Answerdotai

answerai-colbert-small-v1

answerdotai

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The answerai-colbert-small-v1 model is a new, proof-of-concept model by Answer.AI that showcases the strong performance multi-vector models can achieve with the new JaColBERTv2.5 training recipe and some extra tweaks, even with just 33 million parameters. Despite its MiniLM-sized architecture, it outperforms larger popular models like e5-large-v2 or bge-base-en-v1.5 on common benchmarks. Model inputs and outputs Inputs Text**: The model takes text inputs such as queries or passages. Outputs Ranked list of passages**: Given a query, the model returns a ranked list of the most relevant passages. Capabilities The answerai-colbert-small-v1 model demonstrates that compact multi-vector models can achieve high performance on retrieval tasks. It outperforms much larger single-vector models, showing the power of the contextualized late interaction approach pioneered by the ColBERT family of models. What can I use it for? The answerai-colbert-small-v1 model can be used for efficient and accurate semantic search applications. Its small size makes it particularly suitable for deployment in resource-constrained environments. You can use it as a re-ranker to improve the results of an initial lexical search, or as the primary retrieval engine in a RAGatouille system. Things to try One interesting aspect of the answerai-colbert-small-v1 model is its ability to achieve high performance with a relatively small number of parameters. This suggests there may be opportunities to further optimize the architecture and training process to create even more efficient retrieval models. Researchers and developers interested in building high-performance search systems may want to explore how the techniques used to train this model could be applied to their own use cases.

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