nomic-embed-text-v1.5

nomic-embed-text-v1.5: Resizable Production Embeddings with Matryoshka Representation Learning Blog | Technical Report | AWS SageMaker | Nomic Platform Exciting Update!**: `nomic-embed-text-v1.5` is now multimodal! nomic-embed-vision-v1.5 is aligned to the embedding space of `nomic-embed-text-v1.5`, meaning any text embedding is multimodal! Usage Important**: the text prompt *must* include a *task instruction prefix*, instructing the model which task is being performed. For example, if you are implementing a RAG application, you embed your documents as `search_document: ` and embed your user queries as `search_query: `. Notice**: From transformers v5.5.0 and sentence transformers v5.3.0, `trust_remote_code=True` will no longer be necessary. This will only be possible with the text-only series as of now. Task instruction prefixes `search_document` Purpose: embed texts as documents from a dataset This prefix is used for embedding texts as documents, for example as documents for a RAG index. `search_query` Purpose: embed texts as questions to answer This prefix is used for embedding texts as questions that documents from a dataset could resolve, for example as queries to be answered by a RAG application. `clustering` Purpose: embed texts to group them into clusters This prefix is used for embedding texts in order to group them into clusters, discover common topics, or remove semantic duplicates. `classification` Purpose: embed texts to classify them This prefix is used for embedding texts into vectors that will be used as features for a classification model Sentence Transformers Transformers The model natively supports scaling of the sequence length past 2048 tokens. To do so, Transformers.js Nomic API The easiest way to use Nomic Embed is through the Nomic Embedding API. Generating embeddings with the `nomic` Python client is as easy as For more information, see the API reference Infinity Usage with Infinity. Adjusting Dimensionality `nomic-embed-text-v1.5` is an impr