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This technical report describes the training of nomic-embed-vision, a highly performant, open-code, open-weights image embedding model that shares the same latent space as nomic-embed-text. Together, nomic-embed-vision and nomic-embed-text form the f
Externí odkaz:
http://arxiv.org/abs/2406.18587
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
Externí odkaz:
http://arxiv.org/abs/2402.01613
Autor:
Anand, Yuvanesh, Nussbaum, Zach, Treat, Adam, Miller, Aaron, Guo, Richard, Schmidt, Ben, Community, GPT4All, Duderstadt, Brandon, Mulyar, Andriy
Large language models (LLMs) have recently achieved human-level performance on a range of professional and academic benchmarks. The accessibility of these models has lagged behind their performance. State-of-the-art LLMs require costly infrastructure
Externí odkaz:
http://arxiv.org/abs/2311.04931
As machine learning models are increasingly employed to assist human decision-makers, it becomes critical to communicate the uncertainty associated with these model predictions. However, the majority of work on uncertainty has focused on traditional
Externí odkaz:
http://arxiv.org/abs/2107.13098
Autor:
JENKS-DALY, SARAH
Publikováno v:
Teen Vogue; Sep2009, Vol. 9 Issue 7, p157-157, 1p