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Tabpfn V2 Reg
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Tabpfn V2 Reg

Developed by Prior-Labs
TabPFN is a Transformer-based foundational model for tabular data. Through prior-data-based learning methods, it achieves strong performance on small-scale tabular regression tasks without requiring task-specific training.
Materials Science#Tabular Few-Shot Learning#Zero-Training Prediction#Transformer Tabular Model
Downloads 24.67k
Release Time : 1/4/2025

Model Overview

TabPFN is a Transformer-based foundational model specifically designed for tabular data, excelling in regression tasks, particularly with few-shot learning scenarios.

Model Features

Few-Shot Learning
Particularly suitable for regression prediction tasks with limited data samples.
No Task-Specific Training
The model learns from prior data and does not require training for specific tasks.
Efficient Inference
Delivers excellent performance and computational efficiency on small-scale tabular data.

Model Capabilities

Tabular Data Regression Prediction
Few-Shot Learning
No Task-Specific Training

Use Cases

Data Analysis
Small-Scale Tabular Data Prediction
Suitable for regression prediction tasks with limited sample sizes in tabular data.
Outperforms traditional machine learning methods on small datasets.
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