Medical Search Engine with SPLADE + Sentence Transformers in Python
Build a medical search engine using hybrid search with SPLADE and sentence transformers. Learn to combine dense and sparse vectors for improved semantic and keyword search capabilities in medical question-answering.
Document Similarity: Langchain Sentence Transformers and Chroma - Spanish
Aprende a clasificar documentos usando Langchain con Sentence Transformers de HuggingFace y Chromadb. Explora técnicas avanzadas de procesamiento de lenguaje natural y similitud documental.
Recent Developments in Neural Search
Explore cutting-edge neural methods for semantic search, improving text search quality with pre-trained transformer networks and advanced deployment techniques.
LLama 2 and PEFT Documentation - Building Interactive LLM with Retrieval Augmented Generation
Master building a RAG-enhanced LLM system using Llama 2 through hands-on coding. Learn web scraping, embedding generation, vector indexing, and semantic search to create an AI that leverages external knowledge sources.
Train a Domain-Specific BERT Tokenizer Using SBERT - Part 50
Master BERT tokenizer training for scientific domains using SBERT, focusing on byte-pair encoding and specialized pre-training for complex domain-specific knowledge like bio-pharmacological content.
Feature Vectors - Understanding BERT and SBERT Transformer Models
Dive into feature vectors and their crucial role in BERT and SBERT transformer models, exploring vectorization techniques, Python implementation, and practical applications in NLP.
SETFIT Hyperparameter Optimization for SBERT Text Classification - Part 45
Master SETFIT hyperparameter optimization techniques for efficient text classification using Sentence Transformers, focusing on contrastive training and automated parameter tuning for optimal performance.
SetFit with SBERT for Few-Shot Text Classification - Multi-Class and Multi-Label
Master SetFit and SBERT techniques for efficient few-shot text classification, exploring contrastive training, dense embeddings, and classification head implementation for both multi-class and multi-label scenarios.
Pre-Training BERT from Scratch for Domain-Specific Knowledge Using PyTorch - Part 51
Master BERT model pre-training from scratch using PyTorch, focusing on domain-specific data. Build custom tokenizers, design architecture, and implement masked language modeling for specialized neural information retrieval systems.
Cohere Embed V3: Innovations in Content Quality and Compressed Embeddings
Discover state-of-the-art embedding models featuring content quality assessment and vector compression techniques, with practical insights on implementation and scaling in vector database setups.
Semantic Search for AI - Testing Out Qdrant Neural Search
Explore semantic search implementation using Qdrant, covering Docker setup, data cleaning, microservice creation, and leveraging sentence embeddings for improved AI-powered search capabilities.
How to Use OpenAI Whisper to Fix YouTube Search
Learn to enhance YouTube search using OpenAI Whisper, transformers, and vector search. Build a system for precise content retrieval, transcription, and question-answering from YouTube videos.
Spotify's Podcast Search Explained
Explore Spotify's innovative podcast search system using natural language processing and learn to implement a similar solution with transformer models and vector search techniques.
Is GPL the Future of Sentence Transformers - Generative Pseudo-Labeling Deep Dive
Explore Generative Pseudo-Labeling for training high-performance sentence transformers using unlabeled text data. Learn implementation techniques, potential applications, and future implications for NLP and semantic search.
Train Sentence Transformers by Generating Queries - GenQ
Learn to train sentence transformers using GenQ, a technique that generates synthetic queries for effective bi-encoder models in semantic search, with practical code walkthroughs and implementation insights.
Fine-Tune High Performance Sentence Transformers With Multiple Negatives Ranking
Learn to fine-tune sentence transformers using multiple negatives ranking loss, improving embedding quality for similarity prediction and outperforming SBERT. Includes PyTorch and sentence-transformers library implementations.
Fine-Tune Sentence Transformers the OG Way - With NLI Softmax Loss
Explore fine-tuning sentence transformers using NLI softmax loss. Learn preprocessing, PyTorch implementation, and Sentence-Transformers library usage for improved sentence embeddings in NLP applications.
NER Powered Semantic Search in Python
Learn to implement NER-powered semantic search in Python using Transformers, Sentence Transformers, and Pinecone. Explore dataset preparation, entity creation, embedding generation, and querying techniques for efficient information retrieval.
Llama 3 8B - Building Local AI Agents with Function Calling - Full Tutorial
Learn to build AI agents with Llama 3 8B, exploring function calling, tool creation, and customization. Hands-on tutorial for developing powerful local language models with practical applications.
AugSBERT: Domain Transfer for Sentence Transformers
Learn to transfer knowledge from out-of-domain datasets to your target domain using AugSBERT. Optimize language models with efficient data selection and domain-transfer training strategies for improved performance.
Representation is King: The Journey to Quality Dialog Embeddings
Explore sentence embeddings in dialog systems, focusing on classification. Learn to fine-tune top-performing small-sized sentence transformers using SentenceTransformers framework with practical examples.