Text Features

TabPFN-3.5 can use free-text columns together with the rest of your table to predict a target. For example, support ticket notes can contribute to churn predictions, reviewer notes to insurance claim predictions, and product descriptions to demand estimates.

Local package and API

Access	Model	Text support
Local `tabpfn` package	TabPFN-3.5	Supports text columns directly alongside numerical and categorical features.
Hosted API	TabPFN-3.5-Plus and TabPFN-3.5-Thinking	Adds enhanced text processing for stronger results on text-rich datasets.

Use a package release with TabPFN-3.5 support. See the quickstart for installation, authentication, and model selection.

Plus text limits

TabPFN-3.5-Plus text preprocessing supports at most 10 million tokens in total and 2,500 characters per text value. These limits apply in addition to the model’s row and column limits.

Pass a mixed table

Keep text in its original columns. You do not need to manually vectorize it before passing it to TabPFN-3.5. For a churn classification task, a customer dataset might contain:

Column	Example value	Role
`support_notes`	`Customer reported repeated service outages.`	Text feature
`plan`	`business`	Categorical feature
`tenure_months`	`18`	Numerical feature
`churned`	`1`	Target to predict

Load your data and split it into training and test sets:

import pandas as pd
from sklearn.model_selection import train_test_split

data = pd.read_csv(
    "customers.csv",
    dtype={"support_notes": "string", "plan": "category"},
)
X = data[["support_notes", "plan", "tenure_months"]]
y = data["churned"]

X_train, X_test, y_train, y_test = train_test_split(
    X, y, test_size=0.2, random_state=42, stratify=y
)

Set text columns to the pandas string dtype so they can be detected as text, including on pandas 2. Declaring plan as category keeps it categorical regardless of how many distinct values it contains. Select TabPFN-3.5 locally or TabPFN-3.5-Plus through the API:

from tabpfn import TabPFNClassifier
from tabpfn.constants import ModelVersion

model = TabPFNClassifier.create_default_for_version(ModelVersion.V3_5)
model.fit(X_train, y_train)
predictions = model.predict(X_test)

from tabpfn_client import TabPFNClassifier

model = TabPFNClassifier.create_default_for_version("v3.5")
model.fit(X_train, y_train)
predictions = model.predict(X_test)

For a numerical target, use TabPFNRegressor from the same package. The feature table can still contain text, categorical labels, and numerical values.

Preparing text columns

Keep text features alongside the other relevant columns, so the model can use both the text and the structured data.
Use the same feature columns when fitting and predicting.
Include only text available at prediction time. For example, notes written after a customer churns should not be inputs to a prediction of that churn.
Start with raw text. Evaluate manual vectorization or domain-specific text features against that baseline on held-out data, and fit learned preprocessing only on the training split.

See Preprocessing for text-input guidance and Benchmarking for comparing models with different preprocessing.

Getting Started

Metering and rate limits

Capabilities

Agentic

Integrations

Troubleshooting

TabPFN in Action

Customer References

Changelog

Local package and API

Plus text limits

Pass a mixed table

Preparing text columns

​Local package and API

​Plus text limits

​Pass a mixed table

​Preparing text columns

Local package and API

Plus text limits

Pass a mixed table

Preparing text columns