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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

GLTR is designed to utilize the same models that generate counterfeit text as a means for detection. It incorporates the GPT-2 117M language model from OpenAI, which stands out as one of the most substantial models accessible to the public. By taking any given textual input, GLTR evaluates the predictions made by GPT-2 at each position in the text. The output showcases a ranking of all words recognized by the model, allowing us to determine how the actual following word ranks in comparison. Utilizing this positional data, a color-coded mask is applied to the text, reflecting the ranking position of each word. Words that rank among the most probable are shaded in green (for the top 10), yellow (for the top 100), red (for the top 1,000), while the remaining words appear in purple. Consequently, this method provides a clear visual representation of how probable each word is according to the model's predictions, ultimately enhancing our ability to identify potentially fake text. Additionally, this visual tool can help users quickly gauge the authenticity of a given passage.

API Access

Has API No 

API Access

Has API No 

Screenshots View All

Screenshots View All

Integrations

ChatGPT No 
GPT-3 No 
GPT-4 No 
OpenAI No 
Spark NLP Yes 

Integrations

ChatGPT Yes 
GPT-3 Yes 
GPT-4 Yes 
OpenAI Yes 
Spark NLP No 

Pricing Details

No price information available.
Free Trial No 
Free Version No 

Pricing Details

Free
Free Trial No 
Free Version Yes 

Deployment

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

Deployment

Web-Based Yes 
On-Premises Yes 
iPhone App No 
iPad App No 
Android App No 
Windows No 
Mac No 
Linux No 
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 No 
Webinars No 
Live Training (Online) No 
In Person No 

Vendor Details

Company Name

Google

Founded

1998

Country

United States

Website

github.com/google-research/albert

Vendor Details

Company Name

GLTR

Country

United States

Website

gltr.io

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