Nomic Embed: Training a Reproducible Long Context Text Embedder
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Cited by 55 in Retrieval-augmented generation, but placed by its own abstract, which didn’t fit.
Abstract (arXiv)
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.
Key facts. Cited by 255 papers in the map (data through 7 Oct 2026). Builds on 51 of its 77 references that are in the map. First posted on arXiv 2 Feb 2024.
Builds on
51 of 77 references.
References · 51
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