Google & Hugging Face: The Strongest Language Model Architecture for Zero-Shot Capability

Google & Hugging Face: The Strongest Language Model Architecture for Zero-Shot Capability

This article is approximately 2000 words long and takes about 5 minutes to read. If the goal is the model's zero-shot generalization capability, the decoder structure + language model task is the best; if multitask finetuning is also needed, the encoder-decoder structure + MLM task is the best. From GPT-3 to prompts, more and more … Read more

Prompt Paradigms in Multimodal: CLIP

Prompt Paradigms in Multimodal: CLIP

Machine Learning Algorithms and Natural Language Processing(ML-NLP) is one of the largest natural language processing communities both domestically and internationally, gathering over 500,000 subscribers, covering NLP master’s and doctoral students, university teachers, and corporate researchers. The Vision of the Communityis to promote communication and progress between the academic and industrial sectors of natural language processing … Read more

Google & Hugging Face: The Most Powerful Language Model Architecture for Zero-Shot Learning

Google & Hugging Face: The Most Powerful Language Model Architecture for Zero-Shot Learning

Data Digest authorized reprint from Xi Xiaoyao’s Cute Selling House Author: iven From GPT-3 to prompts, more and more people have discovered that large models perform very well under zero-shot learning settings. This has led to increasing expectations for the arrival of AGI. However, one thing is very puzzling: In 2019, T5 discovered through “hyperparameter … Read more

Language-Guided Open Set Computer Vision

Language-Guided Open Set Computer Vision

Source: ZHUAN ZHI This article is approximately 1000 words, recommended reading time is 5 minutes. We explore three paths to introduce language into computer vision systems for open set recognition. The visual world is vast and constantly evolving. Additionally, due to the long-tail nature of data collection, computer vision systems cannot observe all visual concepts … Read more