--- library_name: transformers base_model: Qwen/Qwen2.5-1.5B-Instruct license: apache-2.0 datasets: - shibing624/chinese_text_correction language: - zh metrics: - f1 tags: - text-generation-inference widget: - text: "文本纠错：\n少先队员因该为老人让坐。" --- # 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](https://huggingface.co/shibing624/chinese-text-correction-1.5b) | | chinese-text-correction-1.5b-lora | Qwen/Qwen2.5-1.5B-Instruct | [🤗 Hugging Face](https://huggingface.co/shibing624/chinese-text-correction-1.5b-lora) | | chinese-text-correction-7b | Qwen/Qwen2.5-7B-Instruct | [🤗 Hugging Face](https://huggingface.co/shibing624/chinese-text-correction-7b) | | chinese-text-correction-7b-lora | Qwen/Qwen2.5-7B-Instruct | [🤗 Hugging Face](https://huggingface.co/shibing624/chinese-text-correction-7b-lora) | ### 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](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 | ## Usage (pycorrector) 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： Install package: ```shell pip install -U pycorrector ``` ```python 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](https://github.com/shibing624/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 ``` ```python # 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: ```shell 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](https://huggingface.co/datasets/shibing624/chinese_text_correction) - train time: 9 days 8 hours - eval_loss: ![](https://huggingface.co/shibing624/chinese-text-correction-1.5b-lora/resolve/main/eval_loss_1.5b.png) - train_loss: ![](https://huggingface.co/shibing624/chinese-text-correction-1.5b-lora/resolve/main/train_loss_1.5b.png) ### 训练数据集 #### 中文纠错数据集 - 数据：[shibing624/chinese_text_correction](https://huggingface.co/datasets/shibing624/chinese_text_correction) 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) ## Citation ```latex @software{pycorrector, author = {Xu Ming}, title = {pycorrector: Implementation of language model finetune}, year = {2024}, url = {https://github.com/shibing624/pycorrector}, } ```