Nomic Embed
Nomic AI
• Framework: PyTorchNomic Embed is an open-source text embedding model built with PyTorch and Apache 2.0 license. With support for up to 8192-token context length, it achieves state-of-the-art performance on tasks like semantic search and retrieval using benchmarks such as MTEB and LoCo. It fully open-source the model weights, training data, and code, making it ideal for production and research usage.
Nomic Embed AI Model
Model Performance Statistics
Views
Released
Last Checked
Version
- Semantic Search
- Clustering
- Parameter Count
- N/A
Dataset Used
BEIR, MassiveText
Related AI Models
Discover similar AI models that might interest you
E5-Mistral
E5-Mistral
Microsoft
E5-Mistral is an open-source embeddings model developed by Microsoft, released under the MIT license. Built with PyTorch, it generates high-quality vector representations useful for semantic search, information retrieval, and clustering tasks. E5-Mistral enables efficient and accurate AI applications requiring text similarity and understanding.
BGE v3
BGE v3
BAAI
BGE v3 is an open-source multilingual embedding model developed by BAAI, designed for retrieval-augmented generation (RAG), semantic search, and vector database applications. Achieving a 65.3 score on the MTEB leaderboard, BGE v3 supports over 100 languages and handles an 8K context window for long-document embedding. The model delivers performance comparable to OpenAI’s text-embedding models while being eight times smaller and optimized for 4-bit quantization, making it ideal for on-device and scalable vector search systems. BGE v3 helps developers build advanced semantic search engines, chatbot retrieval layers, and knowledge-grounded AI applications efficiently.
Emu2-Chat
Emu2-Chat
Beijing Academy of AI
Emu2-Chat is a conversational AI model designed for engaging and context-aware chat interactions. It is optimized for natural language understanding and generating human-like responses across various domains. Ideal for chatbots, virtual assistants, and customer support automation.