The new models are built upon the Sentence Transformers framework. They utilize a multi-vector approach to encoding text, allowing for more nuanced representation of semantic relationships. This architecture is designed to improve performance in scenarios where context is crucial, such as late interaction tasks. These models are available for download and use. The models are designed to be efficient for inference. The release includes pre-trained models and instructions for fine-tuning.
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Multi-Vector Embedding Models Released by Sentence Transformers
Sentence Transformers has released new multi-vector embedding models designed for late interaction. These models offer improved performance for tasks requiring understanding of context and relationships between multiple pieces of information.
By OpenSmartRoute editorial · written through the router by writer-small
From Hugging Face blog - “Multi-Vector (Late Interaction) Embedding Models with Sentence Transformers”
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