---
language:
- zh
tags:
- chatglm
- pytorch
- Text-Generation
license: apache-2.0
widget:
- text: |-
    对下面中文拼写纠错：
    少先队员因该为老人让坐。
    答：
base_model: THUDM/chatglm3-6b
pipeline_tag: text-generation
library_name: peft
inference: false
---

# Chinese Spelling Correction LoRA Model
ChatGLM3-6B中文纠错LoRA模型

`shibing624/chatglm3-6b-csc-chinese-lora` evaluate test data：

The overall performance of shibing624/chatglm3-6b-csc-chinese-lora on CSC **test**:

|input_text|pred|
|:--- |:--- |
|对下面文本纠错：少先队员因该为老人让坐。|少先队员应该为老人让座。|

在CSC测试集上生成结果纠错准确率高，由于是基于[THUDM/chatglm3-6b](https://huggingface.co/THUDM/chatglm3-6b)模型，结果常常能带给人惊喜，不仅能纠错，还带有句子润色和改写功能。


## Usage

本项目开源在 pycorrector 项目：[pycorrector](https://github.com/shibing624/pycorrector)，可支持ChatGLM原生模型和LoRA微调后的模型，通过如下命令调用：

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

```python
from pycorrector import GptCorrector
model = GptCorrector("THUDM/chatglm3-6b", "chatglm", peft_name="shibing624/chatglm3-6b-csc-chinese-lora")
r = model.correct_batch(["少先队员因该为老人让坐。"])
print(r) # ['少先队员应该为老人让座。']
```

## 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
import os

import torch
from peft import PeftModel
from transformers import AutoTokenizer, AutoModel

os.environ["KMP_DUPLICATE_LIB_OK"] = "TRUE"
tokenizer = AutoTokenizer.from_pretrained("THUDM/chatglm3-6b", trust_remote_code=True)
model = AutoModel.from_pretrained("THUDM/chatglm3-6b", trust_remote_code=True).half().cuda()
model = PeftModel.from_pretrained(model, "shibing624/chatglm3-6b-csc-chinese-lora")

sents = ['对下面文本纠错\n\n少先队员因该为老人让坐。',
         '对下面文本纠错\n\n下个星期，我跟我朋唷打算去法国玩儿。']


def get_prompt(user_query):
    vicuna_prompt = "A chat between a curious user and an artificial intelligence assistant. " \
                    "The assistant gives helpful, detailed, and polite answers to the user's questions. " \
                    "USER: {query} ASSISTANT:"
    return vicuna_prompt.format(query=user_query)


for s in sents:
    q = get_prompt(s)
    input_ids = tokenizer(q).input_ids
    generation_kwargs = dict(max_new_tokens=128, do_sample=True, temperature=0.8)
    outputs = model.generate(input_ids=torch.as_tensor([input_ids]).to('cuda:0'), **generation_kwargs)
    output_tensor = outputs[0][len(input_ids):]
    response = tokenizer.decode(output_tensor, skip_special_tokens=True)
    print(response)
```

output:
```shell
少先队员应该为老人让座。
下个星期，我跟我朋友打算去法国玩儿。
```


模型文件组成：
```
chatglm3-6b-csc-chinese-lora
    ├── adapter_config.json
    └── adapter_model.bin
```

#### 训练参数：

![loss](train_loss.png)

- num_epochs: 5
- per_device_train_batch_size: 6
- learning_rate: 2e-05
- best steps: 25100
- train_loss: 0.0834
- lr_scheduler_type: linear
- base model: THUDM/chatglm3-6b
- warmup_steps: 50
- "save_strategy": "steps"
- "save_steps": 500
- "save_total_limit": 10
- "bf16": false
- "fp16": true
- "optim": "adamw_torch"
- "ddp_find_unused_parameters": false
- "gradient_checkpointing": true
- max_seq_length: 512
- max_length: 512
- prompt_template_name: vicuna
- 6 * V100 32GB, training 48 hours

### 训练数据集
训练集包括以下数据：

- 中文拼写纠错数据集：https://huggingface.co/datasets/shibing624/CSC
- 中文语法纠错数据集：https://github.com/shibing624/pycorrector/tree/llm/examples/data/grammar
- 通用GPT4问答数据集：https://huggingface.co/datasets/shibing624/sharegpt_gpt4


如果需要训练文本纠错模型，请参考[https://github.com/shibing624/pycorrector](https://github.com/shibing624/pycorrector)



## Citation

```latex
@software{pycorrector,
  author = {Ming Xu},
  title = {pycorrector: Text Error Correction Tool},
  year = {2023},
  url = {https://github.com/shibing624/pycorrector},
}
```