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----
-license: apache-2.0
----

+---
+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_v3, 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_v3的MFT只85%的时间no-error-mask(0.15), 5%的时间target-to-target, 10%的时间不mask;
+ - 训练数据为 2000w+, 相对于macbert4mdcspell_v2, 强化了古诗和文言文的训练;
+## 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;
+15.wenyanwen_and_poetry.tet.json(5000), 来自课本古诗和通用文言文;
+```
+### 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/v2/v3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 |
+|:----------------------------------------|:-----------------|:-----------------|:-----------------|:-----------------|:-----------------|:-----------------|:-----------------|:-----------------|:-----------------|:-----------------|:-----------------|:-----------------|:-----------------|
+| shibing624/macbert4csc-base-chinese     | 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 |
+| shibing624/chinese-text-correction-1.5b | 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 |
+| twnlp/ChineseErrorCorrector3-4B         | 53.59| 30.28| 89.43| 22.94| 39.9| 16.89| 30.53| 71.0| 99.92| 72.43| 65.02| 47.81| 56.88 |
+| relm_v1                                 | 54.12| 89.86| 51.79| 38.4| 63.74| 30.6| 31.95| 49.82| 64.7| 73.57| 66.4| 39.87| 48.8 |
+| 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 |
+| macbert4mdcspell_v3                     | 71.71| 96.43| 68.07| 59.36| 78.81| 50.07| 48.67| 74.51| 79.03| 87.16| 81.31| 64.29| 72.76 |
+| macbert4mdcspell_v1_rethink2            | 69.64| 92.4| 67.99| 57.69| 77.49| 50.38| 53.96| 69.35| 84.65| 88.26| 70.96| 56.05| 66.54 |
+| macbert4mdcspell_v2_rethink2            | 72.54| 95.59| 65.54| 58.01| 75.86| 49.67| 55.56| 72.78| 84.65| 90.78| 80.93| 65.74| 75.39 |
+| macbert4mdcspell_v3_rethink2            | 71.82| 95.05| 67.48| 62.19| 78.0| 55.2| 49.5| 74.26| 81.72| 87.56| 76.75| 62.96| 71.12 |
+### 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 |
+|:----------------------------------------|:------|:-----------------|:-----------------|:-----------------|:-----------------|:-----------------|:-----------------|:-----------------|:-----------------|:-----------------|:-----------------|:-----------------|:-----------------|
+| shibing624/macbert4csc-base-chinese     | 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 |
+| shibing624/chinese-text-correction-1.5b | 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 |
+| twnlp/ChineseErrorCorrector3-4B         | 51.85 | 17.87| 81.2| 27.32| 48.17| 23.44| 20.8| 77.16| 99.92| 76.6| 49.0| 47.18| 53.55 |
+| relm_v1                                 | 51.9  | 81.71| 36.18| 37.04| 63.99| 29.34| 22.9| 51.98| 74.1| 76.0| 50.3| 45.76| 53.45 |
+| 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 |
+| macbert4mdcspell_v3                     | 68.09 | 93.11| 52.42| 53.91| 77.89| 45.28| 34.2| 76.82| 82.5| 88.13| 69.2| 68.83| 74.82 |
+| macbert4mdcspell_v1_rethink2            | 65.04 | 85.88| 52.42| 51.69| 76.23| 44.52| 38.9| 70.78| 86.48| 88.93| 55.8| 59.98| 68.91 |
+| macbert4mdcspell_v2_rethink2            | 69.14 | 91.56| 49.76| 53.01| 75.67| 44.84| 40.5| 76.98| 86.56| 91.47| 68.8| 72.03| 78.45 |
+| macbert4mdcspell_v3_rethink2            | 67.84 | 90.57| 51.76| 56.24| 77.19| 49.16| 34.9| 76.64| 84.4| 88.47| 63.1| 67.98| 73.64 |
+### 2.3 acc(acc_true, thr=0.75)
+| model/acc                               | avg    | acc_rmrb | acc_xxqg |
+|:----------------------------------------|:-------|:---------|:---------|
+| shibing624/macbert4csc-base-chinese     | 99.24  | 99.22    | 99.26    |
+| shibing624/chinese-text-correction-1.5b | 82.0   | 77.14    | 86.86    |
+| twnlp/ChineseErrorCorrector3-4B         | 77.03  | 76.96    | 77.1     |
+| relm_v1                                 | 93.47  | 90.21    | 96.74    |
+| 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    |
+| macbert4mdcspell_v3                     | 98.85  | 98.32    | 99.38    |
+| macbert4mdcspell_v1_rethink2            | 92.78  | 88.31    | 97.24    |
+| macbert4mdcspell_v2_rethink2            | 98.15  | 96.72    | 99.58    |
+| macbert4mdcspell_v3_rethink2            | 98.85  | 98.32    | 99.38    |
+## 数据集_alipayseq
+| model/common_cor_f1             | alipayseq |
+|:--------------------------------|:----------|
+| shibing624/macbert4csc          | 15.36     |
+| twnlp/ChineseErrorCorrector3-4B | 42.84     |
+| bert4csc_v1                     | 42.23     |
+| macbert4csc_v1                  | 48.45     |
+| macbert4csc_v2                  | 45.60     |
+| macbert4mdcspell_v1             | 48.97     |
+| macbert4mdcspell_v2             | 50.41     |
+| macbert4mdcspell_v3             | 50.14     |
+## 数据集_古诗-文言文数据集测评
+| model/common_cor_f1                         | det   | cor   |
+|:------------------------------------|:------|:------|
+| shibing624/macbert4csc-base-chinese | 44.12 | 7.48  |
+| macbert4mdcspell_v1                 | 58.98 | 12.43 |
+| macbert4mdcspell_v2                 | 50.61 | 10.40 |
+| macbert4mdcspell_v3                 | 73.24 | 47.41 |
+## 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;
+7.macbert4mdcspell_v3的MFT只85%的时间no-error-mask(0.15), 5%的时间target-to-target, 10%的时间不mask;(强化古文/现代文, 但降target-to-target比例太多使其比v2过纠多一点点)
+```
+## 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
+### 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"
+             ]
+text_csc = correct(text_list)
+print("默认纠错(list输入):")
+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]]}
+"""
+```
+### 3.2 使用 transformers
+```
+# !/usr/bin/python
+# -*- coding: utf-8 -*-
+# @time    : 2021/2/29 21:41
+# @author  : Mo
+# @function: transformers直接加载bert类模型测试
+import traceback
+import time
+import sys
+import os
+os.environ["USE_TORCH"] = "1"
+from transformers import BertConfig, BertTokenizer, BertForMaskedLM
+import torch
+# pretrained_model_name_or_path = "shibing624/macbert4csc-base-chinese"
+pretrained_model_name_or_path = "Macropodus/macbert4mdcspell_v3"
+# pretrained_model_name_or_path = "Macropodus/macbert4mdcspell_v2"
+# pretrained_model_name_or_path = "Macropodus/macbert4mdcspell_v1"
+# pretrained_model_name_or_path = "Macropodus/macbert4csc_v1"
+# pretrained_model_name_or_path = "Macropodus/macbert4csc_v2"
+# pretrained_model_name_or_path = "Macropodus/bert4csc_v1"
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+max_len = 128
+print("load model, please wait a few minute!")
+tokenizer = BertTokenizer.from_pretrained(pretrained_model_name_or_path)
+bert_config = BertConfig.from_pretrained(pretrained_model_name_or_path)
+model = BertForMaskedLM.from_pretrained(pretrained_model_name_or_path)
+model.to(device)
+print("load model success!")
+texts = [
+    "机七学习是人工智能领遇最能体现智能的一个分知",
+    "我是练习时长两念半的鸽仁练习生蔡徐坤",
+    "真麻烦你了。希望你们好好的跳无",
+    "他法语说的很好，的语也不错",
+    "遇到一位很棒的奴生跟我疗天",
+    "我们为这个目标努力不解",
+]
+len_mid = min(max_len, max([len(t)+2 for t in texts]))
+with torch.no_grad():
+    outputs = model(**tokenizer(texts, padding=True, max_length=len_mid,
+                                return_tensors="pt").to(device))
+def get_errors(source, target):
+    """   极简方法获取 errors   """
+    len_min = min(len(source), len(target))
+    errors = []
+    for idx in range(len_min):
+        if source[idx] != target[idx]:
+            errors.append([source[idx], target[idx], idx])
+    return errors
+result = []
+for probs, source in zip(outputs.logits, texts):
+    ids = torch.argmax(probs, dim=-1)
+    tokens_space = tokenizer.decode(ids[1:-1], skip_special_tokens=False)
+    text_new = tokens_space.replace(" ", "")
+    target = text_new[:len(source)]
+    errors = get_errors(source, target)
+    print(source, " => ", target, errors)
+    result.append([target, errors])
+print(result)
+"""
+机七学习是人工智能领遇最能体现智能的一个分知  =>  机器学习是人工智能领域最能体现智能的一个分支 [['七', '器', 1], ['遇', '域', 10], ['知', '支', 21]]
+我是练习时长两念半的鸽仁练习生蔡徐坤  =>  我是练习时长两年半的个人练习生蔡徐坤 [['念', '年', 7], ['鸽', '个', 10], ['仁', '人', 11]]
+真麻烦你了。希望你们好好的跳无  =>  真麻烦你了。希望你们好好地跳舞 [['的', '地', 12], ['无', '舞', 14]]
+他法语说的很好，的语也不错  =>  他法语说得很好，德语也不错 [['的', '得', 4], ['的', '德', 8]]
+遇到一位很棒的奴生跟我疗天  =>  遇到一位很棒的女生跟我聊天 [['奴', '女', 7], ['疗', '聊', 11]]
+我们为这个目标努力不解  =>  我们为这个目标努力不懈 [['解', '懈', 10]]
+"""
+```
+## 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,
+    url = {https://github.com/yongzhuo/macro-correct},
+    author = {Yongzhuo Mo},
+    title = {macro-correct},
+    year = {2025}
+```