title: Nomic Embed: Training a Reproducible Long Context Text Embedder
summary: 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
paper_authors._id: 65c071a277b1ecb76d6a3c5f
paper_authors.name: Zach Nussbaum
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paper_authors.fullname: Zach Nussbaum
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paper_authors.name: John X. Morris
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paper_authors.name: Brandon Duderstadt
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paper_authors.fullname: Brandon Duderstadt
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paper_authors._id: 65c071a277b1ecb76d6a3c62
paper_authors.name: Andriy Mulyar
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paper_authors.fullname: Andriy Mulyar
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paper_authors.user: andriym
paper_title: Nomic Embed: Training a Reproducible Long Context Text Embedder
paper_summary: 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
paper_ai_summary: 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.