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README.md
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---
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library_name: transformers
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base_model: Qwen/Qwen2.5-7B-Instruct
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license: apache-2.0
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datasets:
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- shibing624/chinese_text_correction
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language:
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---
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library_name: transformers
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base_model: Qwen/Qwen2.5-7B-Instruct
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license: apache-2.0
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datasets:
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- shibing624/chinese_text_correction
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language:
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- zho
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- eng
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- fra
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- spa
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- por
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- deu
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- ita
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- rus
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- jpn
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- kor
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- vie
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- tha
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- ara
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metrics:
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- f1
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tags:
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- text-generation-inference
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widget:
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- text: '文本纠错：
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少先队员因该为老人让坐。'
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---
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# Chinese Text Correction Model
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中文文本纠错模型chinese-text-correction-7b：用于拼写纠错、语法纠错
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`shibing624/chinese-text-correction-7b` evaluate test data：
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The overall performance of CSC **test**:
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|input_text|predict_text|
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|:--- |:--- |
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|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。|
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# Models
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| Name | Base Model | Download |
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|-----------------|-------------------|-----------------------------------------------------------------------|
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| chinese-text-correction-1.5b | Qwen/Qwen2.5-1.5B-Instruct | [🤗 Hugging Face](https://huggingface.co/shibing624/chinese-text-correction-1.5b) |
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| chinese-text-correction-1.5b-lora | Qwen/Qwen2.5-1.5B-Instruct | [🤗 Hugging Face](https://huggingface.co/shibing624/chinese-text-correction-1.5b-lora) |
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| chinese-text-correction-7b | Qwen/Qwen2.5-7B-Instruct | [🤗 Hugging Face](https://huggingface.co/shibing624/chinese-text-correction-7b) |
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| chinese-text-correction-7b-lora | Qwen/Qwen2.5-7B-Instruct | [🤗 Hugging Face](https://huggingface.co/shibing624/chinese-text-correction-7b-lora) |
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### 评估结果
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- 评估指标：F1
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- 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
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- 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
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- GPU：Tesla V100，显存 32 GB
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| Model Name | Model Link | Base Model | Avg | SIGHAN-2015 | EC-LAW | MCSC | GPU/CPU | QPS |
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|:-----------------|:------------------------------------------------------------------------------------------------------------------------|:---------------------------|:-----------|:------------|:-------|:-------|:--------|:--------|
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| Kenlm-CSC | [shibing624/chinese-kenlm-klm](https://huggingface.co/shibing624/chinese-kenlm-klm) | kenlm | 0.3409 | 0.3147 | 0.3763 | 0.3317 | CPU | 9 |
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| 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 |
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| 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 |
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| 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** |
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| 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 |
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| 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 |
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| 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 |
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## Usage (pycorrector)
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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：
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Install package:
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```shell
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pip install -U pycorrector
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```
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```python
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from pycorrector.gpt.gpt_corrector import GptCorrector
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if __name__ == '__main__':
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error_sentences = [
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'真麻烦你了。希望你们好好的跳无',
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'少先队员因该为老人让坐',
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'机七学习是人工智能领遇最能体现智能的一个分知',
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'一只小鱼船浮在平净的河面上',
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'我的家乡是有明的渔米之乡',
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]
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m = GptCorrector("shibing624/chinese-text-correction-7b")
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batch_res = m.correct_batch(error_sentences)
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for i in batch_res:
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print(i)
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print()
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```
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## Usage (HuggingFace Transformers)
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Without [pycorrector](https://github.com/shibing624/pycorrector), you can use the model like this:
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First, you pass your input through the transformer model, then you get the generated sentence.
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Install package:
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```
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pip install transformers
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```
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```python
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# pip install transformers
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from transformers import AutoModelForCausalLM, AutoTokenizer
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checkpoint = "shibing624/chinese-text-correction-7b"
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device = "cuda" # for GPU usage or "cpu" for CPU usage
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tokenizer = AutoTokenizer.from_pretrained(checkpoint)
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model = AutoModelForCausalLM.from_pretrained(checkpoint).to(device)
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input_content = "文本纠错：\n少先队员因该为老人让坐。"
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messages = [{"role": "user", "content": input_content}]
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input_text=tokenizer.apply_chat_template(messages, tokenize=False)
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print(input_text)
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inputs = tokenizer.encode(input_text, return_tensors="pt").to(device)
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outputs = model.generate(inputs, max_new_tokens=1024, temperature=0, do_sample=False, repetition_penalty=1.08)
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print(tokenizer.decode(outputs[0]))
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```
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output:
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```shell
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少先队员应该为老人让座。
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```
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模型文件组成：
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```
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shibing624/chinese-text-correction-7b
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|-- added_tokens.json
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|-- config.json
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|-- generation_config.json
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|-- merges.txt
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|-- model.safetensors
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|-- model.safetensors.index.json
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|-- README.md
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|-- special_tokens_map.json
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|-- tokenizer_config.json
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|-- tokenizer.json
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`-- vocab.json
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```
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#### 训练参数：
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- num_epochs: 8
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- batch_size: 2
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- steps: 36000
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- eval_loss: 0.12
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- base model: Qwen/Qwen2.5-7B-Instruct
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- train data: [shibing624/chinese_text_correction](https://huggingface.co/datasets/shibing624/chinese_text_correction)
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- train time: 10 days
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- eval_loss: 
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- train_loss: 
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### 训练数据集
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#### 中文纠错数据集
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- 数据：[shibing624/chinese_text_correction](https://huggingface.co/datasets/shibing624/chinese_text_correction)
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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)
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## Citation
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```latex
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@software{pycorrector,
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author = {Xu Ming},
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title = {pycorrector: Implementation of language model finetune},
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year = {2024},
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url = {https://github.com/shibing624/pycorrector},
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}
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```
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