Real time code for SBERT Sentence Embedding in a vector space with SBERT Transformer models, Bi-encoder Transformer models! Learn SBERT Sentence Embedding: TSDAE, SimCSE and CT. With NEW pre-trained models best suited for your application. A) Add "SUPERVISED training data" to your SentenceTransformers to improve model performance. B) If you have NO labeled training data: Add "UNsupervised learning" to learn semantically meaningful sentence embedding from the text/sentences itself! For 3 models I show you coding examples for unsupervised learning: 1. TSDAE - Transformer-based Denoising AutoEncoder. 2. Simple Contrastive Learning of Sentence Embeddings (SimCSE). 3. Semantic Re-Tuning with Contrastive Tension (CT). Suitable for beginners to sentence embedding. Overview of sentence embedding in vector space. Sentence embedding with code examples. Code sentence embedding in real time. Source: https://www.sbert.net/index.html "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 #sbert #deeplearning #datascience #vocabulary #nlproc #datascience #dataanalytics #nlptechniques #clustering #semantic #bert #3danimation #3dvisualization #topologicalspace #machinelearningwithpython #pytorch #sentence #embedding #sentence #embedding #complex #umap #insight #algebraic_topology #code_your_own_AI #SentenceTransformers #code #code_in_real_time Sentence embedding with unsupervised training. Improve model performance with Transformer-based Denoising AutoEncoders.