SBERT TSDAE (Transformer based Denoising Auto Encoder): You want to code Sentence Transformers (based on BERT models) to extract semantic information on millions of documents? Here is your python code - with October 2021 updates. New pre-trained models of BERT transformer models and Sentence Transformer models on HuggingFace accelerate Sentence embedding for semantic content visualization of huge text documents. Learn to apply Sentence transformer models (BERT based) in real time coding. Free python coding sequences for Sentence Embedding in high dimensional topological spaces. "TSDAE: Using Transformer-based Sequential Denoising Auto-Encoder for Unsupervised Sentence Embedding Learning" by Kexin Wang, Nils Reimers, Iryna Gurevych https://arxiv.org/abs/2104.06979 See also: https://SBERT.net/ for in-depth documentation (recommended). SBERT & BERT Models available free-of-charge & open source on https://HuggingFace.co/ 00:00 Load Libs for SBERT 02:15 Choose Sentence Transformer model 03:53 On HuggingFace 07:30 Unsupervised Learning: SBERT TSDAE 09:29 Different Loss functions for SBERT 14:08 UMAP Dimensions 15:25 HDBSCAN clustering 17:08 3D Cluster visualization My JupyterLab of this YouTube video is available for demonstration purposes only via: https://gist.github.com/qcdquark/f2923e44c6a5660562f1d978a702cbf4 real-time coding. code in real time. Pytorch code examples of Sentence embedding. Pre-trained sentence transformer models on Huggingface. Code 3D sentence embeddings in vector spaces. #bert #sbert #machinelearningwithpython #datascience #deeplearning #ai #machinelearning #pytorch #pythonprogramming #documents #vectorspace #topologicalspace #nlproc #nlptechniques #nlp #embedding #sentence