InstaDBAtinea
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TabFMGoogle
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Related Products
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About
It has been extensively tested on real-life business projects. It is stable and robust, easily extendable and efficient. It can be used in a vast range of applications. Every column added to a table is automatically accessible in the table filters. Moreover, in the case of references, you may filter using any parameter of referee tables. You may use any column (including columns of referee tables) for sorting your records. You may compose several filters to get a hierarchical order. Export to xls or csv is simple. You just copy-paste or download a csv file. Importing from spreadsheets is supported. InstaDB checks whether the formats are correct and referenced records exist in the database. Before any update, a preview of changes is displayed in order to avoid unwanted changes. It is easy to show, hide and change the order of columns. The Reset View button always restores the default column composition.
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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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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
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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Audience
Application development platform for DevOp teams
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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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Support
Phone Support
Supported
24/7 Live Support
Not Supported
Online
Supported
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Support
Phone Support
Not Supported
24/7 Live Support
Not Supported
Online
Supported
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API
Offers API
Not Supported
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API
Offers API
Not Supported
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Screenshots and Videos |
Screenshots and Videos |
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Pricing
$20 per month
Free Version
Supported
Free Trial
Supported
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Pricing
Free
Free Version
Supported
Free Trial
Not Supported
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Reviews/
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Reviews/
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Training
Documentation
Supported
Webinars
Not Supported
Live Online
Supported
In Person
Not Supported
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Training
Documentation
Supported
Webinars
Not Supported
Live Online
Not Supported
In Person
Not Supported
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Company InformationAtinea
Founded: 2008
Poland
instadb.com
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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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Categories |
Categories |
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Application Development Features
Access Controls/Permissions
Supported
Code Assistance
Not Supported
Code Refactoring
Supported
Collaboration Tools
Supported
Compatibility Testing
Supported
Data Modeling
Supported
Debugging
Not Supported
Deployment Management
Supported
Graphical User Interface
Supported
Mobile Development
Not Supported
No-Code
Supported
Reporting/Analytics
Supported
Software Development
Supported
Source Control
Supported
Testing Management
Not Supported
Version Control
Not Supported
Web App Development
Not Supported
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Integrations
Microsoft Excel
Not Supported
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