{"id":13672501,"url":"https://github.com/DA-southampton/NLP_ability","last_synced_at":"2025-04-27T22:32:27.256Z","repository":{"id":37360893,"uuid":"272599346","full_name":"DA-southampton/NLP_ability","owner":"DA-southampton","description":"总结梳理自然语言处理工程师(NLP)需要积累的各方面知识，包括面试题，各种基础知识，工程能力等等，提升核心竞争力","archived":false,"fork":false,"pushed_at":"2022-08-24T16:54:12.000Z","size":24449,"stargazers_count":7238,"open_issues_count":1,"forks_count":1201,"subscribers_count":106,"default_branch":"master","last_synced_at":"2025-04-11T04:57:28.032Z","etag":null,"topics":[],"latest_commit_sha":null,"homepage":"","language":"Python","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/DA-southampton.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null}},"created_at":"2020-06-16T03:20:02.000Z","updated_at":"2025-04-11T03:22:14.000Z","dependencies_parsed_at":"2022-07-06T11:11:05.156Z","dependency_job_id":null,"html_url":"https://github.com/DA-southampton/NLP_ability","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/DA-southampton%2FNLP_ability","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/DA-southampton%2FNLP_ability/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/DA-southampton%2FNLP_ability/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/DA-southampton%2FNLP_ability/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/DA-southampton","download_url":"https://codeload.github.com/DA-southampton/NLP_ability/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":251219601,"owners_count":21554444,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":[],"created_at":"2024-08-02T09:01:37.382Z","updated_at":"2025-04-27T22:32:22.220Z","avatar_url":"https://github.com/DA-southampton.png","language":"Python","readme":"# 背景介绍\n\nNLP日常工作经验和论文解析，包含：预训练模型，文本表征，文本相似度，文本分类，多模态，知识蒸馏，词向量。\n\n我觉得NLP是一个值得深耕的领域，所以希望可以不停的提升自己核心竞争力和自己的段位！\n\n微信公众号：DASOU\n\n## 深度学习自然语言处理\n\n### Transformer\n\n1. [史上最全Transformer面试题](./深度学习自然语言处理/Transformer/史上最全Transformer面试题.md)\n2. [答案解析(1)-史上最全Transformer面试题](./深度学习自然语言处理/Transformer/答案解析(1)—史上最全Transformer面试题：灵魂20问帮你彻底搞定Transformer.md) \n3. [Pytorch代码分析--如何让Bert在finetune小数据集时更“稳”一点](./深度学习自然语言处理/Bert/Pytorch代码分析-如何让Bert在finetune小数据集时更“稳”一点.md)\n4. [解决老大难问题-如何一行代码带你随心所欲重新初始化bert的某些参数(附Pytorch代码详细解读)](https://github.com/DA-southampton/NLP_ability/blob/master/%E6%B7%B1%E5%BA%A6%E5%AD%A6%E4%B9%A0%E8%87%AA%E7%84%B6%E8%AF%AD%E8%A8%80%E5%A4%84%E7%90%86/Bert/%E8%A7%A3%E5%86%B3%E8%80%81%E5%A4%A7%E9%9A%BE%E9%97%AE%E9%A2%98-%E5%A6%82%E4%BD%95%E4%B8%80%E8%A1%8C%E4%BB%A3%E7%A0%81%E5%B8%A6%E4%BD%A0%E9%9A%8F%E5%BF%83%E6%89%80%E6%AC%B2%E9%87%8D%E6%96%B0%E5%88%9D%E5%A7%8B%E5%8C%96bert%E7%9A%84%E6%9F%90%E4%BA%9B%E5%8F%82%E6%95%B0(%E9%99%84Pytorch%E4%BB%A3%E7%A0%81).md)\n5. [3分钟从零解读Transformer的Encoder](https://github.com/DA-southampton/NLP_ability/blob/master/%E6%B7%B1%E5%BA%A6%E5%AD%A6%E4%B9%A0%E8%87%AA%E7%84%B6%E8%AF%AD%E8%A8%80%E5%A4%84%E7%90%86/Transformer/3%E5%88%86%E9%92%9F%E4%BB%8E%E9%9B%B6%E8%A7%A3%E8%AF%BBTransformer%E7%9A%84Encoder.md)\n6. [原版Transformer的位置编码究竟有没有包含相对位置信息](https://github.com/DA-southampton/NLP_ability/blob/master/%E6%B7%B1%E5%BA%A6%E5%AD%A6%E4%B9%A0%E8%87%AA%E7%84%B6%E8%AF%AD%E8%A8%80%E5%A4%84%E7%90%86/Transformer/%E5%8E%9F%E7%89%88Transformer%E7%9A%84%E4%BD%8D%E7%BD%AE%E7%BC%96%E7%A0%81%E7%A9%B6%E7%AB%9F%E6%9C%89%E6%B2%A1%E6%9C%89%E5%8C%85%E5%90%AB%E7%9B%B8%E5%AF%B9%E4%BD%8D%E7%BD%AE%E4%BF%A1%E6%81%AF.md)\n7. [BN踩坑记--谈一下Batch Normalization的优缺点和适用场景](https://github.com/DA-southampton/NLP_ability/blob/master/%E6%B7%B1%E5%BA%A6%E5%AD%A6%E4%B9%A0%E8%87%AA%E7%84%B6%E8%AF%AD%E8%A8%80%E5%A4%84%E7%90%86/Transformer/BN%E8%B8%A9%E5%9D%91%E8%AE%B0--%E8%B0%88%E4%B8%80%E4%B8%8BBatch%20Normalization%E7%9A%84%E4%BC%98%E7%BC%BA%E7%82%B9%E5%92%8C%E9%80%82%E7%94%A8%E5%9C%BA%E6%99%AF.md)\n8. [谈一下相对位置编码](https://github.com/DA-southampton/NLP_ability/blob/master/%E6%B7%B1%E5%BA%A6%E5%AD%A6%E4%B9%A0%E8%87%AA%E7%84%B6%E8%AF%AD%E8%A8%80%E5%A4%84%E7%90%86/Transformer/%E8%B0%88%E4%B8%80%E4%B8%8B%E7%9B%B8%E5%AF%B9%E4%BD%8D%E7%BD%AE%E7%BC%96%E7%A0%81.md)\n9. [NLP任务中-layer-norm比BatchNorm好在哪里](https://github.com/DA-southampton/NLP_ability/blob/master/%E6%B7%B1%E5%BA%A6%E5%AD%A6%E4%B9%A0%E8%87%AA%E7%84%B6%E8%AF%AD%E8%A8%80%E5%A4%84%E7%90%86/Transformer/NLP%E4%BB%BB%E5%8A%A1%E4%B8%AD-layer-norm%E6%AF%94BatchNorm%E5%A5%BD%E5%9C%A8%E5%93%AA%E9%87%8C.md)\n10. [谈一谈Decoder模块](https://github.com/DA-southampton/NLP_ability/blob/master/%E6%B7%B1%E5%BA%A6%E5%AD%A6%E4%B9%A0%E8%87%AA%E7%84%B6%E8%AF%AD%E8%A8%80%E5%A4%84%E7%90%86/Transformer/%E8%B0%88%E4%B8%80%E8%B0%88Decoder%E6%A8%A1%E5%9D%97.md)\n11. [Transformer的并行化](https://github.com/DA-southampton/NLP_ability/blob/master/%E6%B7%B1%E5%BA%A6%E5%AD%A6%E4%B9%A0%E8%87%AA%E7%84%B6%E8%AF%AD%E8%A8%80%E5%A4%84%E7%90%86/Transformer/Transformer%E7%9A%84%E5%B9%B6%E8%A1%8C%E5%8C%96.md)\n12. [Transformer全部文章合辑](https://github.com/DA-southampton/NLP_ability/blob/master/%E6%B7%B1%E5%BA%A6%E5%AD%A6%E4%B9%A0%E8%87%AA%E7%84%B6%E8%AF%AD%E8%A8%80%E5%A4%84%E7%90%86/Transformer/%E7%AD%94%E6%A1%88%E5%90%88%E8%BE%91.md)\n13. [RNN的梯度消失有什么与众不同的地方.md](./深度学习自然语言处理/其他/RNN的梯度消失有什么与众不同的地方.md)\n14. [VIT-如何将Transformer更好的应用到CV领域](./深度学习自然语言处理/Transformer/VIT-如何将Transformer更好的应用到CV领域.md)\n\n### Bert-基本知识\n\n1. [FastBERT-CPU推理加速10倍](./深度学习自然语言处理/Bert/FastBert.md)\n6. [RoBERTa：更多更大更强](./深度学习自然语言处理/Bert/RoBERTa.md)\n7. [为什么Bert做不好无监督语义匹配](./深度学习自然语言处理/Bert/为什么Bert做不好无监督语义匹配.md)\n8. [UniLM:为Bert插上文本生成的翅膀](./深度学习自然语言处理/Bert/UniLM.md)\n9. [tBERT-BERT融合主题模型做文本匹配](./深度学习自然语言处理/Bert/tBERT-BERT融合主题模型.md)\n10. [XLNET模型从零解读](./深度学习自然语言处理/Bert/XLNET.md)\n11. [如何在脱敏数据中使用BERT等预训练模型](./深度学习自然语言处理/BERT/如何在脱敏数据中使用BERT等预训练模型.md)\n\n### Bert-知识蒸馏\n\n1. [什么是知识蒸馏](./深度学习自然语言处理/模型蒸馏/什么是知识蒸馏.md)\n2. [如何让 TextCNN 逼近 Bert](./深度学习自然语言处理/模型蒸馏/bert2textcnn模型蒸馏.md)\n3. [Bert蒸馏到简单网络lstm](./深度学习自然语言处理/模型蒸馏/Bert蒸馏到简单网络lstm.md)\n4. [PKD-Bert基于多层的知识蒸馏方式](./深度学习自然语言处理/模型蒸馏/PKD-Bert基于多层的知识蒸馏方式.md)\n5. [BERT-of-Theseus-模块压缩交替训练](./深度学习自然语言处理/模型蒸馏/Theseus-模块压缩交替训练.md)\n6. [tinybert-全方位蒸馏](./深度学习自然语言处理/模型蒸馏/tinybert-全方位蒸馏.md)\n7. [ALBERT：更小更少但并不快](./深度学习自然语言处理/模型蒸馏/ALBERT-更小更少但并不快.md)\n8. [BERT知识蒸馏代码解析-如何写好损失函数](./深度学习自然语言处理/模型蒸馏/BERT知识蒸馏代码解析-如何写好损失函数.md)\n9. [知识蒸馏综述万字长文](./深度学习自然语言处理/模型蒸馏/知识蒸馏综述万字长文.md)\n\n### 词向量-word embedding\n\n1. [史上最全词向量面试题-Word2vec/fasttext/glove/Elmo](https://github.com/DA-southampton/NLP_ability/blob/master/%E6%B7%B1%E5%BA%A6%E5%AD%A6%E4%B9%A0%E8%87%AA%E7%84%B6%E8%AF%AD%E8%A8%80%E5%A4%84%E7%90%86/%E8%AF%8D%E5%90%91%E9%87%8F/%E5%8F%B2%E4%B8%8A%E6%9C%80%E5%85%A8%E8%AF%8D%E5%90%91%E9%87%8F%E9%9D%A2%E8%AF%95%E9%A2%98%E6%A2%B3%E7%90%86.md)\n\n- Word2vec\n\n1. [Word2vec两种训练模型详细解读-一个词经过模型训练可以获得几个词向量](https://github.com/DA-southampton/NLP_ability/blob/master/%E6%B7%B1%E5%BA%A6%E5%AD%A6%E4%B9%A0%E8%87%AA%E7%84%B6%E8%AF%AD%E8%A8%80%E5%A4%84%E7%90%86/%E8%AF%8D%E5%90%91%E9%87%8F/%E8%81%8A%E4%B8%80%E4%B8%8BWord2vec-%E6%A8%A1%E5%9E%8B%E7%AF%87.md)\n2. [Word2vec两种优化方式细节详细解读](https://github.com/DA-southampton/NLP_ability/blob/master/%E6%B7%B1%E5%BA%A6%E5%AD%A6%E4%B9%A0%E8%87%AA%E7%84%B6%E8%AF%AD%E8%A8%80%E5%A4%84%E7%90%86/%E8%AF%8D%E5%90%91%E9%87%8F/%E8%81%8A%E4%B8%80%E4%B8%8BWord2vec-%E8%AE%AD%E7%BB%83%E4%BC%98%E5%8C%96%E7%AF%87.md)\n3. [Word2vec-负采样和层序softmax与原模型是否等价](https://github.com/DA-southampton/NLP_ability/blob/master/%E6%B7%B1%E5%BA%A6%E5%AD%A6%E4%B9%A0%E8%87%AA%E7%84%B6%E8%AF%AD%E8%A8%80%E5%A4%84%E7%90%86/%E8%AF%8D%E5%90%91%E9%87%8F/word2vec%E4%B8%A4%E7%A7%8D%E4%BC%98%E5%8C%96%E6%96%B9%E5%BC%8F%E7%9A%84%E8%81%94%E7%B3%BB%E5%92%8C%E5%8C%BA%E5%88%AB.md)\n4. [Word2vec为何需要二次采样以及相关细节详细解读](https://github.com/DA-southampton/NLP_ability/blob/master/%E6%B7%B1%E5%BA%A6%E5%AD%A6%E4%B9%A0%E8%87%AA%E7%84%B6%E8%AF%AD%E8%A8%80%E5%A4%84%E7%90%86/%E8%AF%8D%E5%90%91%E9%87%8F/Word2vec%E4%B8%BA%E4%BB%80%E4%B9%88%E9%9C%80%E8%A6%81%E4%BA%8C%E6%AC%A1%E9%87%87%E6%A0%B7%EF%BC%9F.md)\n5. [Word2vec的负采样](https://github.com/DA-southampton/NLP_ability/blob/master/%E6%B7%B1%E5%BA%A6%E5%AD%A6%E4%B9%A0%E8%87%AA%E7%84%B6%E8%AF%AD%E8%A8%80%E5%A4%84%E7%90%86/%E8%AF%8D%E5%90%91%E9%87%8F/Word2vec%E7%9A%84%E8%B4%9F%E9%87%87%E6%A0%B7.md) \n6. [Word2vec模型究竟是如何获得词向量的](https://github.com/DA-southampton/NLP_ability/blob/master/%E6%B7%B1%E5%BA%A6%E5%AD%A6%E4%B9%A0%E8%87%AA%E7%84%B6%E8%AF%AD%E8%A8%80%E5%A4%84%E7%90%86/%E8%AF%8D%E5%90%91%E9%87%8F/Word2vec%E6%A8%A1%E5%9E%8B%E7%A9%B6%E7%AB%9F%E6%98%AF%E5%A6%82%E4%BD%95%E8%8E%B7%E5%BE%97%E8%AF%8D%E5%90%91%E9%87%8F%E7%9A%84.md) \n7. [Word2vec训练参数的选定](https://github.com/DA-southampton/NLP_ability/blob/master/%E6%B7%B1%E5%BA%A6%E5%AD%A6%E4%B9%A0%E8%87%AA%E7%84%B6%E8%AF%AD%E8%A8%80%E5%A4%84%E7%90%86/%E8%AF%8D%E5%90%91%E9%87%8F/Word2vec%E8%AE%AD%E7%BB%83%E5%8F%82%E6%95%B0%E7%9A%84%E9%80%89%E5%AE%9A.md) \n8. [CBOW和skip-gram相较而言，彼此相对适合哪些场景.md](https://github.com/DA-southampton/NLP_ability/blob/master/%E6%B7%B1%E5%BA%A6%E5%AD%A6%E4%B9%A0%E8%87%AA%E7%84%B6%E8%AF%AD%E8%A8%80%E5%A4%84%E7%90%86/%E8%AF%8D%E5%90%91%E9%87%8F/CBOW%E5%92%8Cskip-gram%E7%9B%B8%E8%BE%83%E8%80%8C%E8%A8%80%EF%BC%8C%E5%BD%BC%E6%AD%A4%E7%9B%B8%E5%AF%B9%E9%80%82%E5%90%88%E5%93%AA%E4%BA%9B%E5%9C%BA%E6%99%AF.md) \n\n- Fasttext/Glove\n\n1. [Fasttext详解解读(1)-文本分类](https://github.com/DA-southampton/NLP_ability/blob/master/%E6%B7%B1%E5%BA%A6%E5%AD%A6%E4%B9%A0%E8%87%AA%E7%84%B6%E8%AF%AD%E8%A8%80%E5%A4%84%E7%90%86/%E8%AF%8D%E5%90%91%E9%87%8F/Fasttext%E8%A7%A3%E8%AF%BB(1).md)\n2. [Fasttext详解解读(2)-训练词向量](https://github.com/DA-southampton/NLP_ability/blob/master/%E6%B7%B1%E5%BA%A6%E5%AD%A6%E4%B9%A0%E8%87%AA%E7%84%B6%E8%AF%AD%E8%A8%80%E5%A4%84%E7%90%86/%E8%AF%8D%E5%90%91%E9%87%8F/Fasttext%E8%A7%A3%E8%AF%BB(2).md)\n3. [GLove细节详细解读](https://github.com/DA-southampton/NLP_ability/blob/master/%E6%B7%B1%E5%BA%A6%E5%AD%A6%E4%B9%A0%E8%87%AA%E7%84%B6%E8%AF%AD%E8%A8%80%E5%A4%84%E7%90%86/%E8%AF%8D%E5%90%91%E9%87%8F/%E8%81%8A%E4%B8%80%E4%B8%8BGlove.md) \n\n### 多模态\n\n1. [多模态之ViLBERT：双流网络，各自为王](./深度学习自然语言处理/多模态/多模态之ViLBERT：双流网络，各自为王.md)\n2. [复盘多模态任务落地的六大问题](./深度学习自然语言处理/多模态/复盘多模态需要解决的6个问题.md)\n3. [如何将多模态数据融入到BERT架构中-多模态BERT的两类预训练任务](./深度学习自然语言处理/多模态/如何将多模态数据融入到BERT架构中-多模态BERT的两类预训练任务.md)\n1. [层次分类体系的必要性-多模态讲解系列(1)](./深度学习自然语言处理/多模态/层次分类体系的必要性-多模态讲解系列.md)\n2. [文本和图像特征表示模块详解-多模态讲解系列(2)](深度学习自然语言处理/多模态/文本和图像特征表示模块详解-多模态讲解系列.md) \n7. [多模态中各种Fusion方式汇总](./深度学习自然语言处理/多模态/多模态中各种Fusion方式汇总.md ) \n\n\n###  句向量-sentence embedding\n\n\n1. [句向量模型综述](https://github.com/DA-southampton/NLP_ability/blob/master/%E6%B7%B1%E5%BA%A6%E5%AD%A6%E4%B9%A0%E8%87%AA%E7%84%B6%E8%AF%AD%E8%A8%80%E5%A4%84%E7%90%86/%E5%8F%A5%E5%90%91%E9%87%8F/%E5%8F%A5%E5%90%91%E9%87%8F%E6%A8%A1%E5%9E%8B%E7%BB%BC%E8%BF%B0.md) \n\n\n### 文本相似度\n\n1. [五千字全面梳理文本相似度/文本匹配模型](./深度学习自然语言处理/文本匹配和文本相似度/五千字全面梳理文本相似度和文本匹配模型.md)\n2. [如何又好又快的做文本匹配-ESIM模型](./深度学习自然语言处理/文本匹配和文本相似度/ESIM.md)\n3. [阿里RE2-将残差连接和文本匹配模型融合.md](./深度学习自然语言处理/文本匹配和文本相似度/阿里RE2-将残差连接和文本匹配模型融合.md)\n4. [聊一下孪生网络和DSSM的混淆点以及向量召回的一个细节](./深度学习自然语言处理/文本匹配和文本相似度/聊一下孪生网络和DSSM的混淆点以及向量召回的一个细节.md)\n5. [DSSM论文-公司实战文章](./深度学习自然语言处理/文本匹配和文本相似度/DSSM论文-公司实战文章.md)\n6. [bert白化简单的梳理:公式推导+PCA\u0026SVD+代码解读](./深度学习自然语言处理/文本匹配和文本相似度/bert白化简单的梳理.md)\n7. [SIMCSE论文解析](./深度学习自然语言处理/文本匹配和文本相似度/SIMCSE论文解析.md)\n\n\n###  关键词提取\n\n1. [基于词典的正向/逆向最大匹配](./深度学习自然语言处理/关键词提取/中文分词/基于词典的正向最大匹配和逆向最大匹配中文分词.md)\n2. [实体库构建：大规模离线新词实体挖掘](https://github.com/DA-southampton/NLP_ability/blob/master/%E6%B7%B1%E5%BA%A6%E5%AD%A6%E4%B9%A0%E8%87%AA%E7%84%B6%E8%AF%AD%E8%A8%80%E5%A4%84%E7%90%86/%E5%85%B3%E9%94%AE%E8%AF%8D%E6%8F%90%E5%8F%96/%E5%AE%9E%E4%BD%93%E5%BA%93%E6%9E%84%E5%BB%BA%EF%BC%9A%E5%A4%A7%E8%A7%84%E6%A8%A1%E7%A6%BB%E7%BA%BF%E6%96%B0%E8%AF%8D%E5%AE%9E%E4%BD%93%E6%8C%96%E6%8E%98.md)\n3. [聊一聊NLPer如何做关键词抽取](https://github.com/DA-southampton/NLP_ability/blob/master/%E6%B7%B1%E5%BA%A6%E5%AD%A6%E4%B9%A0%E8%87%AA%E7%84%B6%E8%AF%AD%E8%A8%80%E5%A4%84%E7%90%86/%E5%85%B3%E9%94%AE%E8%AF%8D%E6%8F%90%E5%8F%96/%E5%85%B3%E9%94%AE%E8%AF%8D%E6%8F%90%E5%8F%96%E6%96%B9%E6%B3%95%E7%BB%BC%E8%BF%B0.md)\n\n###  命名体识别\n\n1. [命名体识别资源梳理(代码+博客讲解)](https://github.com/DA-southampton/NLP_ability/blob/master/%E6%B7%B1%E5%BA%A6%E5%AD%A6%E4%B9%A0%E8%87%AA%E7%84%B6%E8%AF%AD%E8%A8%80%E5%A4%84%E7%90%86/%E5%91%BD%E5%90%8D%E4%BD%93%E8%AF%86%E5%88%AB/%E5%91%BD%E5%90%8D%E4%BD%93%E8%AF%86%E5%88%AB%E8%B5%84%E6%BA%90%E6%A2%B3%E7%90%86(%E4%BB%A3%E7%A0%81%2B%E5%8D%9A%E5%AE%A2%E8%AE%B2%E8%A7%A3).md)\n2. [HMM/CRF 详细解读](./深度学习自然语言处理/命名体识别/HMM_CRF.md) \n3. [工业级命名体识别的做法](https://github.com/DA-southampton/NLP_ability/blob/master/%E6%B7%B1%E5%BA%A6%E5%AD%A6%E4%B9%A0%E8%87%AA%E7%84%B6%E8%AF%AD%E8%A8%80%E5%A4%84%E7%90%86/%E5%91%BD%E5%90%8D%E4%BD%93%E8%AF%86%E5%88%AB/%E5%B7%A5%E4%B8%9A%E7%BA%A7%E5%91%BD%E5%90%8D%E4%BD%93%E8%AF%86%E5%88%AB%E7%9A%84%E5%81%9A%E6%B3%95.md)     \n4. [词典匹配+模型预测-实体识别两大法宝](https://github.com/DA-southampton/NLP_ability/blob/master/%E6%B7%B1%E5%BA%A6%E5%AD%A6%E4%B9%A0%E8%87%AA%E7%84%B6%E8%AF%AD%E8%A8%80%E5%A4%84%E7%90%86/%E5%91%BD%E5%90%8D%E4%BD%93%E8%AF%86%E5%88%AB/%E8%AF%8D%E5%85%B8%E5%8C%B9%E9%85%8D%2B%E6%A8%A1%E5%9E%8B%E9%A2%84%E6%B5%8B-%E5%AE%9E%E4%BD%93%E8%AF%86%E5%88%AB%E4%B8%A4%E5%A4%A7%E6%B3%95%E5%AE%9D.md)\n\n5. [autoner+fuzzy-CRF-使用领域词典做命名体识别](./深度学习自然语言处理/命名体识别/autoner.md)\n\n6. [FLAT-Transformer-词典+Transformer融合词汇信息](./深度学习自然语言处理/命名体识别/FLAT-Transformer.md)--公众号\n\n7. [TENER-复旦为什么TRM在NER上效果差.md](./深度学习自然语言处理/命名体识别/TNER-复旦为什么TRM在NER上效果差.md)\n\n###  文本分类\n\n1. [TextCNN论文详细解读](./深度学习自然语言处理/文本分类/CNN文本分类解读.md) \n2. [只使用标签名称就可以文本分类.md ](./深度学习自然语言处理/文本分类/只使用标签名称就可以文本分类.md )\n3. [半监督入门思想之伪标签](./深度学习自然语言处理/文本分类/半监督入门思想之伪标签.md)\n4. [ACL2020-多任务负监督方式增加CLS表达差异性](./深度学习自然语言处理/文本分类/ACL2020-多任务负监督方式增加CLS表达差异性.md)\n5. [Bert在文本分类任务上微调](./深度学习自然语言处理/文本分类/在文本分类上微调Bert.md)\n6. [UDA-Unsupervised Data Augmentation for Consistency Training-半监督集大成](./深度学习自然语言处理/文本分类/UDA.md)\n7. [LCM-缓解标签不独立以及标注错误的问题](./深度学习自然语言处理/文本分类/LCM-缓解标签不独立以及标注错误的问题.md)\n8. [关键词信息如何融入到文本分类任务中](./深度学习自然语言处理/文本分类/关键词信息如何融入到文本分类任务中.md)\n\n### 对比学习\n\n1. [Moco论文解析](./深度学习自然语言处理/对比学习/Moco1论文解析.md)","funding_links":[],"categories":["Python","📚 Project Purpose"],"sub_categories":["Machine Learning (Interview-Level"],"project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2FDA-southampton%2FNLP_ability","html_url":"https://awesome.ecosyste.ms/projects/github.com%2FDA-southampton%2FNLP_ability","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2FDA-southampton%2FNLP_ability/lists"}