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Introducing Nomic Embed: A Truly Open Embedding Model Author: Nomic Team Introducing Nomic Embed: A Truly Open Embedding Model We're excited to announce the release of Nomic Embed, the first Open source Open data Open training code Fully reproducible and auditable text embedding model with a 8192 context-length that outperforms OpenAI Ada-002 and text-embedding-3-small on both short and long context tasks We release the model weights and
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Nomic News We initialize the training of nomic-embed with nomic-bert-2048 Our contrastive dataset is composed of ~235M text pairs We extensively validated its quality during collection with Nomic Atlas You can find dataset details in the nomic-ai constrastors codebase as well as explore a 5M pair subset in Nomic Atlas On the Massive Text Embedding Benchmark (MTEB), nomic-embed outperforms text
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Text Embedding | Nomic API Platform Documentation Visit the API Reference for use details *Model quality degrades with decreased output dimensionality Learn more Embedding task types There are four task types for Nomic Embed: search_query: A query for retrieval search_document: A document for retrieval, or a query for similarity search classification: Used for classification tasks clustering: Results in very high linear separability