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What is XLNet?

XLNet presents a groundbreaking method for unsupervised language representation learning through its distinct generalized permutation language modeling objective. In addition, it employs the Transformer-XL architecture, which excels in managing language tasks that necessitate the analysis of longer contexts. Consequently, XLNet achieves remarkable results, establishing new benchmarks with its state-of-the-art (SOTA) performance in various downstream language applications like question answering, natural language inference, sentiment analysis, and document ranking. This innovative model not only enhances the capabilities of natural language processing but also opens new avenues for further research in the field. Its impact is expected to influence future developments and methodologies in language understanding.

What is Megatron-Turing?

The Megatron-Turing Natural Language Generation model (MT-NLG) is distinguished as the most extensive and sophisticated monolithic transformer model designed for the English language, featuring an astounding 530 billion parameters. Its architecture, consisting of 105 layers, significantly amplifies the performance of prior top models, especially in scenarios involving zero-shot, one-shot, and few-shot learning. The model demonstrates remarkable accuracy across a diverse array of natural language processing tasks, such as completion prediction, reading comprehension, commonsense reasoning, natural language inference, and word sense disambiguation. In a bid to encourage further exploration of this revolutionary English language model and to enable users to harness its capabilities across various linguistic applications, NVIDIA has launched an Early Access program that offers a managed API service specifically for the MT-NLG model. This program is designed not only to promote experimentation but also to inspire innovation within the natural language processing domain, ultimately paving the way for new advancements in the field. Through this initiative, researchers and developers will have the opportunity to delve deeper into the potential of MT-NLG and contribute to its evolution.

What is LUIS?

Language Understanding (LUIS) is a sophisticated machine learning service that facilitates the integration of natural language processing capabilities into various applications, bots, and IoT devices. It provides a fast track for creating customized models that evolve over time, allowing developers to seamlessly incorporate natural language features into their projects. LUIS is particularly adept at identifying critical information within conversations by interpreting user intentions (intents) and extracting relevant details from statements (entities), thereby contributing to a comprehensive language understanding framework. In conjunction with the Azure Bot Service, it streamlines the creation of effective bots, making the development process more efficient. With a wealth of developer resources and customizable existing applications, along with entity dictionaries that include categories like Calendar, Music, and Devices, users can quickly design and deploy innovative solutions. These dictionaries benefit from a vast pool of online knowledge, containing billions of entries that assist in accurately extracting pivotal insights from user interactions. The service continuously evolves through active learning, ensuring that the quality of its models improves consistently, thereby solidifying LUIS as an essential asset for contemporary application development. This capability not only empowers developers to craft engaging and responsive user experiences but also significantly enhances overall user satisfaction and interaction quality.

Media

Media

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Media

Integrations Supported

Azure CLU
BotCore
Dydu
Geomant
Geomant Wallboard
Ideta
Microsoft Entra ID
Neo Enterprise Assistant Platform
QnA Maker
Quickwork
Spark NLP
Take Blip

Integrations Supported

Azure CLU
BotCore
Dydu
Geomant
Geomant Wallboard
Ideta
Microsoft Entra ID
Neo Enterprise Assistant Platform
QnA Maker
Quickwork
Spark NLP
Take Blip

Integrations Supported

Azure CLU
BotCore
Dydu
Geomant
Geomant Wallboard
Ideta
Microsoft Entra ID
Neo Enterprise Assistant Platform
QnA Maker
Quickwork
Spark NLP
Take Blip

API Availability

Has API

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

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

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

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

Training Options

Documentation Hub
Webinars
Online Training
On-Site Training

Company Facts

Organization Name

XLNet

Date Founded

2019

Company Website

github.com/zihangdai/xlnet

Company Facts

Organization Name

NVIDIA

Date Founded

1993

Company Location

United States

Company Website

developer.nvidia.com/megatron-turing-natural-language-generation

Company Facts

Organization Name

Microsoft

Date Founded

1975

Company Location

United States

Company Website

www.luis.ai/

Categories and Features

Categories and Features

Machine Learning

Deep Learning
ML Algorithm Library
Model Training
Natural Language Processing (NLP)
Predictive Modeling
Statistical / Mathematical Tools
Templates
Visualization

Natural Language Processing

Co-Reference Resolution
In-Database Text Analytics
Named Entity Recognition
Natural Language Generation (NLG)
Open Source Integrations
Parsing
Part-of-Speech Tagging
Sentence Segmentation
Stemming/Lemmatization
Tokenization

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