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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

PanGu-α has been created using the MindSpore framework and utilizes a powerful setup of 2048 Ascend 910 AI processors for its training. The training process employs an advanced parallelism strategy that leverages MindSpore Auto-parallel, which integrates five different parallelism dimensions—data parallelism, operation-level model parallelism, pipeline model parallelism, optimizer model parallelism, and rematerialization—to effectively distribute tasks across the 2048 processors. To improve the model's generalization, we gathered 1.1TB of high-quality Chinese language data from diverse fields for pretraining. We conduct extensive tests on PanGu-α's generation capabilities across multiple situations, such as text summarization, question answering, and dialogue generation. Additionally, we examine how varying model scales influence few-shot performance across a wide array of Chinese NLP tasks. The results from our experiments highlight the exceptional performance of PanGu-α, demonstrating its strengths in handling numerous tasks even in few-shot or zero-shot contexts, thus showcasing its versatility and robustness. This comprehensive evaluation reinforces the potential applications of PanGu-α in real-world scenarios.

API Access

Has API No 

API Access

Has API No 

Screenshots View All

Screenshots View All

No images available

Integrations

ERNIE Bot Yes 

Integrations

ERNIE Bot No 

Pricing Details

No price information available.
Free Trial No 
Free Version No 

Pricing Details

No price information available.
Free Trial No 
Free Version No 

Deployment

Web-Based Yes 
On-Premises Yes 
iPhone App No 
iPad App No 
Android App No 
Windows Yes 
Mac Yes 
Linux Yes 
Chromebook No 

Deployment

Web-Based Yes 
On-Premises Yes 
iPhone App No 
iPad App No 
Android App No 
Windows Yes 
Mac Yes 
Linux Yes 
Chromebook No 

Customer Support

Business Hours No 
Live Rep (24/7) No 
Online Support No 

Customer Support

Business Hours No 
Live Rep (24/7) No 
Online Support No 

Types of Training

Training Docs Yes 
Webinars No 
Live Training (Online) No 
In Person No 

Types of Training

Training Docs Yes 
Webinars No 
Live Training (Online) No 
In Person No 

Vendor Details

Company Name

Baidu

Founded

2000

Country

China

Website

research.baidu.com/Public/uploads/61c4362c79ee8.pdf

Vendor Details

Company Name

Huawei

Founded

1987

Country

China

Website

arxiv.org/abs/2104.12369

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