nomic-embed-text-v1

nomic-embed-text-v1: A Reproducible Long Context (8192) Text Embedder Blog | Technical Report | AWS SageMaker | Atlas Embedding and Unstructured Data Analytics Platform `nomic-embed-text-v1` is 8192 context length text encoder that surpasses OpenAI text-embedding-ada-002 and text-embedding-3-small performance on short and long context tasks. Performance Benchmarks | Name | SeqLen | MTEB | LoCo | Jina Long Context | Open Weights | Open Training Code | Open Data | | :-------------------------------:| :----- | :-------- | :------: | :---------------: | :-----------: | :----------------: | :---------- | | nomic-embed-text-v1 | 8192 | **62.39** |**85.53** | 54.16 | ✅ | ✅ | ✅ | | jina-embeddings-v2-base-en | 8192 | 60.39 | 85.45 | 51.90 | ✅ | ❌ | ❌ | | text-embedding-3-small | 8191 | 62.26 | 82.40 | **58.20** | ❌ | ❌ | ❌ | | text-embedding-ada-002 | 8191 | 60.99 | 52.7 | 55.25 | ❌ | ❌ | ❌ | Exciting Update!**: `nomic-embed-text-v1` is now multimodal! nomic-embed-vision-v1 is aligned to the embedding space of `nomic-embed-text-v1`, 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 gr