README.md

---

library_name: transformers
base_model: Qwen/Qwen2.5-1.5B-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。"

---

[![QuantFactory Banner](https://lh7-rt.googleusercontent.com/docsz/AD_4nXeiuCm7c8lEwEJuRey9kiVZsRn2W-b4pWlu3-X534V3YmVuVc2ZL-NXg2RkzSOOS2JXGHutDuyyNAUtdJI65jGTo8jT9Y99tMi4H4MqL44Uc5QKG77B0d6-JfIkZHFaUA71-RtjyYZWVIhqsNZcx8-OMaA?key=xt3VSDoCbmTY7o-cwwOFwQ)](https://hf.co/QuantFactory)


# QuantFactory/chinese-text-correction-1.5b-GGUF
This is quantized version of [shibing624/chinese-text-correction-1.5b](https://huggingface.co/shibing624/chinese-text-correction-1.5b) created using llama.cpp

# Original Model Card




# Chinese Text Correction Model
中文文本纠错模型chinese-text-correction-1.5b：用于拼写纠错、语法纠错

`shibing624/chinese-text-correction-1.5b` 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)  |


### 评估结果
- 评估指标：F1
- CSC(Chinese Spelling Correction): 拼写纠错模型，表示模型可以处理音似、形似、语法等长度对齐的错误纠正
- CTC(CHinese Text Correction): 文本纠错模型，表示模型支持拼写、语法等长度对齐的错误纠正，还可以处理多字、少字等长度不对齐的错误纠正
- GPU：Tesla V100，显存 32 GB

| Model Name       | Model Link                                                                                                              | Base Model                 | Avg        | SIGHAN-2015 | EC-LAW | MCSC   | GPU/CPU | QPS     |
|:-----------------|:------------------------------------------------------------------------------------------------------------------------|:---------------------------|:-----------|:------------|:-------|:-------|:--------|:--------|
| Kenlm-CSC        | [shibing624/chinese-kenlm-klm](https://huggingface.co/shibing624/chinese-kenlm-klm)                                     | kenlm | 0.3409     | 0.3147      | 0.3763 | 0.3317 | CPU     | 9       |
| Mengzi-T5-CSC    | [shibing624/mengzi-t5-base-chinese-correction](https://huggingface.co/shibing624/mengzi-t5-base-chinese-correction)     | mengzi-t5-base | 0.3984     | 0.7758      | 0.3156 | 0.1039 | GPU     | 214     |
| ERNIE-CSC        | [PaddleNLP/ernie-csc](https://github.com/PaddlePaddle/PaddleNLP/tree/develop/legacy/examples/text_correction/ernie-csc) | PaddlePaddle/ernie-1.0-base-zh | 0.4353     | 0.8383      | 0.3357 | 0.1318 | GPU     | 114     |
| MacBERT-CSC      | [shibing624/macbert4csc-base-chinese](https://huggingface.co/shibing624/macbert4csc-base-chinese)                       | hfl/chinese-macbert-base   | 0.3993     | 0.8314      | 0.1610 | 0.2055 | GPU     | **224** |
| ChatGLM3-6B-CSC  | [shibing624/chatglm3-6b-csc-chinese-lora](https://huggingface.co/shibing624/chatglm3-6b-csc-chinese-lora)               | THUDM/chatglm3-6b          | 0.4538     | 0.6572      | 0.4369     | 0.2672      | GPU     | 3       |
| Qwen2.5-1.5B-CTC | [shibing624/chinese-text-correction-1.5b](https://huggingface.co/shibing624/chinese-text-correction-1.5b)               | Qwen/Qwen2.5-1.5B-Instruct | 0.6802     | 0.3032      | 0.7846 | 0.9529 | GPU     | 6       |
| Qwen2.5-7B-CTC   | [shibing624/chinese-text-correction-7b](https://huggingface.co/shibing624/chinese-text-correction-7b)                   | Qwen/Qwen2.5-7B-Instruct   | **0.8225** | 0.4917      | 0.9798 | 0.9959 | GPU     | 3       |

## 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-1.5b")

    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-1.5b"

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-1.5b
|-- added_tokens.json
|-- config.json
|-- generation_config.json
|-- merges.txt
|-- model.safetensors
|-- model.safetensors.index.json
|-- README.md
|-- special_tokens_map.json
|-- tokenizer_config.json
|-- tokenizer.json
`-- vocab.json
```

#### 训练参数：

- num_epochs: 8
- batch_size: 4
- steps: 36000
- eval_loss: 0.14
- base model: Qwen/Qwen2.5-1.5B-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-1.5b-lora/resolve/main/eval_loss_1.5b.png)
- train_loss: ![](https://huggingface.co/shibing624/chinese-text-correction-1.5b-lora/resolve/main/train_loss_1.5b.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)

## Citation

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