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What is TabFM?

TabFM is a cutting-edge foundation model designed for zero-shot learning specifically tailored to manage tabular data, with the goal of simplifying the processes of classification and regression that often demand considerable manual training, hyperparameter tuning, and customized feature engineering. By reframing the difficulties associated with tabular prediction as an in-context learning challenge, TabFM eliminates the necessity of training a distinct supervised model for each dataset; rather, it merges previous training examples with target testing rows into a unified prompt, enabling it to identify the complex relationships that exist between different columns and rows during the inference phase. Since tables are fundamentally two-dimensional and do not depend on a predetermined order, TabFM utilizes a hybrid architecture that combines alternating attention mechanisms for both rows and columns, along with row compression methods, and a dedicated Transformer designed for in-context learning based on these compressed row representations. This advanced structure allows the model to adeptly capture intricate interactions and dependencies among features while ensuring computational efficiency, which is particularly beneficial for dealing with larger datasets. Moreover, this innovative methodology not only boosts performance but also markedly decreases the time and resources generally required for the development of models in tabular data applications, paving the way for more effective analytical solutions. As a result, TabFM represents a significant advancement in the realm of machine learning for tabular data, starting a new era in data analysis.

What is Evo 2?

Evo 2 is an advanced genomic foundation model that excels in predicting and creating tasks associated with DNA, RNA, and proteins. Utilizing a sophisticated deep learning architecture, it models biological sequences with precision down to single-nucleotide accuracy, demonstrating remarkable scalability in both computational and memory resources as context length expands. The model has been trained on an impressive 40 billion parameters and can handle a context length of 1 megabase, analyzing an immense dataset of over 9 trillion nucleotides derived from diverse eukaryotic and prokaryotic genomes. This extensive training enables Evo 2 to perform zero-shot function predictions across a range of biological types, including DNA, RNA, and proteins, while also generating novel sequences that adhere to plausible genomic frameworks. Its robust capabilities have been highlighted in applications such as the design of efficient CRISPR systems and the identification of potentially disease-causing mutations in human genes. Additionally, Evo 2 is accessible to the public via Arc's GitHub repository and is integrated into the NVIDIA BioNeMo framework, which significantly enhances its availability to researchers and developers. This integration not only broadens the model's reach but also represents a pivotal advancement in the fields of genomic modeling and analysis, paving the way for future innovations in biotechnology.

Media

Media

Integrations Supported

Evo Designer
GitHub
Hugging Face
NVIDIA BioNeMo

Integrations Supported

Evo Designer
GitHub
Hugging Face
NVIDIA BioNeMo

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

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

Training Options

Documentation Hub
Webinars
Online Training
On-Site Training

Training Options

Documentation Hub
Webinars
Online Training
On-Site Training

Company Facts

Organization Name

Google

Date Founded

1998

Company Location

United States

Company Website

research.google/blog/introducing-tabfm-a-zero-shot-foundation-model-for-tabular-data/

Company Facts

Organization Name

Arc Institute

Company Location

United States

Company Website

arcinstitute.org/tools/evo

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