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Chinese Text Correction Model

中文文本纠错模型chinese-text-correction-1.5b-lora:用于拼写纠错、语法纠错

shibing624/chinese-text-correction-1.5b-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
chinese-text-correction-1.5b-lora Qwen/Qwen2.5-1.5B-Instruct 🤗 Hugging Face
chinese-text-correction-7b Qwen/Qwen2.5-7B-Instruct 🤗 Hugging Face
chinese-text-correction-7b-lora Qwen/Qwen2.5-7B-Instruct 🤗 Hugging Face

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 kenlm 0.3409 0.3147 0.3763 0.3317 CPU 9
Mengzi-T5-CSC shibing624/mengzi-t5-base-chinese-correction mengzi-t5-base 0.3984 0.7758 0.3156 0.1039 GPU 214
ERNIE-CSC PaddleNLP/ernie-csc PaddlePaddle/ernie-1.0-base-zh 0.4353 0.8383 0.3357 0.1318 GPU 114
MacBERT-CSC 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 THUDM/chatglm3-6b 0.4538 0.6572 0.4369 0.2672 GPU 3
Qwen2.5-1.5B-CTC 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 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,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:

pip install -U pycorrector
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, 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 
# 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:

少先队员应该为老人让座。

模型文件组成:

shibing624/chinese-text-correction-1.5b-lora
    ├── adapter_config.json
    └── adapter_model.safetensors

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
  • train time: 9 days 8 hours
  • eval_loss:
  • train_loss:

训练数据集

中文纠错数据集

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

Framework versions

  • PEFT 0.11.1

Citation

@software{pycorrector,
  author = {Xu Ming},
  title = {pycorrector: Implementation of language model finetune},
  year = {2024},
  url = {https://github.com/shibing624/pycorrector},
}
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Dataset used to train shibing624/chinese-text-correction-1.5b-lora