Macropodus
/

macbert4mdcspell_v2

@@ -2,81 +2,118 @@
 license: apache-2.0
 language:
 - zh
 tags:
 - csc
-- macro-correct
-- pycorrector
 - mdcspell
-- macbert4mdcspell
-- chinese-spelling-correct
 ---
-<p align="center">
-    <img src="tet/images/csc_logo.png" width="480">
-</p>
-# [macro-correct](https://github.com/yongzhuo/macro-correct)
-[![PyPI](https://img.shields.io/pypi/v/macro-correct)](https://pypi.org/project/macro-correct/)
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->>> macro-correct, 文本纠错工具包(Text Correct), 支持中文拼写纠错/标点符号纠错(CSC, Chinese Spelling Correct / Check), CSC支持各领域数据(包括古文), 模型在大规模、各领域的、现代/当代语料上训练而得, 泛化性强.
->>> macro-correct是一个只依赖pytorch、transformers、numpy、opencc的文本纠错(CSC, 中文拼写纠错; Punct, 中文标点纠错)工具包，专注于中文文本纠错的极简自然语言处理工具包。
-使用大部分市面上的开源数据集构建生成的混淆集,使用人民日报语料&学习强国语料等生成1000万+训练数据集来训练模型;
-支持MDCSpell、Macbert、ReLM、SoftBERT、BertCRF等多种经典模型;
-支持中文拼写纠错、中文标点符号纠错、中文语法纠错(待续)、独立的检测模型/识别模型(待续);
-具有依赖轻量、代码简洁、注释详细、调试清晰、配置灵活、拓展方便、适配NLP等特性。
 ## 目录
-* [安装](#安装)
-* [调用](#调用)
-* [体验](#体验)
-* [词典](#词典)
-* [详情](#详情)
-* [训练](#训练)
-* [测评](#测评)
-* [日志](#日志)
-* [参考](#参考)
-* [论文](#论文)
-* [Cite](#Cite)
-# 安装
-```bash
-pip install macro-correct
-# 清华镜像源
-pip install -i https://pypi.tuna.tsinghua.edu.cn/simple macro-correct
-# 如果不行, 则不带依赖安装, 之后缺什么包再补充什么
-pip install -i https://pypi.tuna.tsinghua.edu.cn/simple macro-correct --no-dependencies
 ```
-# 调用
-  更多样例sample详情见/tet目录
-  - 使用example详见/tet/tet目录, 中文拼写纠错代码为tet_csc_token_zh.py, 中文标点符号纠错代码为tet_csc_punct_zh.py, CSC也可以直接用tet_csc_flag_transformers.py
-  - 训练代码详见/tet/train目录, 可配置本地预训练模型地址和各种参数等;
-# 体验
-  [HF---Space---Macropodus/macbert4csc_v2](https://huggingface.co/spaces/Macropodus/macbert4csc_v2)
-<img src="tet/images/csc_demo.png" width="1024">
-## 2.调用-文本纠错
-### 2.1 CSC 使用 macro-bert
-```python
-# !/usr/bin/python
-# -*- coding: utf-8 -*-
-# @time    : 2021/2/29 21:41
-# @author  : Mo
-# @function: 文本纠错, 使用macro-correct
 import os
 os.environ["MACRO_CORRECT_FLAG_CSC_TOKEN"] = "1"
 from macro_correct import correct
@@ -101,8 +138,8 @@ print("#" * 128)
 """
 ```
-### 2.2 CSC 使用 transformers
-```bash
 # !/usr/bin/python
 # -*- coding: utf-8 -*-
 # @time    : 2021/2/29 21:41
@@ -177,424 +214,67 @@ print(result)
 """
 ```
-## 3.调用-标点纠错
-```python
-import os
-os.environ["MACRO_CORRECT_FLAG_CSC_PUNCT"] = "1"
-from macro_correct import correct_punct
-### 1.默认标点纠错(list输入)
-text_list = ["山不在高有仙则名。",
-             "水不在深，有龙则灵",
-             "斯是陋室惟吾德馨",
-             "苔痕上阶绿草,色入帘青。"
-             ]
-text_csc = correct_punct(text_list)
-print("默认标点纠错(list输入):")
-for res_i in text_csc:
-    print(res_i)
-print("#" * 128)
-"""
-默认标点纠错(list输入):
-{'index': 0, 'source': '山不在高有仙则名。', 'target': '山不在高，有仙则名。', 'score': 0.9917, 'errors': [['', '，', 4, 0.9917]]}
-{'index': 1, 'source': '水不在深，有龙则灵', 'target': '水不在深，有龙则灵。', 'score': 0.9995, 'errors': [['', '。', 9, 0.9995]]}
-{'index': 2, 'source': '斯是陋室惟吾德馨', 'target': '斯是陋室，惟吾德馨。', 'score': 0.9999, 'errors': [['', '，', 4, 0.9999], ['', '。', 8, 0.9998]]}
-{'index': 3, 'source': '苔痕上阶绿草,色入帘青。', 'target': '苔痕上阶绿，草色入帘青。', 'score': 0.9998, 'errors': [['', '，', 5, 0.9998]]}
-"""
-```
-# 词典
-## 默认混淆词典地址
-* macro_correct/output/confusion_dict.json
-## 操作混淆词典
-```python
-## 自定义混淆词典
-# !/usr/bin/python
-# -*- coding: utf-8 -*-
-# @time    : 2021/2/29 21:41
-# @author  : Mo
-# @function: tet csc of token confusion dict, 混淆词典
-import os
-os.environ["MACRO_CORRECT_FLAG_CSC_TOKEN"] = "1"
-from macro_correct.pytorch_textcorrection.tcTrie import ConfusionCorrect
-from macro_correct import MODEL_CSC_TOKEN
-from macro_correct import correct
-### 默认使用混淆词典
-user_dict = {
-    "乐而往返": "乐而忘返",
-    "金钢钻": "金刚钻",
-    "藤罗蔓": "藤萝蔓",
-}
-text_list = [
-    "为什么乐而往返？",
-    "没有金钢钻就不揽瓷活！",
-    "你喜欢藤罗蔓吗？",
-    "三周年祭日在哪举行？"
-]
-text_csc = correct(text_list, flag_confusion=False)
-print("默认纠错(不带混淆词典):")
-for res_i in text_csc:
-    print(res_i)
-print("#" * 128)
-text_csc = correct(text_list, flag_confusion=True)
-print("默认纠错(-带混淆词典-默认):")
-for res_i in text_csc:
-    print(res_i)
-print("#" * 128)
-# ---混淆词典---
-### 只新增, 新增用户词典(默认混淆词典也使用)
-MODEL_CSC_TOKEN.model_csc.model_confusion = ConfusionCorrect(user_dict=user_dict)
-text_csc = correct(text_list, flag_confusion=True)
-print("默认纠错(-带混淆词典-新增):")
-for res_i in text_csc:
-    print(res_i)
-print("#" * 128)
-### 全覆盖, 只使用用户词典(默认混淆词典废弃)
-MODEL_CSC_TOKEN.model_csc.model_confusion = ConfusionCorrect(confusion_dict=user_dict)
-text_csc = correct(text_list, flag_confusion=True)
-print("默认纠错(-带混淆词典-全覆盖):")
-for res_i in text_csc:
-    print(res_i)
-print("#" * 128)
-# ---混淆词典文件---
-### 只新增, 新增用户词典(默认混淆词典也使用), path不为空即可; json文件, {混淆词语:正确词语} key-value; 详见macro-correct/tet/tet/tet_csc_token_confusion.py
-path_user = "./user_confusion_dict.json"
-MODEL_CSC_TOKEN.model_csc.model_confusion = ConfusionCorrect(path="1", path_user=path_user)
-text_csc = correct(text_list, flag_confusion=True)
-print("默认纠错(-带混淆词典文件-新增):")
-for res_i in text_csc:
-    print(res_i)
-print("#" * 128)
-### 全覆盖, 只使用用户词典(默认混淆词典废弃); path必须传空字符串
-MODEL_CSC_TOKEN.model_csc.model_confusion = ConfusionCorrect(path="", path_user=path_user)
-text_csc = correct(text_list, flag_confusion=True)
-print("默认纠错(-带混淆词典文件-全覆盖):")
-for res_i in text_csc:
-    print(res_i)
-print("#" * 128)
-"""
-默认纠错(不带混淆词典):
-{'index': 0, 'source': '为什么乐而往返？', 'target': '为什么乐而往返？', 'errors': []}
-{'index': 1, 'source': '没有金钢钻就不揽瓷活！', 'target': '没有金刚钻就不揽瓷活！', 'errors': [['钢', '刚', 3, 0.6587]]}
-{'index': 2, 'source': '你喜欢藤罗蔓吗？', 'target': '你喜欢藤萝蔓吗？', 'errors': [['罗', '萝', 4, 0.8582]]}
-{'index': 3, 'source': '三周年祭日在哪举行？', 'target': '三周年祭日在哪举行？', 'errors': []}
-################################################################################################################################
-默认纠错(-带混淆词典-默认):
-{'index': 0, 'source': '为什么乐而往返？', 'target': '为什么乐而往返？', 'errors': []}
-{'index': 1, 'source': '没有金钢钻就不揽瓷活！', 'target': '没有金刚钻就不揽瓷活！', 'errors': [['钢', '刚', 3, 1.0]]}
-{'index': 2, 'source': '你喜欢藤罗蔓吗？', 'target': '你喜欢藤萝蔓吗？', 'errors': [['罗', '萝', 4, 0.8582]]}
-{'index': 3, 'source': '三周年祭日在哪举行？', 'target': '三周年忌日在哪举行？', 'errors': [['祭', '忌', 3, 1.0]]}
-################################################################################################################################
-默认纠错(-带混淆词典-新增):
-{'index': 0, 'source': '为什么乐而往返？', 'target': '为什么乐而忘返？', 'errors': [['往', '忘', 5, 1.0]]}
-{'index': 1, 'source': '没有金钢钻就不揽瓷活！', 'target': '没有金刚钻就不揽瓷活！', 'errors': [['钢', '刚', 3, 1.0]]}
-{'index': 2, 'source': '你喜欢藤罗蔓吗？', 'target': '你喜欢藤萝蔓吗？', 'errors': [['罗', '萝', 4, 1.0]]}
-{'index': 3, 'source': '三周年祭日在哪举行？', 'target': '三周年忌日在哪举行？', 'errors': [['祭', '忌', 3, 1.0]]}
-################################################################################################################################
-默认纠错(-带混淆词典-全覆盖):
-{'index': 0, 'source': '为什么乐而往返？', 'target': '为什么乐而忘返？', 'errors': [['往', '忘', 5, 1.0]]}
-{'index': 1, 'source': '没有金钢钻就不揽瓷活！', 'target': '没有金刚钻就不揽瓷活！', 'errors': [['钢', '刚', 3, 1.0]]}
-{'index': 2, 'source': '你喜欢藤罗蔓吗？', 'target': '你喜欢藤萝蔓吗？', 'errors': [['罗', '萝', 4, 1.0]]}
-{'index': 3, 'source': '三周年祭日在哪举行？', 'target': '三周年祭日在哪举行？', 'errors': []}
-################################################################################################################################
-默认纠错(-带混淆词典文件-新增):
-{'index': 0, 'source': '为什么乐而往返？', 'target': '为什么乐而忘返？', 'errors': [['往', '忘', 5, 1.0]]}
-{'index': 1, 'source': '没有金钢钻就不揽瓷活！', 'target': '没有金刚钻就不揽瓷活！', 'errors': [['钢', '刚', 3, 1.0]]}
-{'index': 2, 'source': '你喜欢藤罗蔓吗？', 'target': '你喜欢藤萝蔓吗？', 'errors': [['罗', '萝', 4, 1.0]]}
-{'index': 3, 'source': '三周年祭日在哪举行？', 'target': '三周年忌日在哪举行？', 'errors': [['祭', '忌', 3, 1.0]]}
-################################################################################################################################
-默认纠错(-带混淆词典文件-全覆盖):
-{'index': 0, 'source': '为什么乐而往返？', 'target': '为什么乐而忘返？', 'errors': [['往', '忘', 5, 1.0]]}
-{'index': 1, 'source': '没有金钢钻就不揽瓷活！', 'target': '没有金刚钻就不揽瓷活！', 'errors': [['钢', '刚', 3, 1.0]]}
-{'index': 2, 'source': '你喜欢藤罗蔓吗？', 'target': '你喜欢藤萝蔓吗？', 'errors': [['罗', '萝', 4, 1.0]]}
-{'index': 3, 'source': '三周年祭日在哪举行？', 'target': '三周年祭日在哪举行？', 'errors': []}
-################################################################################################################################
-"""
-```
-# 详情
-## CSC调用(超参数说明)
-```python
-import os
-os.environ["MACRO_CORRECT_FLAG_CSC_TOKEN"] = "1"
-from macro_correct import correct
-### 默认纠错(list输入)
-text_list = ["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"
-             ]
-### 默认纠错(list输入, 参数配置)
-params = {
-    "threshold": 0.55,  # token阈值过滤
-    "batch_size": 32,  # 批大小
-    "max_len": 128,  # 自定义的长度, 如果截断了, 则截断部分不参与纠错, 后续直接一模一样的补回来
-    "rounded": 4,  # 保存4位小数
-    "flag_confusion": True,  # 是否使用默认的混淆词典
-    "flag_prob": True,  # 是否返回纠错token处的概率
-}
-text_csc = correct(text_list, **params)
-print("默认纠错(list输入, 参数配置):")
-for res_i in text_csc:
-    print(res_i)
-print("#" * 128)
-"""
-默认纠错(list输入):
-{'index': 0, 'source': '真麻烦你了。希望你们好好的跳无', 'target': '真麻烦你了。希望你们好好地跳舞', 'errors': [['的', '地', 12, 0.6584], ['无', '舞', 14, 1.0]]}
-{'index': 1, 'source': '少先队员因该为老人让坐', 'target': '少先队员应该为老人让坐', 'errors': [['因', '应', 4, 0.995]]}
-{'index': 2, 'source': '机七学习是人工智能领遇最能体现智能的一个分知', 'target': '机器学习是人工智能领域最能体现智能的一个分支', 'errors': [['七', '器', 1, 0.9998], ['遇', '域', 10, 0.9999], ['知', '支', 21, 1.0]]}
-{'index': 3, 'source': '一只小鱼船浮在平净的河面上', 'target': '一只小鱼船浮在平静的河面上', 'errors': [['净', '静', 8, 0.9961]]}
-"""
-```
-## PUNCT调用(超参数说明)
-```python
-import os
-os.environ["MACRO_CORRECT_FLAG_CSC_PUNCT"] = "1"
-from macro_correct import correct_punct
-### 1.默认标点纠错(list输入)
-text_list = ["山不在高有仙则名。",
-             "水不在深，有龙则灵",
-             "斯是陋室惟吾德馨",
-             "苔痕上阶绿草,色入帘青。"
-             ]
-### 2.默认标点纠错(list输入, 参数配置详情)
-params = {
-        "limit_num_errors": 4,  # 一句话最多的错别字, 多的就剔除
-        "limit_len_char": 4,   # 一句话的最小字符数
-        "threshold_zh": 0.5,  # 句子阈值, 中文字符占比的最低值
-        "threshold": 0.55,  # token阈值过滤
-        "batch_size": 32,  # 批大小
-        "max_len": 128,  # 自定义的长度, 如果截断了, 则截断部分不参与纠错, 后续直接一模一样的补回来
-        "rounded": 4,  # 保存4位小数
-        "flag_prob": True,  # 是否返回纠错token处的概率
-    }
-text_csc = correct_punct(text_list, **params)
-print("默认标点纠错(list输入):")
-for res_i in text_csc:
-    print(res_i)
-print("#" * 128)
-"""
-默认标点纠错(list输入):
-{'index': 0, 'source': '山不在高有仙则名。', 'target': '山不在高，有仙则名。', 'score': 0.9917, 'errors': [['', '，', 4, 0.9917]]}
-{'index': 1, 'source': '水不在深，有龙则灵', 'target': '水不在深，有龙则灵。', 'score': 0.9995, 'errors': [['', '。', 9, 0.9995]]}
-{'index': 2, 'source': '斯是陋室惟吾德馨', 'target': '斯是陋室，惟吾德馨。', 'score': 0.9999, 'errors': [['', '，', 4, 0.9999], ['', '。', 8, 0.9998]]}
-{'index': 3, 'source': '苔痕上阶绿草,色入帘青。', 'target': '苔痕上阶绿，草色入帘青。', 'score': 0.9998, 'errors': [['', '，', 5, 0.9998]]}
-"""
-```
-# 训练
-## CSC任务
-### 目录地址
-* macbert4mdcspell: macro_correct/pytorch_user_models/csc/macbert4mdcspell/train_yield.py
-* macbert4csc: macro_correct/pytorch_user_models/csc/macbert4csc/train_yield.py
-* relm: macro_correct/pytorch_user_models/csc/relm/train_yield.py
-### 数据准备
-* espell: list<dict>的json文件结构, 带"original_text"和"correct_text"就好, 参考macro_correct/corpus/text_correction/espell
-```
-[
-    {
-        "original_text": "遇到逆竟时，我们必须勇于面对，而且要愈挫愈勇，这样我们才能朝著成功之路前进。",
-        "correct_text": "遇到逆境时，我们必须勇于面对，而且要愈挫愈勇，这样我们才能朝著成功之路前进。",
-    }
-]
-```
-* sighan: list<dict>的json文件结构, 带"source"和"target"就好, 参考macro_correct/corpus/text_correction/sighan
-```
-[
-    {
-        "source": "若被告人正在劳动教养，则可以通过劳动教养单位转交",
-        "target": "若被告人正在劳动教养，则可以通过劳动教养单位转交",
-    }
-]
-```
-### 配置-训练-验证-预测
-#### 配置
-配置好数据地址和超参, 参考macro_correct/pytorch_user_models/csc/macbert4mdcspell/config.py
-#### 训练-验证-预测
-```
-训练
-nohup python train_yield.py > tc.train_yield.py.log 2>&1 &
-tail -n 1000  -f tc.train_yield.py.log
-验证
-python eval_std.py
-预测
-python predict.py
-```
-## PUNCT任务
-### 目录地址
-* PUNCT: macro_correct/pytorch_sequencelabeling/slRun.py
-### 数据准备
-* SPAN格式: NER任务, 默认用span格式(jsonl), 参考macro_correct/corpus/sequence_labeling/chinese_symbol的chinese_symbol.dev.span文件
-```
-{'label': [{'type': '0', 'ent': '下', 'pos': [7, 7]}, {'type': '1', 'ent': '林', 'pos': [14, 14]}], 'text': '#桂林山水甲天下阳朔山水甲桂林'}
-{'label': [{'type': '11', 'ent': 'o', 'pos': [5, 5]}, {'type': '0', 'ent': 't', 'pos': [12, 12]}, {'type': '1', 'ent': '包', 'pos': [19, 19]}], 'text': '#macrocorrect文本纠错工具包'}
-```
-* CONLL格式: 生成SPAN格式后, 用macro_correct/tet/corpus/pos_to_conll.py转换一下就好
-```
-神 O
-秘 O
-宝 O
-藏 B-1
-在 O
-旅 O
-途 O
-中 B-0
-他 O
-```
-### 配置-训练-验证-预测
-#### 配置
-配置好数据地址和超参, 参考macro_correct/pytorch_user_models/csc/macbert4mdcspell/config.py
-#### 训练-验证-预测
-```
-训练
-nohup python train_yield.py > tc.train_yield.py.log 2>&1 &
-tail -n 1000  -f tc.train_yield.py.log
-验证
-python eval_std.py
-预测
-python predict.py
-```
-# 测评
-## 说明
-* 所有训练数据均来自公网或开源数据, 训练数据为1千万左右, 混淆词典较大;
-* 所有测试数据均来自公网或开源数据, 测评数据地址为[Macropodus/csc_eval_public](https://huggingface.co/datasets/Macropodus/csc_eval_public);
-* 测评代码主要为[tcEval.py](https://github.com/yongzhuo/macro-correct/macro_correct/pytorch_textcorrection/tcEval.py); 其中[qwen25_1-5b_pycorrector]()的测评代码在目录[eval](https://github.com/yongzhuo/macro-correct/tet/eval)
-* 评估标准：过纠率(过度纠错, 即高质量正确句子的错误纠正); 句子级宽松标准的准确率/精确率/召回率/F1(同[shibing624/pycorrector](https://github.com/shibing624/pycorrector)); 句子级严格标准的准确率/精确率/召回率/F1(同[wangwang110/CSC](https://github.com/wangwang110/CSC)); 字符级别的准确率/精确率/召回率/F1(错别字);
-* qwen25_1-5b_pycorrector权重地址在[shibing624/chinese-text-correction-1.5b](https://huggingface.co/shibing624/chinese-text-correction-1.5b)
-* macbert4csc_pycorrector权重地址在[shibing624/macbert4csc-base-chinese](https://huggingface.co/shibing624/macbert4csc-base-chinese);
-* macbert4mdcspell_v1权重地址在[Macropodus/macbert4mdcspell_v1](https://huggingface.co/Macropodus/macbert4mdcspell_v1);
-* macbert4mdcspell_v2权重地址在[Macropodus/macbert4mdcspell_v2](https://huggingface.co/Macropodus/macbert4mdcspell_v2);
-* macbert4csc_v2权重地址在[Macropodus/macbert4csc_v2](https://huggingface.co/Macropodus/macbert4csc_v2);
-* macbert4csc_v1权重地址在[Macropodus/macbert4csc_v1](https://huggingface.co/Macropodus/macbert4csc_v1);
-* bert4csc_v1权重地址在[Macropodus/bert4csc_v1](https://huggingface.co/Macropodus/bert4csc_v1);
-## 3.1 测评数据
-```
-1.gen_de3.json(5545): '的地得'纠错, 由人民日报/学习强国/chinese-poetry等高质量数据人工生成;
-2.lemon_v2.tet.json(1053): relm论文提出的数据, 多领域拼写纠错数据集(7个领域), ; 包括game(GAM), encyclopedia (ENC), contract (COT), medical care(MEC), car (CAR), novel (NOV), and news (NEW)等领域;
-3.acc_rmrb.tet.json(4636): 来自NER-199801(人民日报高质量语料);
-4.acc_xxqg.tet.json(5000): 来自学习强国网站的高质量语料;
-5.gen_passage.tet.json(10000): 源数据为qwen生成的好词好句, 由几乎所有的开源数据汇总的混淆词典生成;
-6.textproof.tet.json(1447): NLP竞赛数据, TextProofreadingCompetition;
-7.gen_xxqg.tet.json(5000): 源数据为学习强国网站的高质量语料, 由几乎所有的开源数据汇总的混淆词典生成;
-8.faspell.dev.json(1000): 视频字幕通过OCR后获取的数据集; 来自爱奇艺的论文faspell;
-9.lomo_tet.json(5000): 主要为音似中文拼写纠错数据集; 来自腾讯; 人工标注的数据集CSCD-NS;
-10.mcsc_tet.5000.json(5000): 医学拼写纠错; 来自腾讯医典APP的真实历史日志; 注意论文说该数据集只关注医学实体的纠错, 常用字等的纠错并不关注;
-11.ecspell.dev.json(1500): 来自ECSpell论文, 包括(law/med/gov)等三个领域;
-12.sighan2013.dev.json(1000): 来自sighan13会议;
-13.sighan2014.dev.json(1062): 来自sighan14会议;
-14.sighan2015.dev.json(1100): 来自sighan15会议;
-```
-## 3.2 测评再说明
-```
-1.数据预处理, 测评数据都经过 全角转半角,繁简转化,标点符号标准化等操作;
-2.指标带common的极为宽松指标, 同开源项目pycorrector的评估指标;
-3.指标带strict的极为严格指标, 同开源项目[wangwang110/CSC](https://github.com/wangwang110/CSC);
-4.macbert4mdcspell_v1/v2模型为训练使用mdcspell架构+bert的mlm-loss, 但是推理的时候只用bert-mlm;
-5.acc_rmrb/acc_xxqg数据集没有错误, 用于评估模型的误纠率(过度纠错);
-6.qwen25_1-5b_pycorrector的模型为shibing624/chinese-text-correction-1.5b, 其训练数据包括了lemon_v2/mcsc_tet/ecspell的验证集和测试集, 其他的bert类模型的训练不包括验证集和测试集;
-```
-## 3.3 测评结果
-### 3.3.1 F1(common_cor_f1)
-| model/common_cor_f1     | avg| gen_de3| lemon_v2| gen_passage| text_proof| gen_xxqg| faspell| lomo_tet| mcsc_tet| ecspell| sighan2013| sighan2014| sighan2015 |
-|:------------------------|:-----------------|:-----------------|:-----------------|:-----------------|:-----------------|:-----------------|:-----------------|:-----------------|:-----------------|:-----------------|:-----------------|:-----------------|:-----------------|
-| macbert4csc_pycorrector | 45.8| 42.44| 42.89| 31.49| 46.31| 26.06| 32.7| 44.83| 27.93| 55.51| 70.89| 61.72| 66.81 |
-| qwen25_1-5b_pycorrector | 45.11| 27.29| 89.48| 14.61| 83.9| 13.84| 18.2| 36.71| 96.29| 88.2| 36.41| 15.64| 20.73 |
-| bert4csc_v1             | 62.28| 93.73| 61.99| 44.79| 68.0| 35.03| 48.28| 61.8| 64.41| 79.11| 77.66| 51.01| 61.54 |
-| macbert4csc_v1          | 68.55| 96.67| 65.63| 48.4| 75.65| 38.43| 51.76| 70.11| 80.63| 85.55| 81.38| 57.63| 70.7 |
-| macbert4csc_v2          | 68.6| 96.74| 66.02| 48.26| 75.78| 38.84| 51.91| 70.17| 80.71| 85.61| 80.97| 58.22| 69.95 |
-| macbert4mdcspell_v1     | 71.1| 96.42| 70.06| 52.55| 79.61| 43.37| 53.85| 70.9| 82.38| 87.46| 84.2| 61.08| 71.32 |
-| macbert4mdcspell_v2     | 71.23| 96.42| 65.8| 52.35| 75.94| 43.5| 53.82| 72.66| 82.28| 88.69| 82.51| 65.59| 75.26 |
-### 3.3.2 acc(common_cor_acc)
-| model/common_cor_acc| avg| gen_de3| lemon_v2| gen_passage| text_proof| gen_xxqg| faspell| lomo_tet| mcsc_tet| ecspell| sighan2013| sighan2014| sighan2015 |
-|:-----------------|:-----------------|:-----------------|:-----------------|:-----------------|:-----------------|:-----------------|:-----------------|:-----------------|:-----------------|:-----------------|:-----------------|:-----------------|:-----------------|
-| macbert4csc_pycorrector| 48.26| 26.96| 28.68| 34.16| 55.29| 28.38| 22.2| 60.96| 57.16| 67.73| 55.9| 68.93| 72.73 |
-| qwen25_1-5b_pycorrector| 46.09| 15.82| 81.29| 22.96| 82.17| 19.04| 12.8| 50.2| 96.4| 89.13| 22.8| 27.87| 32.55 |
-| bert4csc_v1| 60.76| 88.21| 45.96| 43.13| 68.97| 35.0| 34.0| 65.86| 73.26| 81.8| 64.5| 61.11| 67.27 |
-| macbert4csc_v1| 65.34| 93.56| 49.76| 44.98| 74.64| 36.1| 37.0| 73.0| 83.6| 86.87| 69.2| 62.62| 72.73 |
-| macbert4csc_v2| 65.22| 93.69| 50.14| 44.92| 74.64| 36.26| 37.0| 72.72| 83.66| 86.93| 68.5| 62.43| 71.73 |
-| macbert4mdcspell_v1| 67.15| 93.09| 54.8| 47.71| 78.09| 39.52| 38.8| 71.92| 84.78| 88.27| 73.2| 63.28| 72.36 |
-| macbert4mdcspell_v2     | 68.31| 93.09| 50.05| 48.72| 75.74| 40.52| 38.9| 76.9| 84.8| 89.73| 71.0| 71.94| 78.36 |
-### 3.3.3 acc(acc_true, thr=0.75)
-| model/acc               | avg| acc_rmrb| acc_xxqg |
-|:------------------------|:-----------------|:-----------------|:-----------------|
-| macbert4csc_pycorrector | 99.24| 99.22| 99.26 |
-| qwen25_1-5b_pycorrector | 82.0| 77.14| 86.86 |
-| bert4csc_v1             | 98.71| 98.36| 99.06 |
-| macbert4csc_v1          | 97.72| 96.72| 98.72 |
-| macbert4csc_v2          | 97.89| 96.98| 98.8 |
-| macbert4mdcspell_v1     | 97.75| 96.51| 98.98 |
-| macbert4mdcspell_v2     | 99.54| 99.22| 99.86 |
-### 3.3.4 结论(Conclusion)
-```
-1.macbert4csc_v1/macbert4csc_v2/macbert4mdcspell_v1等模型使用多种领域数据训练, 比较均衡, 也适合作为第一步的预训练模型, 可用于专有领域数据的继续微调;
-2.比较macbert4csc_pycorrector/bertbase4csc_v1/macbert4csc_v2/macbert4mdcspell_v1, 观察表2.3, 可以发现训练数据越多, 准确率提升的同时, 误纠率也会稍微高一些;
-3.MFT(Mask-Correct)依旧有效, 不过对于数据量足够的情形提升不明显, 可能也是误纠率升高的一个重要原因;
-4.训练数据中也存在文言文数据, 训练好的模型也支持文言文纠错;
-5.训练好的模型对"地得的"等高频错误具有较高的识别率和纠错率;
-6.macbert4mdcspell_v2的MFT只70%的时间no-error-mask(0.15), 15%的时间target-to-target, 15%的时间不mask;
-```
-# 日志
-```
-1. v20240129, 完成csc_punct模块;
-2. v20241001, 完成csc_token模块;
-3. v20250117, 完成csc_eval模块；
-4. v20250501, 完成macbert4mdcspell_v2
-```
-# 参考
-This library is inspired by and references following frameworks and papers.
-* Chinese-text-correction-papers: [nghuyong/Chinese-text-correction-papers](https://github.com/nghuyong/Chinese-text-correction-papers)
-* pycorrector: [shibing624/pycorrector](https://github.com/shibing624/pycorrector)
-* CTCResources: [destwang/CTCResources](https://github.com/destwang/CTCResources)
-* CSC: [wangwang110/CSC](https://github.com/wangwang110/CSC)
-* char-similar: [yongzhuo/char-similar](https://github.com/yongzhuo/char-similar)
-* MDCSpell: [iioSnail/MDCSpell_pytorch](https://github.com/iioSnail/MDCSpell_pytorch)
-* CSCD-NS: [nghuyong/cscd-ns](https://github.com/nghuyong/cscd-ns)
-* lemon: [gingasan/lemon](https://github.com/gingasan/lemon)
-* ReLM: [Claude-Liu/ReLM](https://github.com/Claude-Liu/ReLM)
-# 论文
-## 中文拼写纠错(CSC, Chinese Spelling Correction)
-* 共收录34篇论文, 写了一个简短的综述. 详见[README.csc_survey.md](https://github.com/yongzhuo/macro-correct/blob/master/README.csc_survey.md)
-# Cite
 For citing this work, you can refer to the present GitHub project. For example, with BibTeX:
 ```
 @software{macro-correct,
@@ -602,5 +282,4 @@ For citing this work, you can refer to the present GitHub project. For example,
     author = {Yongzhuo Mo},
     title = {macro-correct},
     year = {2025}
 ```

 license: apache-2.0
 language:
 - zh
+base_model:
+- hfl/chinese-macbert-base
+pipeline_tag: text-generation
 tags:
 - csc
+- text-correct
+- chinses-spelling-correct
+- chinese-spelling-check
+- 中文拼写纠错
+- 文本纠错
 - mdcspell
+- macro-correct
 ---
+# macbert4mdcspell
+## 概述(macbert4mdcspell)
+ - macro-correct, 中文拼写纠错CSC测评(文本纠错), 权重使用
+ - 项目地址在 [https://github.com/yongzhuo/macro-correct](https://github.com/yongzhuo/macro-correct)
+ - 本模型权重为macbert4mdcspell_v2, 使用mdcspell架构, 其特点是det_label和cor_label交互;
+ - 训练时加入了macbert的mlm-loss, 推理时舍弃了macbert后面的部分;
+ - 如何使用: 1.使用transformers调用; 2.使用[macro-correct](https://github.com/yongzhuo/macro-correct)项目调用; 详情见***三、调用(Usage)***;
+ - 为了修复过纠问题, macbert4mdcspell_v2的MFT只70%的时间no-error-mask(0.15), 15%的时间target-to-target, 15%的时间不mask;
 ## 目录
+* [一、测评(Test)](#一、测评(Test))
+* [二、结论(Conclusion)](#二、结论(Conclusion))
+* [三、调用(Usage)](#三、调用(Usage))
+* [四、论文(Paper)](#四、论文(Paper))
+* [五、参考(Refer)](#五、参考(Refer))
+* [六、引用(Cite)](#六、引用(Cite))
+## 一、测评(Test)
+### 1.1 测评数据来源
+地址为[Macropodus/csc_eval_public](https://huggingface.co/datasets/Macropodus/csc_eval_public), 所有训练数据均来自公网或开源数据, 训练数据为1千万左右, 混淆词典较大;
+```
+1.gen_de3.json(5545): '的地得'纠错, 由人民日报/学习强国/chinese-poetry等高质量数据人工生成;
+2.lemon_v2.tet.json(1053): relm论文提出的数据, 多领域拼写纠错数据集(7个领域), ; 包括game(GAM), encyclopedia (ENC), contract (COT), medical care(MEC), car (CAR), novel (NOV), and news (NEW)等领域;
+3.acc_rmrb.tet.json(4636): 来自NER-199801(人民日报高质量语料);
+4.acc_xxqg.tet.json(5000): 来自学习强国网站的高质量语料;
+5.gen_passage.tet.json(10000): 源数据为qwen生成的好词好句, 由几乎所有的开源数据汇总的混淆词典生成;
+6.textproof.tet.json(1447): NLP竞赛数据, TextProofreadingCompetition;
+7.gen_xxqg.tet.json(5000): 源数据为学习强国网站的高质量语料, 由几乎所有的开源数据汇总的混淆词典生成;
+8.faspell.dev.json(1000): 视频字幕通过OCR后获取的数据集; 来自爱奇艺的论文faspell;
+9.lomo_tet.json(5000): 主要��音似中文拼写纠错数据集; 来自腾讯; 人工标注的数据集CSCD-NS;
+10.mcsc_tet.5000.json(5000): 医学拼写纠错; 来自腾讯医典APP的真实历史日志; 注意论文说该数据集只关注医学实体的纠错, 常用字等的纠错并不关注;
+11.ecspell.dev.json(1500): 来自ECSpell论文, 包括(law/med/gov)等三个领域;
+12.sighan2013.dev.json(1000): 来自sighan13会议;
+13.sighan2014.dev.json(1062): 来自sighan14会议;
+14.sighan2015.dev.json(1100): 来自sighan15会议;
+```
+### 1.2 测评数据预处理
+```
+测评数据都经过 全角转半角,繁简转化,标点符号标准化等操作;
 ```
+### 1.3 其他说明
+```
+1.指标带common的极为宽松指标, 同开源项目pycorrector的评估指标;
+2.指标带strict的极为严格指标, 同开源项目[wangwang110/CSC](https://github.com/wangwang110/CSC);
+3.macbert4mdcspell_v1模型为训练使用mdcspell架构+bert的mlm-loss, 但是推理的时候只用bert-mlm;
+4.acc_rmrb/acc_xxqg数据集没有错误, 用于评估模型的误纠率(过度纠错);
+5.qwen25_1-5b_pycorrector的模型为shibing624/chinese-text-correction-1.5b, 其训练数据包括了lemon_v2/mcsc_tet/ecspell的验证集和测试集, 其他的bert类模型的训练不包括验证集和测试集;
+```
+## 二、重要指标
+### 2.1 F1(common_cor_f1)
+| model/common_cor_f1     | avg| gen_de3| lemon_v2| gen_passage| text_proof| gen_xxqg| faspell| lomo_tet| mcsc_tet| ecspell| sighan2013| sighan2014| sighan2015 |
+|:------------------------|:-----------------|:-----------------|:-----------------|:-----------------|:-----------------|:-----------------|:-----------------|:-----------------|:-----------------|:-----------------|:-----------------|:-----------------|:-----------------|
+| macbert4csc_pycorrector | 45.8| 42.44| 42.89| 31.49| 46.31| 26.06| 32.7| 44.83| 27.93| 55.51| 70.89| 61.72| 66.81 |
+| qwen25_1-5b_pycorrector | 45.11| 27.29| 89.48| 14.61| 83.9| 13.84| 18.2| 36.71| 96.29| 88.2| 36.41| 15.64| 20.73 |
+| bert4csc_v1             | 62.28| 93.73| 61.99| 44.79| 68.0| 35.03| 48.28| 61.8| 64.41| 79.11| 77.66| 51.01| 61.54 |
+| macbert4csc_v1          | 68.55| 96.67| 65.63| 48.4| 75.65| 38.43| 51.76| 70.11| 80.63| 85.55| 81.38| 57.63| 70.7 |
+| macbert4csc_v2          | 68.6| 96.74| 66.02| 48.26| 75.78| 38.84| 51.91| 70.17| 80.71| 85.61| 80.97| 58.22| 69.95 |
+| macbert4mdcspell_v1     | 71.1| 96.42| 70.06| 52.55| 79.61| 43.37| 53.85| 70.9| 82.38| 87.46| 84.2| 61.08| 71.32 |
+| macbert4mdcspell_v2     | 71.23| 96.42| 65.8| 52.35| 75.94| 43.5| 53.82| 72.66| 82.28| 88.69| 82.51| 65.59| 75.26 |
+### 2.2 acc(common_cor_acc)
+| model/common_cor_acc| avg| gen_de3| lemon_v2| gen_passage| text_proof| gen_xxqg| faspell| lomo_tet| mcsc_tet| ecspell| sighan2013| sighan2014| sighan2015 |
+|:-----------------|:-----------------|:-----------------|:-----------------|:-----------------|:-----------------|:-----------------|:-----------------|:-----------------|:-----------------|:-----------------|:-----------------|:-----------------|:-----------------|
+| macbert4csc_pycorrector| 48.26| 26.96| 28.68| 34.16| 55.29| 28.38| 22.2| 60.96| 57.16| 67.73| 55.9| 68.93| 72.73 |
+| qwen25_1-5b_pycorrector| 46.09| 15.82| 81.29| 22.96| 82.17| 19.04| 12.8| 50.2| 96.4| 89.13| 22.8| 27.87| 32.55 |
+| bert4csc_v1| 60.76| 88.21| 45.96| 43.13| 68.97| 35.0| 34.0| 65.86| 73.26| 81.8| 64.5| 61.11| 67.27 |
+| macbert4csc_v1| 65.34| 93.56| 49.76| 44.98| 74.64| 36.1| 37.0| 73.0| 83.6| 86.87| 69.2| 62.62| 72.73 |
+| macbert4csc_v2| 65.22| 93.69| 50.14| 44.92| 74.64| 36.26| 37.0| 72.72| 83.66| 86.93| 68.5| 62.43| 71.73 |
+| macbert4mdcspell_v1| 67.15| 93.09| 54.8| 47.71| 78.09| 39.52| 38.8| 71.92| 84.78| 88.27| 73.2| 63.28| 72.36 |
+| macbert4mdcspell_v2     | 68.31| 93.09| 50.05| 48.72| 75.74| 40.52| 38.9| 76.9| 84.8| 89.73| 71.0| 71.94| 78.36 |
+### 2.3 acc(acc_true, thr=0.75)
+| model/acc               | avg| acc_rmrb| acc_xxqg |
+|:------------------------|:-----------------|:-----------------|:-----------------|
+| macbert4csc_pycorrector | 99.24| 99.22| 99.26 |
+| qwen25_1-5b_pycorrector | 82.0| 77.14| 86.86 |
+| bert4csc_v1             | 98.71| 98.36| 99.06 |
+| macbert4csc_v1          | 97.72| 96.72| 98.72 |
+| macbert4csc_v2          | 97.89| 96.98| 98.8 |
+| macbert4mdcspell_v1     | 97.75| 96.51| 98.98 |
+| macbert4mdcspell_v2     | 99.54| 99.22| 99.86 |
+## 二、结论(Conclusion)
+```
+1.macbert4csc_v1/macbert4csc_v2/macbert4mdcspell_v1等模型使用多种领域数据训练, 比较均衡, 也适合作为第一步的预训练模型, 可用于专有领域数据的继续微调;
+2.比较macbert4csc_pycorrector/bertbase4csc_v1/macbert4csc_v2/macbert4mdcspell_v1, 观察表2.3, 可以发现训练数据越多, 准确率提升的同时, 误纠率也会稍微高一些;
+3.MFT(Mask-Correct)依旧有效, 不过对于数据量足够的情形提升不明显, 可能也是误纠率升高的一个重要原因;
+4.训练数据中也存在文言文数据, 训练好的模型也支持文言文纠错;
+5.训练好的模型对"地得的"等高频错误具有较高的识别率和纠错率;
+6.macbert4mdcspell_v2的MFT只70%的时间no-error-mask(0.15), 15%的时间target-to-target, 15%的时间不mask;
+```
+## 三、调用(Usage)
+### 3.1 使用macro-correct
+```
 import os
 os.environ["MACRO_CORRECT_FLAG_CSC_TOKEN"] = "1"
 from macro_correct import correct
 """
 ```
+### 3.2 使用 transformers
+```
 # !/usr/bin/python
 # -*- coding: utf-8 -*-
 # @time    : 2021/2/29 21:41
 """
 ```
+## 四、论文(Paper)
+ - 2024-Refining: [Refining Corpora from a Model Calibration Perspective for Chinese](https://arxiv.org/abs/2407.15498)
+ - 2024-ReLM: [Chinese Spelling Correction as Rephrasing Language Model](https://arxiv.org/abs/2308.08796)
+ - 2024-DICS: [DISC: Plug-and-Play Decoding Intervention with Similarity of Characters for Chinese Spelling Check](https://arxiv.org/abs/2412.12863)
+ - 2023-Bi-DCSpell: [A Bi-directional Detector-Corrector Interactive Framework for Chinese Spelling Check]()
+ - 2023-BERT-MFT: [Rethinking Masked Language Modeling for Chinese Spelling Correction](https://arxiv.org/abs/2305.17721)
+ - 2023-PTCSpell: [PTCSpell: Pre-trained Corrector Based on Character Shape and Pinyin for Chinese Spelling Correction](https://arxiv.org/abs/2212.04068)
+ - 2023-DR-CSC: [A Frustratingly Easy Plug-and-Play Detection-and-Reasoning Module for Chinese](https://aclanthology.org/2023.findings-emnlp.771)
+ - 2023-DROM: [Disentangled Phonetic Representation for Chinese Spelling Correction](https://arxiv.org/abs/2305.14783)
+ - 2023-EGCM: [An Error-Guided Correction Model for Chinese Spelling Error Correction](https://arxiv.org/abs/2301.06323)
+ - 2023-IGPI: [Investigating Glyph-Phonetic Information for Chinese Spell Checking: What Works and What’s Next?](https://arxiv.org/abs/2212.04068)
+ - 2023-CL: [Contextual Similarity is More Valuable than Character Similarity-An Empirical Study for Chinese Spell Checking]()
+ - 2022-CRASpell: [CRASpell: A Contextual Typo Robust Approach to Improve Chinese Spelling Correction](https://aclanthology.org/2022.findings-acl.237)
+ - 2022-MDCSpell: [MDCSpell: A Multi-task Detector-Corrector Framework for Chinese Spelling Correction](https://aclanthology.org/2022.findings-acl.98)
+ - 2022-SCOPE: [Improving Chinese Spelling Check by Character Pronunciation Prediction: The Effects of Adaptivity and Granularity](https://arxiv.org/abs/2210.10996)
+ - 2022-ECOPO: [The Past Mistake is the Future Wisdom: Error-driven Contrastive Probability Optimization for Chinese Spell Checking](https://arxiv.org/abs/2203.00991)
+ - 2021-MLMPhonetics: [Correcting Chinese Spelling Errors with Phonetic Pre-training](https://aclanthology.org/2021.findings-acl.198)
+ - 2021-ChineseBERT: [ChineseBERT: Chinese Pretraining Enhanced by Glyph and Pinyin Information](https://aclanthology.org/2021.acl-long.161/)
+ - 2021-BERTCrsGad: [Global Attention Decoder for Chinese Spelling Error Correction](https://aclanthology.org/2021.findings-acl.122)
+ - 2021-ThinkTwice: [Think Twice: A Post-Processing Approach for the Chinese Spelling Error Correction](https://www.mdpi.com/2076-3417/11/13/5832)
+ - 2021-PHMOSpell: [PHMOSpell: Phonological and Morphological Knowledge Guided Chinese Spelling Chec](https://aclanthology.org/2021.acl-long.464)
+ - 2021-SpellBERT: [SpellBERT: A Lightweight Pretrained Model for Chinese Spelling Check](https://aclanthology.org/2021.emnlp-main.287)
+ - 2021-TwoWays: [Exploration and Exploitation: Two Ways to Improve Chinese Spelling Correction Models](https://aclanthology.org/2021.acl-short.56)
+ - 2021-ReaLiSe: [Read, Listen, and See: Leveraging Multimodal Information Helps Chinese Spell Checking](https://arxiv.org/abs/2105.12306)
+ - 2021-DCSpell: [DCSpell: A Detector-Corrector Framework for Chinese Spelling Error Correction](https://dl.acm.org/doi/10.1145/3404835.3463050)
+ - 2021-PLOME: [PLOME: Pre-training with Misspelled Knowledge for Chinese Spelling Correction](https://aclanthology.org/2021.acl-long.233)
+ - 2021-DCN: [Dynamic Connected Networks for Chinese Spelling Check](https://aclanthology.org/2021.findings-acl.216/)
+ - 2020-SoftMaskBERT: [Spelling Error Correction with Soft-Masked BERT](https://arxiv.org/abs/2005.07421)
+ - 2020-SpellGCN: [SpellGCN：Incorporating Phonological and Visual Similarities into Language Models for Chinese Spelling Check](https://arxiv.org/abs/2004.14166)
+ - 2020-ChunkCSC: [Chunk-based Chinese Spelling Check with Global Optimization](https://aclanthology.org/2020.findings-emnlp.184)
+ - 2020-MacBERT: [Revisiting Pre-Trained Models for Chinese Natural Language Processing](https://arxiv.org/abs/2004.13922)
+ - 2019-FASPell: [FASPell: A Fast, Adaptable, Simple, Powerful Chinese Spell Checker Based On DAE-Decoder Paradigm](https://aclanthology.org/D19-5522)
+ - 2018-Hybrid: [A Hybrid Approach to Automatic Corpus Generation for Chinese Spelling Checking](https://aclanthology.org/D18-1273)
+ - 2015-Sighan15: [Introduction to SIGHAN 2015 Bake-off for Chinese Spelling Check](https://aclanthology.org/W15-3106/)
+ - 2014-Sighan14: [Overview of SIGHAN 2014 Bake-off for Chinese Spelling Check](https://aclanthology.org/W14-6820/)
+ - 2013-Sighan13: [Chinese Spelling Check Evaluation at SIGHAN Bake-off 2013](https://aclanthology.org/W13-4406/)
+## 五、参考(Refer)
+ - [nghuyong/Chinese-text-correction-papers](https://github.com/nghuyong/Chinese-text-correction-papers)
+ - [destwang/CTCResources](https://github.com/destwang/CTCResources)
+ - [wangwang110/CSC](https://github.com/wangwang110/CSC)
+ - [chinese-poetry/chinese-poetry](https://github.com/chinese-poetry/chinese-poetry)
+ - [chinese-poetry/huajianji](https://github.com/chinese-poetry/huajianji)
+ - [garychowcmu/daizhigev20](https://github.com/garychowcmu/daizhigev20)
+ - [yangjianxin1/Firefly](https://github.com/yangjianxin1/Firefly)
+ - [Macropodus/xuexiqiangguo_428w](https://huggingface.co/datasets/Macropodus/xuexiqiangguo_428w)
+ - [Macropodus/csc_clean_wang271k](https://huggingface.co/datasets/Macropodus/csc_clean_wang271k)
+ - [Macropodus/csc_eval_public](https://huggingface.co/datasets//Macropodus/csc_eval_public)
+ - [shibing624/pycorrector](https://github.com/shibing624/pycorrector)
+ - [iioSnail/MDCSpell_pytorch](https://github.com/iioSnail/MDCSpell_pytorch)
+ - [gingasan/lemon](https://github.com/gingasan/lemon)
+ - [Claude-Liu/ReLM](https://github.com/Claude-Liu/ReLM)
+## 六、引用(Cite)
 For citing this work, you can refer to the present GitHub project. For example, with BibTeX:
 ```
 @software{macro-correct,
     author = {Yongzhuo Mo},
     title = {macro-correct},
     year = {2025}
 ```