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Description

ALBERT is a self-supervised Transformer architecture that undergoes pretraining on a vast dataset of English text, eliminating the need for manual annotations by employing an automated method to create inputs and corresponding labels from unprocessed text. This model is designed with two primary training objectives in mind. The first objective, known as Masked Language Modeling (MLM), involves randomly obscuring 15% of the words in a given sentence and challenging the model to accurately predict those masked words. This approach sets it apart from recurrent neural networks (RNNs) and autoregressive models such as GPT, as it enables ALBERT to capture bidirectional representations of sentences. The second training objective is Sentence Ordering Prediction (SOP), which focuses on the task of determining the correct sequence of two adjacent text segments during the pretraining phase. By incorporating these dual objectives, ALBERT enhances its understanding of language structure and contextual relationships. This innovative design contributes to its effectiveness in various natural language processing tasks.

Description

Language plays a crucial role in showcasing and enhancing understanding, which is essential to the human experience. It empowers individuals to share thoughts, convey ideas, create lasting memories, and foster empathy and connection with others. These elements are vital for social intelligence, which is why our teams at DeepMind focus on various facets of language processing and communication in both artificial intelligences and humans. Within the larger framework of AI research, we are convinced that advancing the capabilities of language models—systems designed to predict and generate text—holds immense promise for the creation of sophisticated AI systems. Such systems can be employed effectively and safely to condense information, offer expert insights, and execute commands through natural language. However, the journey toward developing beneficial language models necessitates thorough exploration of their possible consequences, including the challenges and risks they may introduce into society. By understanding these dynamics, we can work towards harnessing their power while minimizing any potential downsides.

API Access

Has API

API Access

Has API

Screenshots View All

Screenshots View All

Integrations

BERT
ChatGPT
Dolly
GPT-4
Llama
Llama 2
Llama 3.1
Llama 3.2
Llama 3.3
Spark NLP
Stable LM

Integrations

BERT
ChatGPT
Dolly
GPT-4
Llama
Llama 2
Llama 3.1
Llama 3.2
Llama 3.3
Spark NLP
Stable LM

Pricing Details

No price information available.
Free Trial
Free Version

Pricing Details

No price information available.
Free Trial
Free Version

Deployment

Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook

Deployment

Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook

Customer Support

Business Hours
Live Rep (24/7)
Online Support

Customer Support

Business Hours
Live Rep (24/7)
Online Support

Types of Training

Training Docs
Webinars
Live Training (Online)
In Person

Types of Training

Training Docs
Webinars
Live Training (Online)
In Person

Vendor Details

Company Name

Google

Founded

1998

Country

United States

Website

github.com/google-research/albert

Vendor Details

Company Name

DeepMind

Founded

2010

Country

United Kingdom

Website

www.deepmind.com/blog/language-modelling-at-scale-gopher-ethical-considerations-and-retrieval

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