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PaliGemma: Open Source Multimodal Model by Google

PaliGemma: Open Source Multimodal Model by Google
roboflow.com - get the latest breaking news, showbiz & celebrity photos, sport news & rumours, viral videos and top stories from roboflow.com Daily Mail and Mail on Sunday newspapers.

Google Paligemma , Kevin Mcallister , Vision Transformer , Gemma Evaluation , Optical Character Recognition , Question Answering , Home Alone ,

OpenAI rival Anthropic launches chatbot Claude in Europe

OpenAI rival Anthropic launches chatbot Claude in Europe
euronews.com - get the latest breaking news, showbiz & celebrity photos, sport news & rumours, viral videos and top stories from euronews.com Daily Mail and Mail on Sunday newspapers.

Claude Team , Gemini Ultra , Bias Benchmark , Question Answering , Claude Pro ,

GitHub - truefoundry/cognita: RAG (Retrieval Augmented Generation) Framework for building modular, open source applications for production by TrueFoundry

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 ....

Langchain Llamaindex , Query Service , Jupyter Notebook , Embedding Job , Embedding Model Deployment , Installing Python , Virtual Environment , Running Cognita , Truefoundry Artifacts , Data Sources , Query Controller , Question Answering , Code Structure , Configure Host Domain , Docker Registry , Open Source ,

"Killing Many Birds with One Stone: Single-Source to Multiple-Target Do" by Xun Yao, Junlong Ma et al.

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 samples, and importantly, a syntax alignment loss is also considered to regulate sample representations from the source-and-target domains. Experimental results on several highly-competitive EQA datasets demonstrate the proposed method outperforms state-of-the-art models by a large margin. Intensive ablation studies are also offered to examine the impact from the integration of source-target domains, in ....

Question Answering , Natural Language ,