TabFMGoogle
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alvaModelAlvascience
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Related Products
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About
TabFM is a zero-shot foundation model for tabular data, designed to simplify classification and regression workflows that traditionally require manual model training, hyperparameter tuning, and domain-specific feature engineering. Built specifically for tables, TabFM reframes tabular prediction as an in-context learning problem: instead of fitting a new supervised model to each dataset, it takes historical training examples and target testing rows together as one unified prompt, then interprets relationships between columns and rows at inference time. Because tables are two-dimensional and orderless, TabFM uses a hybrid architecture that combines alternating row and column attention, row compression, and a dedicated Transformer for in-context learning over compressed row embeddings. This design lets the model capture complex feature interactions and dependencies while keeping prediction computationally efficient for larger datasets.
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About
alvaModel is a software tool for building, validating, comparing, and applying QSAR and QSPR models. It supports regression and classification workflows based on molecular descriptors and fingerprints, with a strong focus on model transparency, interpretability, and scientific robustness.
The software includes multiple data splitting strategies, variable selection methods, modeling algorithms, and comprehensive internal and external validation procedures. alvaModel provides diagnostic plots, applicability domain analysis, and model comparison tools to support the identification of reliable and predictive models.
Designed according to best practices in chemometrics, alvaModel facilitates the development of interpretable models consistent with the OECD principles for QSAR validation, making it suitable for research and regulatory-oriented applications. The graphical interface guides users through the entire modeling workflow while allowing full control over each modeling step.
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Platforms Supported
Windows
Not Supported
Mac
Not Supported
Linux
Not Supported
Cloud
Supported
On-Premises
Not Supported
iPhone
Not Supported
iPad
Not Supported
Android
Not Supported
Chromebook
Not Supported
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Platforms Supported
Windows
Supported
Mac
Supported
Linux
Supported
Cloud
Not Supported
On-Premises
Not Supported
iPhone
Not Supported
iPad
Not Supported
Android
Not Supported
Chromebook
Not Supported
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Audience
Data scientists and machine learning teams seeking to perform zero-shot classification and regression on tabular data without task-specific training
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Audience
Toxicologists, regulatory scientists, risk assessors, environmental chemists, and QSAR/QSPR modelers working in chemical safety assessment, regulatory compliance (e.g. REACH, OECD), environmental risk evaluation, and academic or governmental research.
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Support
Phone Support
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24/7 Live Support
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Online
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Support
Phone Support
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24/7 Live Support
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Online
Supported
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API
Offers API
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API
Offers API
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Screenshots and Videos |
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Pricing
Free
Free Version
Supported
Free Trial
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Pricing
No information available.
Free Version
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Free Trial
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Reviews/
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Reviews/
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Training
Documentation
Supported
Webinars
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Live Online
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In Person
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Training
Documentation
Supported
Webinars
Supported
Live Online
Not Supported
In Person
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Company InformationGoogle
Founded: 1998
United States
research.google/blog/introducing-tabfm-a-zero-shot-foundation-model-for-tabular-data/
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Company InformationAlvascience
Founded: 2018
Italy
www.alvascience.com
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Integrations
alvaBuilder
Not Supported
alvaDesc
Not Supported
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