--- title: ChineseErrorCorrector3-4B canonical_url: "https://www.modelscope.cn/models/tiannlp/ChineseErrorCorrector3-4B" md_url: "https://www.modelscope.cn/models/tiannlp/ChineseErrorCorrector3-4B.md" repository: tiannlp/ChineseErrorCorrector3-4B last_updated: 2025-11-25 license: apache-2.0 pipeline_tag: text-error-correction tasks: - text-error-correction model_type: - qwen3 architectures: - Qwen3ForCausalLM parameters: 4.0B tensor_type: - BF16 library_name: - transformer - safetensors - pytorch frameworks: - Pytorch inference_backends: - "deploy_task text/emb" - "lmdeploy_turbomind 0.9.1" - "sglang 0.5.2" - "vllm 0.9.2" downloads: 78323 stars: 23 --- # ChineseErrorCorrector3-4B > ChineseErrorCorrector3-4B - tiannlp 在 ModelScope 开源的模型。一个面向中文文本纠错任务的综合平台，集学术研究、模型训练、模型评测和推理部署于一体，覆盖拼写纠错与语法纠错两个核心方向。 tiannlp/ChineseErrorCorrector3-4B 是 ModelScope 魔搭社区上的 4.0B 参数text-error-correction模型，采用 apache-2.0 许可，可用 deploy_task text/emb、lmdeploy_turbomind 0.9.1、sglang 0.5.2 部署。 - **Repository**: tiannlp/ChineseErrorCorrector3-4B - **License**: apache-2.0 - **Tasks**: text-error-correction - **Parameters**: 4.0B - **Inference backends**: deploy_task text/emb, lmdeploy_turbomind 0.9.1, sglang 0.5.2, vllm 0.9.2 - **Downloads**: 78323 - **Stars**: 23 - **Last updated**: 2025-11-25 Source: https://www.modelscope.cn/models/tiannlp/ChineseErrorCorrector3-4B --- 一个面向中文文本纠错任务的综合平台，集学术研究、模型训练、模型评测和推理部署于一体，覆盖拼写纠错与语法纠错两个核心方向。 🔥项目地址：https://github.com/TW-NLP/ChineseErrorCorrector ## 模型列表 | 模型名称 | 纠错类型 | 描述 | |:--------------------------------------------------------------------------------------------|:------|:-------------------------------------------| | [twnlp/ChineseErrorCorrector3-4B](https://huggingface.co/twnlp/ChineseErrorCorrector3-4B) | 语法+拼写 | 使用200万纠错数据进行全量训练，适用于语法纠错和拼写纠错，效果最好，推荐使用。 | | [twnlp/ChineseErrorCorrector2-7B](https://huggingface.co/twnlp/ChineseErrorCorrector2-7B) | 语法+拼写 | 使用200万纠错数据进行多轮迭代训练，适用于语法纠错和拼写纠错，效果较好。 | ## 模型评测（NaCGEC Data） | Model Name | Model Link | Base Model | Avg | SIGHAN-2015 | EC-LAW | MCSC | GPU | QPS | |:------------------|:------------------------------------------------------------------------------------------------------------------------|:-------------------------------|:-----------|:------------|:-------|:-------|:--------|:--------| | Kenlm-CSC | [shibing624/chinese-kenlm-klm](https://huggingface.co/shibing624/chinese-kenlm-klm) | kenlm | 0.3409 | 0.3147 | 0.3763 | 0.3317 | CPU | 9 | | 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 | | 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 | | 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** | | 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 | | 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 | | 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 | | **Qwen3-4B-CTC(Our)** | [twnlp/ChineseErrorCorrector3-4B](https://huggingface.co/twnlp/ChineseErrorCorrector3-4B) | Qwen/Qwen3-4B | **0.8521** | 0.6340 | 0.9360 | 0.9864 | GPU | 5 | Without [ChineseErrorCorrector](https://github.com/TW-NLP/ChineseErrorCorrector), 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 from transformers import AutoModelForCausalLM, AutoTokenizer model_name = "twnlp/ChineseErrorCorrector3-4B" model = AutoModelForCausalLM.from_pretrained( model_name, torch_dtype="auto", device_map="auto" ) tokenizer = AutoTokenizer.from_pretrained(model_name) prompt = "你是一个文本纠错专家，纠正输入句子中的语法错误，并输出正确的句子，输入句子为：" text_input = "对待每一项工作都要一丝不够。" messages = [ {"role": "user", "content": prompt + text_input} ] text = tokenizer.apply_chat_template( messages, tokenize=False, add_generation_prompt=True, enable_thinking=False # Switches between thinking and non-thinking modes. Default is True. ) model_inputs = tokenizer([text], return_tensors="pt").to(model.device) generated_ids = model.generate( **model_inputs, max_new_tokens=512 ) generated_ids = [ output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids) ] response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0] print(response) ``` output: ```shell 对待每一项工作都要一丝不苟。 ``` ## Citation If this work is helpful, please kindly cite as: ```bibtex @misc{tian2025chineseerrorcorrector34bstateoftheartchinesespelling, title={ChineseErrorCorrector3-4B: State-of-the-Art Chinese Spelling and Grammar Corrector}, author={Wei Tian and YuhaoZhou}, year={2025}, eprint={2511.17562}, archivePrefix={arXiv}, primaryClass={cs.CL}, url={https://arxiv.org/abs/2511.17562}, }