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What is RoBERTa?

RoBERTa improves upon the language masking technique introduced by BERT, as it focuses on predicting parts of text that are intentionally hidden in unannotated language datasets. Built on the PyTorch framework, RoBERTa implements crucial changes to BERT's hyperparameters, including the removal of the next-sentence prediction task and the adoption of larger mini-batches along with increased learning rates. These enhancements allow RoBERTa to perform the masked language modeling task with greater efficiency than BERT, leading to better outcomes in a variety of downstream tasks. Additionally, we explore the advantages of training RoBERTa on a vastly larger dataset for an extended period, which includes not only existing unannotated NLP datasets but also CC-News, a novel compilation derived from publicly accessible news articles. This thorough methodology fosters a deeper and more sophisticated comprehension of language, ultimately contributing to the advancement of natural language processing techniques. As a result, RoBERTa's design and training approach set a new benchmark in the field.

What is PanGu-Σ?

Recent advancements in natural language processing, understanding, and generation have largely stemmed from the evolution of large language models. This study introduces a system that utilizes Ascend 910 AI processors alongside the MindSpore framework to train a language model that surpasses one trillion parameters, achieving a total of 1.085 trillion, designated as PanGu-{\Sigma}. This model builds upon the foundation laid by PanGu-{\alpha} by transforming the traditional dense Transformer architecture into a sparse configuration via a technique called Random Routed Experts (RRE). By leveraging an extensive dataset comprising 329 billion tokens, the model was successfully trained with a method known as Expert Computation and Storage Separation (ECSS), which led to an impressive 6.3-fold increase in training throughput through the application of heterogeneous computing. Experimental results revealed that PanGu-{\Sigma} sets a new standard in zero-shot learning for various downstream tasks in Chinese NLP, highlighting its significant potential for progressing the field. This breakthrough not only represents a considerable enhancement in the capabilities of language models but also underscores the importance of creative training methodologies and structural innovations in shaping future developments. As such, this research paves the way for further exploration into improving language model efficiency and effectiveness.

Media

Media

No images available

Integrations Supported

AWS Marketplace
Haystack
PanGu Chat
Spark NLP

Integrations Supported

AWS Marketplace
Haystack
PanGu Chat
Spark NLP

API Availability

Has API

API Availability

Has API

Pricing Information

Free
Free Trial Offered?
Free Version

Pricing Information

Pricing not provided.
Free Trial Offered?
Free Version

Supported Platforms

SaaS
Android
iPhone
iPad
Windows
Mac
On-Prem
Chromebook
Linux

Supported Platforms

SaaS
Android
iPhone
iPad
Windows
Mac
On-Prem
Chromebook
Linux

Customer Service / Support

Standard Support
24 Hour Support
Web-Based Support

Customer Service / Support

Standard Support
24 Hour Support
Web-Based Support

Training Options

Documentation Hub
Webinars
Online Training
On-Site Training

Training Options

Documentation Hub
Webinars
Online Training
On-Site Training

Company Facts

Organization Name

Meta

Date Founded

2004

Company Location

United States

Company Website

ai.facebook.com/blog/roberta-an-optimized-method-for-pretraining-self-supervised-nlp-systems/

Company Facts

Organization Name

Huawei

Date Founded

1987

Company Location

China

Company Website

huawei.com

Categories and Features

Categories and Features

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