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RAG (Retrieval Augmented Generation) Framework for building modular, open source applications for production by TrueFoundry - GitHub - truefoundry/cognita: RAG (Retrieval Augmented Generation) Framework for building modular, open source applications for production by TrueFoundry
Extractive Question Answering (EQA) is one of fundamental problems in Natural Language Understanding. This paper deals with the problem of transferring an EQA model trained on a single (probably large) dataset, known as a source, to multiple new and unlabeled datasets, known as targets. Specifically, a novel single-source to multiple-target domain adaptation method is proposed to address the cross-domain EQA task. The method forms the shared feature space across different domains via minimizing the training loss on the source and the feature discrimination loss between source and target sample...
Plato Data Intelligence Leverages Vectara to Power its Data-As-A-Service / DaaS Platform
In this guide, we evaluate Google's Gemini LMM against several computer vision tasks, from OCR to VQA to zero-shot object detection.
Ditch boolean parameters for clearer, more maintainable code, reducing ambiguity in method calls and enhancing overall readability.