tabpfn_2_5 EU AI Act Compliance Profile

Model Overview

TabPFN-2.5 is a transformer-based foundation model that uses in-context-learning to solve tabular prediction problems in a forward pass. Inference code can be found at https://github.com/PriorLabs/tabPFN.

Getting started

First, install the inference package:

pip install tabpfn

Fitting a classifier and predicting looks like this:

from sklearn.datasets import load_breast_cancer
from sklearn.model_selection import train_test_split
from tabpfn import TabPFNClassifier

# Load data
X, y = load_breast_cancer(return_X_y=True)
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.5, random_state=42)

# Initialize a classifier
clf = TabPFNClassifier()  # Uses TabPFN 2.5 weights, finetuned on real data.
clf.fit(X_train, y_train)


# Predict probabilities
prediction_probabilities = clf.predict_proba(X_test)
# Predict labels
predictions = clf.predict(X_test)
print("Accuracy", accuracy_score(y_test, predictions))

For more examples (e.g. how to train a regressor), see the github repo: https://github.com/PriorLabs/tabPFN!

Developers & Affiliations

Developed by Prior Labs.

Intended Use

Regression and classification tasks with ≤50 000 samples and ≤2000 features in structured tabular format.

Not Intended Use

• Not suitable for unstructured data (text, images); use API version for textual features.
• Not tested for >50 000 samples or > 2000 features.

Model Architecture

Transformer with TabPFNv2-like alternating attention with 18-24 layers

Training Data and Priors

• TabPFN-2.5: trained purely on synthetic tabular tasks
• Real-TabPFN-2.5: continued pre-training on real-world datasets (for details please see Appendix C.1 of the model tech report).

Performance Benchmarks

Evaluated on proprietary benchmark collection, TabArena, and RealCause (for a causal version), in each of which it yields new SOTA results by a

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