• This Python package allows you to interact with pre-trained foundation models for tabular data.
• Easily fine-tune them on your classification and regression use cases in a single line of code.
• Interested in what we're building? Join our waitlist.

1. Install Future Frame with pip – more details on our PyPI page.

pip install futureframe

2. Download model weights here and store the weights folder in your working directory.

Use Future Frame to fine-tune a pre-trained foundation model on a classification task.

# Import standard libraries
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.metrics import roc_auc_score

# Import Future Frame
import futureframe as ff

# Import data
dataset_name = "tests/data/churn.csv"
target_variable = "Churn"
df = pd.read_csv(dataset_name)

# Split data
X, y = df.drop(columns=[target_variable]), df[target_variable]
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)

# Fine-tune a pre-trained classifier with Future Frame
model = ff.models.CM2Classifier()
model.finetune(X_train, y_train)

# Make predictions with Future Frame
y_pred = model.predict(X_test)

# Evaluate your model
auc = roc_auc_score(y_test, y_pred)
print(f"AUC: {auc:0.2f}")

More foundation models will be integrated into the library soon. Stay stuned by joining our waitlist!

• Future Frame Official Website
• futureframe PyPI Page
• futureframe GitHub Repository
• Documentation: coming soon!

• We are currently under heavy development.
• If you'd like to contribute, please send us an email at eduardo(at)futureframe.ai.
• To report a bug, please write an issue.