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Description
Adaption equips users with the necessary tools to create AI models that can evolve alongside shifting data and requirements. This comprehensive web platform incorporates essential features such as data preparation, synthetic data generation, dataset exploration, and the process of model training. The Adaptive Data feature allows users to import various files and datasets from sources like Hugging Face or Kaggle, facilitating column mapping, deduplication, prompt transformation, augmentation, translation, and evaluation. By utilizing the Invent a Dataset function, users can generate instruction or preference data based on a specified written objective. The Visualize component enhances user experience by supporting inquiries, providing interactive charts, and enabling detailed inspection of text and tabular data on a row-by-row basis. AutoScientist plays a crucial role in optimizing training data and tailoring model recipes to meet specific goals, conducting experiments, assessing outcomes, and exporting refined models. The platform also includes workflows that cover fine-tuning, alignment, multilingual dataset expansion, and thorough data-coverage analysis. Additionally, Adaption provides API access along with a Python SDK, facilitating the seamless integration of these tools into AI development workflows, ultimately enhancing productivity and efficiency in model creation. As such, it serves as a comprehensive resource for developers seeking to leverage AI capabilities effectively.
Description
TabFM is an innovative zero-shot foundation model specifically created for handling tabular data, aimed at streamlining classification and regression processes that usually necessitate extensive manual model training, hyperparameter optimization, and tailored feature engineering. By transforming the challenge of tabular prediction into an in-context learning task, TabFM avoids the need to train a new supervised model for every dataset; instead, it consolidates historical training examples and target testing rows into a single cohesive prompt, allowing it to discern the intricate relationships between various columns and rows during inference. Given that tables are inherently two-dimensional and do not rely on a specific order, TabFM employs a hybrid architecture that integrates alternating attention mechanisms for both rows and columns, row compression techniques, and a specialized Transformer designed for in-context learning based on these compressed row embeddings. This sophisticated framework enables the model to effectively capture complex interactions and dependencies among features while maintaining computational efficiency, particularly advantageous for processing larger datasets. Furthermore, this approach not only enhances performance but also significantly reduces the time and resources typically required for model development in tabular data tasks.
API Access
Has API
Yes
API Access
Has API
No
Screenshots View All
No images available
Integrations
Hugging Face
No
Kaggle
No
Together AI
No
Pricing Details
No price information available.
Free Trial
No
Free Version
No
Pricing Details
Free
Free Trial
No
Free Version
Yes
Deployment
Web-Based
Yes
On-Premises
No
iPhone App
No
iPad App
No
Android App
No
Windows
No
Mac
No
Linux
No
Chromebook
No
Deployment
Web-Based
Yes
On-Premises
No
iPhone App
No
iPad App
No
Android App
No
Windows
No
Mac
No
Linux
No
Chromebook
No
Customer Support
Business Hours
No
Live Rep (24/7)
No
Online Support
No
Customer Support
Business Hours
No
Live Rep (24/7)
No
Online Support
Yes
Types of Training
Training Docs
Yes
Webinars
No
Live Training (Online)
No
In Person
No
Types of Training
Training Docs
Yes
Webinars
No
Live Training (Online)
No
In Person
No
Vendor Details
Company Name
Adaption
Founded
2026
Country
United States
Website
adaptionlabs.ai
Vendor Details
Company Name
Founded
1998
Country
United States
Website
research.google/blog/introducing-tabfm-a-zero-shot-foundation-model-for-tabular-data/
Product Features
Product Features
Alternatives
No Alternatives