Paper · arXiv 2402.01613
Nomic Embed: Training a Reproducible Long Context Text Embedder
Zach Nussbaum, John X. Morris, Brandon Duderstadt, Andriy Mulyar
The report details the development and release of nomic-embed-text-v1, an open-source English text embedding model that surpasses OpenAI models in both short and long-context tasks.
Abstract
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 short and long-context tasks. We release the training code and model weights under an Apache 2 license. In contrast with other open-source models, we release a training data loader with 235 million curated text pairs that allows for the full replication of nomic-embed-text-v1. You can find code and data to replicate the model at https://github.com/nomic-ai/contrastors