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Description
Pre-trained language models have made significant strides, achieving top-tier performance across multiple Natural Language Processing (NLP) applications. The impressive capabilities of GPT-3 highlight how increasing the scale of these models can unlock their vast potential. Recently, a comprehensive framework known as ERNIE 3.0 was introduced to pre-train large-scale models enriched with knowledge, culminating in a model boasting 10 billion parameters. This iteration of ERNIE 3.0 has surpassed the performance of existing leading models in a variety of NLP tasks. To further assess the effects of scaling, we have developed an even larger model called ERNIE 3.0 Titan, which consists of up to 260 billion parameters and is built on the PaddlePaddle platform. Additionally, we have implemented a self-supervised adversarial loss alongside a controllable language modeling loss, enabling ERNIE 3.0 Titan to produce texts that are both reliable and modifiable, thus pushing the boundaries of what these models can achieve. This approach not only enhances the model's capabilities but also opens new avenues for research in text generation and control.
Description
RoBERTa enhances the language masking approach established by BERT, where the model is designed to predict segments of text that have been deliberately concealed within unannotated language samples. Developed using PyTorch, RoBERTa makes significant adjustments to BERT's key hyperparameters, such as eliminating the next-sentence prediction task and utilizing larger mini-batches along with elevated learning rates. These modifications enable RoBERTa to excel in the masked language modeling task more effectively than BERT, resulting in superior performance in various downstream applications. Furthermore, we examine the benefits of training RoBERTa on a substantially larger dataset over an extended duration compared to BERT, incorporating both existing unannotated NLP datasets and CC-News, a new collection sourced from publicly available news articles. This comprehensive approach allows for a more robust and nuanced understanding of language.
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Deployment
Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook
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Online Support
Customer Support
Business Hours
Live Rep (24/7)
Online Support
Types of Training
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Live Training (Online)
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Types of Training
Training Docs
Webinars
Live Training (Online)
In Person
Vendor Details
Company Name
Baidu
Founded
2000
Country
China
Website
research.baidu.com/Public/uploads/61c4362c79ee8.pdf
Vendor Details
Company Name
Meta
Founded
2004
Country
United States
Website
ai.facebook.com/blog/roberta-an-optimized-method-for-pretraining-self-supervised-nlp-systems/