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3 Methods to Combine PDFs

Managing a multitude of documents efficiently is a common challenge. Many individuals and professionals often find themselves juggling multiple PDF files, each containing essential information. Combining PDF files arises from the need to streamline document organization and enhance accessibility. This blog aims to guide you through the process of merging PDF files, offering a…

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Researchers Shanghai AI Lab and SenseTime Propose MM-Grounding-DINO: An Open and Comprehensive Pipeline for Unified Object Grounding and Detection

Object detection plays a vital role in multi-modal understanding systems, where images are input into models to generate proposals aligned with text. This process is crucial for state-of-the-art models handling Open-Vocabulary Detection (OVD), Phrase Grounding (PG), and Referring Expression Comprehension (REC). OVD models are trained on base categories in zero-shot scenarios but must predict both…

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Building, Evaluating and Tracking a Local Advanced RAG System | Mistral 7b + LlamaIndex + W&B | by Nikita Kiselov | Jan, 2024

Explore building an advanced RAG system on your computer. Full-cycle step-by-step guide with code. Image by the Author | Mistral + LlamaIndex + W&BRetrieval Augmented Generation (RAG) is a powerful NLP technique that combines large language models with selective access to knowledge. It allows us to reduce LLM hallucinations by providing the relevant pieces of…

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5 Ways of Converting Unstructured Data into Structured Insights with LLMs

Image by Author   In today's world, we're constantly generating information, yet much of it arises in unstructured formats.  This includes the vast array of content on social media, as well as countless PDFs and Word documents stored across organizational networks.  Getting insights and value from these unstructured sources, whether they be text documents,…

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UC Berkeley and NYU AI Research Explores the Gap Between the Visual Embedding Space of Clip and Vision-only Self-Supervised Learning

MLLMs, or multimodal large language models, have been advancing lately. By incorporating images into large language models (LLMs) and harnessing the capabilities of LLMs, MLLMs demonstrate exceptional skill in tasks including visual question answering, instruction following, and image understanding. Studies have seen a significant flaw in these models despite their improvements; they still have some…

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