Research paper · 2024-02-02
Nomic Embed: Training a Reproducible Long Context Text Embedder
Zach Nussbaum, John X. Morris, Brandon Duderstadt, Andriy Mulyar
Published2024-02-02
Authors4
Citing Models2
arXiv2402.01613
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
Details
- arXiv identifier
- 2402.01613
- Published
- 2024-02-02
- Authors
- Zach Nussbaum, John X. Morris, Brandon Duderstadt, Andriy Mulyar
Models That Cite This Paper
- Described bynomic-embed-text-v1.5
- Described bynomic-embed-text-v1