Sentence Transformers Courses and Certifications

James Briggs

Intro to Sentence Embeddings with Transformers

Learn how sentence transformers create embeddings for semantic similarity, with a Python implementation using transformer models and cosine similarity.

• YouTube
• 31 minutes
• Self-Paced
• Free Video

James Briggs

Today Unsupervised Sentence Transformers, Tomorrow Skynet - How TSDAE Works

Learn to fine-tune sentence transformers without labeled data using TSDAE, from sentence preparation and model initialization through training and evaluation.

• YouTube
• 44 minutes
• Self-Paced
• Free Video

James Briggs

Is GPL the Future of Sentence Transformers - Generative Pseudo-Labeling Deep Dive

Learn how generative pseudo-labeling turns unstructured text into training data for fine-tuning sentence transformers.

• YouTube
• 53 minutes
• Self-Paced
• Free Video

James Briggs

Train Sentence Transformers by Generating Queries - GenQ

Learn to fine-tune sentence-transformer bi-encoders with synthetically generated queries from unstructured text for asymmetric semantic search.

• YouTube
• 27 minutes
• Self-Paced
• Free Video

James Briggs

Fine-Tune High Performance Sentence Transformers With Multiple Negatives Ranking

Learn to fine-tune sentence transformers with multiple negatives ranking loss, from NLI pair preprocessing through PyTorch and Sentence Transformers implementations.

• YouTube
• 37 minutes
• Self-Paced
• Free Video

James Briggs

Fine-Tune Sentence Transformers the OG Way - With NLI Softmax Loss

Fine-tune Sentence-BERT to produce sentence embeddings with the original natural language inference approach using softmax loss.

• YouTube
• 37 minutes
• Self-Paced
• Free Video

James Briggs

AugSBERT: Domain Transfer for Sentence Transformers

Learn how AugSBERT transfers sentence-transformer knowledge from labeled source data to unlabeled target domains through cross-encoder labeling and bi-encoder training.

• YouTube
• 29 minutes
• Self-Paced
• Free Video

Qdrant - Vector Database & Search Engine

Fine-tuning Embedding Models: Sentence Transformers and Emoji Recognition

Discover how to fine-tune embedding models to accurately process emojis and emotional content while maintaining backward compatibility for enhanced social media analysis.

• YouTube
• 44 minutes
• Self-Paced
• Free Video

James Briggs

All You Need to Know on Multilingual Sentence Vectors - 1 Model, 50+ Languages

Learn to build multilingual sentence transformers by transferring knowledge across languages with parallel data and knowledge distillation.

• YouTube
• 40 minutes
• Self-Paced
• Free Video

James Briggs

Medical Search Engine with SPLADE + Sentence Transformers in Python

Build a medical question-answering search engine combining SPLADE sparse embeddings and Sentence Transformers dense embeddings.

• YouTube
• 31 minutes
• Self-Paced
• Free Video

AI Bites

Embeddings for RAG - Understanding BERT and Sentence Transformers

Explore embedding models for RAG applications, from basic transformers to advanced SBERT implementations, with practical insights into sentence transformers for effective data processing.

• YouTube
• 15 minutes
• Self-Paced
• Free Video

Edureka

Vector Indexing and Hybrid Search

Design reusable retrieval pipelines combining FAISS, dense semantic search, BM25 keyword ranking, hybrid retrieval, and metadata-aware filtering.

• Coursera
• 3 weeks, 2 hours a week
• Self-Paced
• Paid Course

Edureka

Vector Embeddings Fundamentals

Traces a reproducible path from Bag-of-Words vectors to Sentence Transformer embeddings and brute-force semantic search across product catalogues.

• Coursera
• 3 weeks, 2 hours a week
• Self-Paced
• Paid Course

James Briggs

Sentence Similarity With Transformers and PyTorch

Implement sentence semantic similarity in Python by mean-pooling BERT hidden states into vectors and comparing them with cosine similarity.

• YouTube
• 21 minutes
• Self-Paced
• Free Video

James Briggs

Vector Similarity Search and Faiss Course

Master vector similarity search techniques and Faiss implementation through hands-on practice with traditional methods, advanced indexing, and performance optimization strategies.

• YouTube
• 6 hours 16 minutes
• Self-Paced
• Free Video