Text summarization is a crucial area in natural language processing (NLP) that focuses on condensing lengthy documents into shorter, coherent summaries while retaining the essential information.

Various models, including convolutional neural networks (CNNs) and transformer architectures ... resulting in finer segmentation of cloud images. The use of pre-trained networks and advanced ...

Abstract: Automatic Text Summarization (ATS) systems aim to generate concise summaries of documents while preserving their essential aspects using either extractive or abstractive approaches.

Code Overview: This code leverages the Hugging Face transformers library and the Pegasus model, specifically fine-tuned ... o The interface allows users to input text, generate a summary, and view the ...

Sakana found that self-adaptive models can modify their weights during inference to adjust behavior to new and unseen tasks.

Essentially, Scarfe says, the new model changes the iterative process through which engineers prompt LLMs to perform complex ...

A team of AI researchers, biologists and evolutionary specialists at EvolutionaryScale and the Arc Institute, both in the U.S ...

As nuclear energy ramps up to move towards decarbonization goals, machine learning and AI techniques offer potential to speed ...

The app is powered by a new model called Computer-Using Agent—CUA (“coo-ah”), for short—built on top of OpenAI’s multimodal large language model GPT-4o. Operator is available today at ...