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 at https://github.com/nomic-ai/contrastors.
Cite
@article{arxiv.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}
}
Comments
Accepted to TMLR https://openreview.net/forum?id=IPmzyQSiQE