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What is RoBERTa?

RoBERTa improves upon the language masking technique introduced by BERT, as it focuses on predicting parts of text that are intentionally hidden in unannotated language datasets. Built on the PyTorch framework, RoBERTa implements crucial changes to BERT's hyperparameters, including the removal of the next-sentence prediction task and the adoption of larger mini-batches along with increased learning rates. These enhancements allow RoBERTa to perform the masked language modeling task with greater efficiency than BERT, leading to better outcomes in a variety of downstream tasks. Additionally, we explore the advantages of training RoBERTa on a vastly larger dataset for an extended period, which includes not only existing unannotated NLP datasets but also CC-News, a novel compilation derived from publicly accessible news articles. This thorough methodology fosters a deeper and more sophisticated comprehension of language, ultimately contributing to the advancement of natural language processing techniques. As a result, RoBERTa's design and training approach set a new benchmark in the field.

What is Llama 2?

We are excited to unveil the latest version of our open-source large language model, which includes model weights and initial code for the pretrained and fine-tuned Llama language models, ranging from 7 billion to 70 billion parameters. The Llama 2 pretrained models have been crafted using a remarkable 2 trillion tokens and boast double the context length compared to the first iteration, Llama 1. Additionally, the fine-tuned models have been refined through the insights gained from over 1 million human annotations. Llama 2 showcases outstanding performance compared to various other open-source language models across a wide array of external benchmarks, particularly excelling in reasoning, coding abilities, proficiency, and knowledge assessments. For its training, Llama 2 leveraged publicly available online data sources, while the fine-tuned variant, Llama-2-chat, integrates publicly accessible instruction datasets alongside the extensive human annotations mentioned earlier. Our project is backed by a robust coalition of global stakeholders who are passionate about our open approach to AI, including companies that have offered valuable early feedback and are eager to collaborate with us on Llama 2. The enthusiasm surrounding Llama 2 not only highlights its advancements but also marks a significant transformation in the collaborative development and application of AI technologies. This collective effort underscores the potential for innovation that can emerge when the community comes together to share resources and insights.

Media

Media

Integrations Supported

AWS Marketplace

Integrations Supported

AI-FLOW
BrandRank.AI
Code Llama
ConfidentialMind
Ema
Evertune
Fireworks AI
Fleak
GMTech
GaiaNet
Graydient AI
Jspreadsheet
Microsoft Foundry Models
Prompt Security
PromptPal
Second State
Waveloom
WebLLM
WebOrion Protector Plus

API Availability

API Availability

Pricing Information

Free
Free Version

Pricing Information

Free
Free Version

Supported Platforms

SaaS

Supported Platforms

SaaS
Windows
Mac
On-Prem
Linux

Customer Service / Support

Not specified

Customer Service / Support

Not specified

Training Options

Documentation Hub

Training Options

Documentation Hub

Company Facts

Organization Name

Meta

Date Founded

2004

Company Location

United States

Company Website

ai.facebook.com/blog/roberta-an-optimized-method-for-pretraining-self-supervised-nlp-systems/

Company Facts

Organization Name

Meta

Date Founded

2004

Company Location

United States

Company Website

ai.meta.com/llama/

Categories and Features

AI Models

Not specified

Generative AI

Not specified

Large Language Models

Not specified

Categories and Features

AI Coding Models

Not specified

AI Models

Not specified

Large Language Models

Not specified

Small Language Models

Not specified

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