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 sentence-transformers model: It maps sentences & paragraphs to a 384 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: Without sentence-transformers, you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings. 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
118M parameters
512 tokens
sentence-transformers
