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! 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 searchdocument: and embed your user queries as searchquery:. Notice: From transformers v5.5.0 and sentence transformers v5.3.0, trustremotecode=True will no longer be necessary. This will only be possible with the text-only series as of now. This prefix is used for embedding texts as documents, for example as…
Open weights
apache-2.0
137M parameters
2,048 tokens
sentence-transformers
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. 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! 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 searchdocument: and embed your user queries as searchquery:. Notice: From transformers v5.5.0 and sentence transformers v5.3.0…
Open weights
apache-2.0
137M parameters
8,192 tokens
sentence-transformers
This is a sentence-transformers model: It maps sentences & paragraphs to a 512 dimensional dense vector space and can be used for tasks like clustering or semantic search. Using this model becomes easy when you have sentence-transformers installed: Then you can use the model like this: This model was trained by sentence-transformers. If you find this model helpful, feel free to cite our publication Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks
Open weights
apache-2.0
135M parameters
512 tokens
sentence-transformers
This is a sentence-transformers model: It maps sentences & paragraphs to a 512 dimensional dense vector space and can be used for tasks like clustering or semantic search. Using this model becomes easy when you have sentence-transformers installed: Then you can use the model like this: This model was trained by sentence-transformers. If you find this model helpful, feel free to cite our publication Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks
Open weights
apache-2.0
135M parameters
512 tokens
sentence-transformers
Model · Sentence similarity
Tech
This is a sentence-transformers model finetuned from microsoft/codebert-base. It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, classification, clustering, and more. First install the Sentence Transformers library: Then you can load this model and run inference. Approximate statistics based on the first 100 samples: - numtrainepochs: 1 - learningrate: 2e-05 - warmupsteps: 0.1 - gradientaccumulationsteps: 4 - fp16: True - perdevicetrainbatchsize: 8 - numtrainepochs: 1 - maxsteps: -1 - learningrate: 2e-05 - lrschedulertype: linear - lrschedulerkwargs: None - warmupsteps: 0.1 - optim…
Open weights
125M parameters
514 tokens
sentence-transformers
This is a PyLate model finetuned from lightonai/LateOn. It maps sentences & paragraphs to sequences of 128-dimensional dense vectors and can be used for semantic textual similarity using the MaxSim operator. Use this model with PyLate to index and retrieve documents. The index uses FastPLAID for efficient similarity search. Load the ColBERT model and initialize the PLAID index, then encode and index your documents: Note that you do not have to recreate the index and encode the documents every time. Once you have created an index and added the documents, you can re-use the index later by loading it: Once the documents are indexed, you can retrieve the top-k most relevant documents for a…
Open weights
149M parameters
8,192 tokens
PyLate
