This page details the command-line utility for the Embedding Atlas, a tool for exploring large text datasets with metadata. It covers installation, data loading (local and Hugging Face), visualization of embeddings using SentenceTransformers and UMAP, and usage instructions with available options.
Multi-class zero-shot embedding classification and error checking. This project improves zero-shot image/text classification using a novel dimensionality reduction technique and pairwise comparison, resulting in increased agreement between text and image classifications.
A post with pithy observations and clear conclusions from building complex LLM workflows, covering topics like prompt chaining, data structuring, model limitations, and fine-tuning strategies.
This article details the often overlooked cost of storing embeddings for RAG systems, and how quantization techniques (int8 and binary) can significantly reduce storage requirements and improve retrieval speed without substantial accuracy loss.
Ryan speaks with Edo Liberty, Founder and CEO of Pinecone, about building vector databases, the power of embeddings, the evolution of RAG, and fine-tuning AI models.
This Space demonstrates a simple method for embedding text using a LLM (Large Language Model) via the Hugging Face Inference API. It showcases how to convert text into numerical vector representations, useful for semantic search and similarity comparisons.
This tutorial demonstrates how to build a powerful document search engine using Hugging Face embeddings, Chroma DB, and Langchain for semantic search capabilities.
Qodo-Embed-1-1.5B is a state-of-the-art code embedding model designed for retrieval tasks in the software development domain. It supports multiple programming languages and is optimized for natural language-to-code and code-to-code retrieval, making it highly effective for applications such as code search and retrieval-augmented generation.
Qodo releases Qodo-Embed-1-1.5B, an open-source code embedding model that outperforms competitors from OpenAI and Salesforce, enhancing code search, retrieval, and understanding for enterprise development teams.
This article provides a comprehensive guide on the basics of BERT (Bidirectional Encoder Representations from Transformers) models. It covers the architecture, use cases, and practical implementations, helping readers understand how to leverage BERT for natural language processing tasks.
