mav23/chinese-text-correction-1.5b-GGUF

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

训练数据集

中文纠错数据集

• 数据：shibing624/chinese_text_correction

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

Citation

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