Leveraging BERT and c-TF-IDF to create easily interpretable topics. - GitHub - MaartenGr/BERTopic: Leveraging BERT and c-TF-IDF to create easily interpretable topics.
这是B站视频课程通俗易懂的BERTopic主题模型教程(可代替LDA、DTM),39集全套教程对应的完整笔记、数据、代码。详细视频教程请点击链接查看。 ⭐ 模板代码是main.ipynb,下载该项目,按视频课程配置好环境后,该文件中的代码可一键运行,提升科研效率!!!⭐ 安装依赖 & 运行代码: 整个教程基于Python 3.10.x版本(具体到...
https://github.com/ymcui/Chinese-BERT-wwm 五、词向量:使用SentenceTransformers (一)理解:我们要做什么 前面我们学习了通过bert-base-chinese,还有哈工大模型生成词向量 这里我们要学习另外一个库:SentenceTransformers,它也可以将一个句子转换成词向量 其实SentenceTransformers是bertopic默认使用的文本转词向量工具,之...
Leveraging BERT and c-TF-IDF to create easily interpretable topics. - GitHub - yh-tokushima/BERTopic: Leveraging BERT and c-TF-IDF to create easily interpretable topics.
Leveraging BERT and c-TF-IDF to create easily interpretable topics. - GitHub - forensic-architecture/BERTopic: Leveraging BERT and c-TF-IDF to create easily interpretable topics.
[project.urls] Documentation = "https://maartengr.github.io/BERTopic/" Homepage = "https://github.com/MaartenGr/BERTopic" Issues = "https://github.com/MaartenGr/BERTopic/issues" Repository = "https://github.com/MaartenGr/BERTopic.git" [tool.setuptools.packages.find] include = ["ber...
Contribute to zwgu/BERTopic-Tutorial development by creating an account on GitHub.
[supervised](https://maartengr.github.io/BERTopic/getting_started/supervised/supervised.html), [semi-supervised](https://maartengr.github.io/BERTopic/getting_started/semisupervised/semisupervised.html) and [manual](https://maartengr.github.io/BERTopic/getting_started/manual/manual.html) topic ...
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BERTopic 中文使用範例. Contribute to Aidenzich/HelloBERTopic development by creating an account on GitHub.