AI正在发生 未来正在更新Reason-ModernColBERT
Reason-ModernColBERT is a late interaction model trained on the reasonir-hq dataset. It achieves extremely competitive performance on the BRIGHT benchmark aimed at evaluating reasoning-intensive retrieval performance, outperforming all existing models up to 7B (more than 45 times its size) and even surprisingly improving performance of ReasonIR-8B (a 8B model trained on the same data) by more than 2.5 NDCG@10 on average on Stack Exchange splits. We attribute such strong results to late-interaction, see evaluation section. Unfortunately, since the ReasonIR data has been released under a cc-by-nc-4.0 license, we cannot release this model under an Apache 2.0 license. However, the authors of ReasonIR released code to generate the data. Anyone willing to reproduce the data could then easily reproduce this model under an Apache 2.0 license by running a fine-tuning lasting lower than 2 hours using this boilerplate. This is a multi-vector (ColBERT-style late interaction) embedding model finetuned from lightonai/GTE-ModernColBERT-v1 on the reasonir-hq dataset. It maps sentences & paragraphs to sequences of 128-dimensional dense vectors and can be used for semantic textual similarity using the MaxSim operator.
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Reason-ModernColBERT is a late interaction model trained on the reasonir-hq dataset. It achieves extremely competitive performance on the BRIGHT benchmark aimed at evaluating reasoning-intensive retrieval performance, outperforming all existing models up to 7B (more than 45 times its size) and even surprisingly improving performance of ReasonIR-8B (a 8B model trained on the same data) by more than 2.5 NDCG@10 on average on Stack Exchange splits. We attribute such strong results to late-interaction, see evaluation section. Unfortunately, since the ReasonIR data has been released under a cc-by-nc-4.0 license, we cannot release this model under an Apache 2.0 license. However, the authors of ReasonIR released code to generate the data. Anyone willing to reproduce the data could then easily reproduce this model under an Apache 2.0 license by running a fine-tuning lasting lower than 2 hours using this boilerplate. This is a multi-vector (ColBERT-style late interaction) embedding model finetuned from lightonai/GTE-ModernColBERT-v1 on the reasonir-hq dataset. It maps sentences & paragraphs to sequences of 128-dimensional dense vectors and can be used for semantic textual similarity using the MaxSim operator.