commit 72238b54a6ef3ceb3a4d62b521e4b1c0e4e83641 Author: ModelHub XC Date: Thu Apr 16 16:38:46 2026 +0800 初始化项目，由ModelHub XC社区提供模型 Model: RichardErkhov/shibing624_-_chinese-text-correction-1.5b-gguf Source: Original Platform diff --git a/.gitattributes b/.gitattributes new file mode 100644 index 0000000..45552a4 --- /dev/null +++ b/.gitattributes @@ -0,0 +1,54 @@ +*.7z filter=lfs diff=lfs merge=lfs -text +*.arrow filter=lfs diff=lfs merge=lfs -text +*.bin filter=lfs diff=lfs merge=lfs -text +*.bz2 filter=lfs diff=lfs merge=lfs -text +*.ckpt filter=lfs diff=lfs merge=lfs -text +*.ftz filter=lfs diff=lfs merge=lfs -text +*.gz filter=lfs diff=lfs merge=lfs -text +*.h5 filter=lfs diff=lfs merge=lfs -text +*.joblib filter=lfs diff=lfs merge=lfs -text +*.lfs.* filter=lfs diff=lfs merge=lfs -text +*.mlmodel filter=lfs diff=lfs merge=lfs -text +*.model filter=lfs diff=lfs merge=lfs -text +*.msgpack filter=lfs diff=lfs merge=lfs -text +*.npy filter=lfs diff=lfs merge=lfs -text +*.npz filter=lfs 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-text +chinese-text-correction-1.5b.Q5_K.gguf filter=lfs diff=lfs merge=lfs -text +chinese-text-correction-1.5b.Q5_K_M.gguf filter=lfs diff=lfs merge=lfs -text +chinese-text-correction-1.5b.Q5_1.gguf filter=lfs diff=lfs merge=lfs -text +chinese-text-correction-1.5b.Q6_K.gguf filter=lfs diff=lfs merge=lfs -text +chinese-text-correction-1.5b.Q8_0.gguf filter=lfs diff=lfs merge=lfs -text diff --git a/README.md b/README.md new file mode 100644 index 0000000..9fb3e19 --- /dev/null +++ b/README.md @@ -0,0 +1,210 @@ +Quantization made by Richard Erkhov. + +[Github](https://github.com/RichardErkhov) + +[Discord](https://discord.gg/pvy7H8DZMG) + +[Request more models](https://github.com/RichardErkhov/quant_request) + + +chinese-text-correction-1.5b - GGUF +- Model creator: https://huggingface.co/shibing624/ +- Original model: https://huggingface.co/shibing624/chinese-text-correction-1.5b/ + + +| Name | Quant method | Size | +| ---- | ---- | ---- | +| [chinese-text-correction-1.5b.Q2_K.gguf](https://huggingface.co/RichardErkhov/shibing624_-_chinese-text-correction-1.5b-gguf/blob/main/chinese-text-correction-1.5b.Q2_K.gguf) | Q2_K | 0.63GB | +| [chinese-text-correction-1.5b.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/shibing624_-_chinese-text-correction-1.5b-gguf/blob/main/chinese-text-correction-1.5b.Q3_K_S.gguf) | Q3_K_S | 0.71GB | +| [chinese-text-correction-1.5b.Q3_K.gguf](https://huggingface.co/RichardErkhov/shibing624_-_chinese-text-correction-1.5b-gguf/blob/main/chinese-text-correction-1.5b.Q3_K.gguf) | Q3_K | 0.77GB | +| [chinese-text-correction-1.5b.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/shibing624_-_chinese-text-correction-1.5b-gguf/blob/main/chinese-text-correction-1.5b.Q3_K_M.gguf) | Q3_K_M | 0.77GB | +| [chinese-text-correction-1.5b.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/shibing624_-_chinese-text-correction-1.5b-gguf/blob/main/chinese-text-correction-1.5b.Q3_K_L.gguf) | Q3_K_L | 0.82GB | +| [chinese-text-correction-1.5b.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/shibing624_-_chinese-text-correction-1.5b-gguf/blob/main/chinese-text-correction-1.5b.IQ4_XS.gguf) | IQ4_XS | 0.84GB | +| [chinese-text-correction-1.5b.Q4_0.gguf](https://huggingface.co/RichardErkhov/shibing624_-_chinese-text-correction-1.5b-gguf/blob/main/chinese-text-correction-1.5b.Q4_0.gguf) | Q4_0 | 0.87GB | +| [chinese-text-correction-1.5b.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/shibing624_-_chinese-text-correction-1.5b-gguf/blob/main/chinese-text-correction-1.5b.IQ4_NL.gguf) | IQ4_NL | 0.88GB | +| [chinese-text-correction-1.5b.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/shibing624_-_chinese-text-correction-1.5b-gguf/blob/main/chinese-text-correction-1.5b.Q4_K_S.gguf) | Q4_K_S | 0.88GB | +| [chinese-text-correction-1.5b.Q4_K.gguf](https://huggingface.co/RichardErkhov/shibing624_-_chinese-text-correction-1.5b-gguf/blob/main/chinese-text-correction-1.5b.Q4_K.gguf) | Q4_K | 0.92GB | +| [chinese-text-correction-1.5b.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/shibing624_-_chinese-text-correction-1.5b-gguf/blob/main/chinese-text-correction-1.5b.Q4_K_M.gguf) | Q4_K_M | 0.92GB | +| [chinese-text-correction-1.5b.Q4_1.gguf](https://huggingface.co/RichardErkhov/shibing624_-_chinese-text-correction-1.5b-gguf/blob/main/chinese-text-correction-1.5b.Q4_1.gguf) | Q4_1 | 0.95GB | +| [chinese-text-correction-1.5b.Q5_0.gguf](https://huggingface.co/RichardErkhov/shibing624_-_chinese-text-correction-1.5b-gguf/blob/main/chinese-text-correction-1.5b.Q5_0.gguf) | Q5_0 | 1.02GB | +| [chinese-text-correction-1.5b.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/shibing624_-_chinese-text-correction-1.5b-gguf/blob/main/chinese-text-correction-1.5b.Q5_K_S.gguf) | Q5_K_S | 1.02GB | +| [chinese-text-correction-1.5b.Q5_K.gguf](https://huggingface.co/RichardErkhov/shibing624_-_chinese-text-correction-1.5b-gguf/blob/main/chinese-text-correction-1.5b.Q5_K.gguf) | Q5_K | 1.05GB | +| [chinese-text-correction-1.5b.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/shibing624_-_chinese-text-correction-1.5b-gguf/blob/main/chinese-text-correction-1.5b.Q5_K_M.gguf) | Q5_K_M | 1.05GB | +| [chinese-text-correction-1.5b.Q5_1.gguf](https://huggingface.co/RichardErkhov/shibing624_-_chinese-text-correction-1.5b-gguf/blob/main/chinese-text-correction-1.5b.Q5_1.gguf) | Q5_1 | 1.1GB | +| [chinese-text-correction-1.5b.Q6_K.gguf](https://huggingface.co/RichardErkhov/shibing624_-_chinese-text-correction-1.5b-gguf/blob/main/chinese-text-correction-1.5b.Q6_K.gguf) | Q6_K | 1.19GB | +| [chinese-text-correction-1.5b.Q8_0.gguf](https://huggingface.co/RichardErkhov/shibing624_-_chinese-text-correction-1.5b-gguf/blob/main/chinese-text-correction-1.5b.Q8_0.gguf) | Q8_0 | 1.53GB | + + + + +Original model description: +--- +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}, +} +``` + + + diff --git a/chinese-text-correction-1.5b.IQ4_NL.gguf b/chinese-text-correction-1.5b.IQ4_NL.gguf new file mode 100644 index 0000000..2b1a726 --- /dev/null +++ b/chinese-text-correction-1.5b.IQ4_NL.gguf @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:6091fef362c3a5dac1c17cc8c4995ffdd3f1d21d98144d334de2d2448b557197 +size 941492256 diff --git a/chinese-text-correction-1.5b.IQ4_XS.gguf b/chinese-text-correction-1.5b.IQ4_XS.gguf new file mode 100644 index 0000000..0a1ee15 --- /dev/null +++ b/chinese-text-correction-1.5b.IQ4_XS.gguf @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:ee3eb5a965f4ff2154c08ba75d2724a6db0294b5ef37912569ff1fe88ddee9c8 +size 902182944 diff --git a/chinese-text-correction-1.5b.Q2_K.gguf b/chinese-text-correction-1.5b.Q2_K.gguf new file mode 100644 index 0000000..65bd712 --- /dev/null +++ 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