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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