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  • nomic-ai nomic-embed-text-v1 - Hugging Face
    nomic-embed-text-v1 is 8192 context length text encoder that surpasses OpenAI text-embedding-ada-002 and text-embedding-3-small performance on short and long context tasks
  • nomic-embed-text - Ollama
    nomic-embed-text is a large context length text encoder that surpasses OpenAI text-embedding-ada-002 and text-embedding-3-small performance on short and long context tasks Usage This model is an embedding model, meaning it can only be used to generate embeddings REST API
  • Introducing Nomic Embed: A Truly Open Embedding Model
    We're excited to announce the release of Nomic Embed, the first text embedding model with a 8192 context-length that outperforms OpenAI Ada-002 and text-embedding-3-small on both short and long context tasks We release the model weights and training code under an Apache-2 license, as well as the curated data we used to train the model
  • Nomic Embeddings — A cheaper and better way to create embeddings
    An advanced Nomic Embeddings Model v1 5 — Nomic Embed v1 5 Variable-sized embeddings with Matryoshka learning and an 8192 context Outperforms OpenAI text-embedding-3-small across
  • Text Embedding | Nomic Atlas Documentation
    Nomic Atlas natively supports the following text embeddings models: Nomic Embed specialized for retrieval, similarity, clustering and classification Nomic Embed with support for variable embedding size and specialized for retrieval, similarity, clustering and classification Recommended output sizes are 768, 512, 256, 128 and 64
  • nomic-ai nomic-embed-text-v2-moe · Hugging Face
    In this paper, we introduce Nomic Embed v2, the first general purpose MoE text embedding model Our model outperforms models in the same parameter class on both monolingual and multilingual benchmarks while also maintaining competitive performance with models twice its size
  • Nomic AI
    Nomic Embed Text v1 5: The most popular open source text embedder on Hugging Face • Outperforms OpenAI Embeddings on the MTEB benchmark • Matryoshka and Binary embeddings for efficient storage • 137M parameters for efficient inference • Open weights, data, and code; Nomic Embed Vision v1 5
  • Nomic Embed: Training a Reproducible Long Context Text Embedder
    This technical report describes the training of nomic-embed-text-v1, the first fully reproducible, open-source, open-weights, open-data, 8192 context length English text embedding model that outperforms both OpenAI Ada-002 and OpenAI text-embedding-3-small on the short-context MTEB benchmark and the long context LoCo benchmark
  • Nomic Blog: The Nomic Embedding Ecosystem
    Nomic Embed Multimodal is a state-of-the-art multimodal embedder that supports interleaved text and image inputs, including rich visual documents like PDFs, slides, and Arxiv papers, for accurate multimodal retrieval Nomic Embed Multimodal achieves SOTA performance on the Vidore v1 and v2 Benchmark Econ Macro Multi AXA Multi
  • Embed Text | Nomic Atlas Documentation
    Generates text embeddings nomic-embed-text was trained to support these tasks: search_document (embedding document chunks for search retrieval) search_query (embedding queries for search retrieval) classification (embeddings for text classification) clustering (embeddings for cluster visualization)





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