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. We release the training code and model weights under an Apache 2.0 license. In contrast with other open-source models, we release the full curated training data and code that allows for full replication of nomic-embed-text-v1. You can find code and data to replicate the model atthis https URL.
View on arXiv@article{nussbaum2025_2402.01613,
title={ Nomic Embed: Training a Reproducible Long Context Text Embedder },
author={ Zach Nussbaum and John X. Morris and Brandon Duderstadt and Andriy Mulyar },
journal={arXiv preprint arXiv:2402.01613},
year={ 2025 }
}