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What is XLNet?

XLNet presents a groundbreaking method for unsupervised language representation learning through its distinct generalized permutation language modeling objective. In addition, it employs the Transformer-XL architecture, which excels in managing language tasks that necessitate the analysis of longer contexts. Consequently, XLNet achieves remarkable results, establishing new benchmarks with its state-of-the-art (SOTA) performance in various downstream language applications like question answering, natural language inference, sentiment analysis, and document ranking. This innovative model not only enhances the capabilities of natural language processing but also opens new avenues for further research in the field. Its impact is expected to influence future developments and methodologies in language understanding.

What is ESMFold?

ESMFold exemplifies how artificial intelligence can provide us with groundbreaking tools to investigate the natural world, similar to how the microscope transformed our ability to see the intricate details of life. By leveraging AI, we can achieve new insights into the rich tapestry of biological diversity, thus deepening our understanding of life sciences. A considerable amount of AI research focuses on teaching machines to perceive the world in ways that parallel human cognition. However, the intricate language of proteins remains difficult for humans to interpret and has posed challenges for even the most sophisticated computational models. Despite these hurdles, AI has the potential to decode this complex language, thereby enhancing our understanding of biological mechanisms. Investigating AI's role in biology not only broadens our comprehension of life sciences but also illuminates the wider implications of artificial intelligence as a whole. Our research underscores the interconnected nature of various disciplines: the large language models that drive advancements in machine translation, natural language processing, speech recognition, and image generation also have the potential to uncover valuable insights into biological systems. This interdisciplinary strategy may lead to groundbreaking discoveries in both the fields of AI and biology, fostering collaboration that could yield transformative advancements. As we continue to explore these synergies, the future holds great promise for expanding our knowledge and capabilities in understanding life itself.

Media

Media

Integrations Supported

Spark NLP

Integrations Supported

API Availability

API Availability

Pricing Information

Free
Free Version

Pricing Information

Free
Free Version

Supported Platforms

SaaS
On-Prem

Supported Platforms

SaaS
On-Prem

Customer Service / Support

Not specified

Customer Service / Support

Not specified

Training Options

Documentation Hub

Training Options

Documentation Hub

Company Facts

Organization Name

XLNet

Date Founded

2019

Company Website

github.com/zihangdai/xlnet

Company Facts

Organization Name

Meta

Date Founded

2004

Company Location

United States

Company Website

github.com/facebookresearch/esm

Categories and Features

AI Models

Not specified

Generative AI

Not specified

Large Language Models

Not specified

Categories and Features

AI Models

Not specified

Generative AI

Not specified

Large Language Models

Not specified

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