Granite Embedding 30m English (revision r1.1) Model Summary: Granite Embedding 30m English is a 30M parameter dense bi encoder embedding model from the Granite Embeddings suite that can be used to generate high quality text embeddings. This model produces embedding vectors of size 384 and is trained using a combination of open source relevance pair datasets with permissive, enterprise friendly license, and IBM collected and generated datasets. While maintaining competitive scores on academic benchmarks such as BEIR, this model also performs well on many enterprise use cases. This model is developed using retrieval oriented pre training, contrastive fine tuning, knowledge distillation and model merging for improved performance. Granite embedding 30m r1.1 was specifically designed to support multi turn information retrieval and is designed to handle contextual document retrieval in multi turn conversational information retrieval. Granite embedding 30m r1.1 was trained on data tailored for multi turn conversational information retrieval and uses multi teacher distillation over granite embedding 30m english (https://huggingface.co/ibm granite/granite embedding 30m english) Developers:…
We use cookies for essential functionality and analytics. You can accept or reject analytics cookies.Cookie policy