentences)
    for i in batch_res:
        print(i)
        print()
```

## Usage (HuggingFace Transformers)
Without [pycorrector](https://github.com/shibing624/pycorrector), you can use the model like this: 

First, you pass your input through the transformer model, then you get the generated sentence.

Install package:
```
pip install transformers 
```

```python
# pip install transformers
from transformers import AutoModelForCausalLM, AutoTokenizer
checkpoint = "shibing624/chinese-text-correction-7b"

device = "cuda" # for GPU usage or "cpu" for CPU usage
tokenizer = AutoTokenizer.from_pretrained(checkpoint)
model = AutoModelForCausalLM.from_pretrained(checkpoint).to(device)

input_content = "文本纠错：\n少先队员因该为老人让坐。"

messages = [{"role": "user", "content": input_content}]
input_text=tokenizer.apply_chat_template(messages, tokenize=False)

print(input_text)

inputs = tokenizer.encode(input_text, return_tensors="pt").to(device)
outputs = model.generate(inputs, max_new_tokens=1024, temperature=0, do_sample=False, repetition_penalty=1.08)

print(tokenizer.decode(outputs[0]))
```

output:
```shell
少先队员应该为老人让座。
```


模型文件组成：
```
shibing624/chinese-text-correction-7b-lora
    ├── adapter_config.json
    └── adapter_model.safetensors
```

#### 训练参数：

- num_epochs: 8
- batch_size: 2
- steps: 36000
- eval_loss: 0.12
- base model: Qwen/Qwen2.5-7B-Instruct
- train data: [shibing624/chinese_text_correction](https://huggingface.co/datasets/shibing624/chinese_text_correction)
- train time: 9 days 8 hours
- eval_loss: ![](https://huggingface.co/shibing624/chinese-text-correction-7b-lora/resolve/main/eval_loss_7b.png)
- train_loss: ![](https://huggingface.co/shibing624/chinese-text-correction-7b-lora/resolve/main/train_loss_7b.png)

### 训练数据集
#### 中文纠错数据集

- 数据：[shibing624/chinese_text_correction](https://huggingface.co/datasets/shibing624/chinese_text_correction)

如果需要训练Qwen的纠错模型，请参考[https://github.com/shibing624/pycorrector](https://github.com/shibing624/pycorrector) 或者 [https://github.com/shibing624/MedicalGPT](https://github.com/shibing624/MedicalGPT)

### Framework versions

- PEFT 0.11.1

## Citation

```latex
@software{pycorrector,
  author = {Xu Ming},
  title = {pycorrector: Implementation of language model finetune},
  year = {2024},
  url = {https://github.com/shibing624/pycorrector},
}
```