# 💠Future Frame
Empowering Data Scientists with Foundation Models for Tabular Data
- 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](https://futureframe.ai/)!
## Installation
1. Install Future Frame with `pip` – more details on our [PyPI page](https://pypi.org/project/futureframe/).
```bash
pip install futureframe
```
## Quick Start
Use Future Frame to fine-tune a pre-trained foundation model on a classification task.
```python linenums="1"
# 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 = "https://raw.githubusercontent.com/futureframeai/futureframe/main/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)
# Fine-tune a pre-trained classifier with Future Frame
model = ff.models.cm2.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}")
```
## Models
| Model Name | Paper Title | Paper | GitHub |
| ---------- | ---------------------------------------------------------- | --------------------------------------------------- | -------------------------------------- |
| CM2 | Towards Cross-Table Masked Pretraining for Web Data Mining | [Ye et al., 2024](https://arxiv.org/abs/2307.04308) | [Link](https://github.com/Chao-Ye/CM2) |
More foundation models will be integrated into the library soon. Stay tuned by joining our [waitlist](https://futureframe.ai/)!
## Links
- [Future Frame Official Website](https://futureframe.ai/)
- [Future Frame API Reference](https://futureframe.ai/api-reference/)
- [`futureframe` PyPI Page](https://pypi.python.org/pypi/futureframe)
- [`futureframe` GitHub Repository](https://github.com/futureframeai/futureframe)
- [`futureframe` Documentation](https://futureframe.ai/docs/)
## Contributing
- We are currently under heavy development.
- If you'd like to contribute, please send us an email at <i>eduardo(at)futureframe.ai</i>.
- To report a bug, please write an [issue](https://github.com/futureframeai/futureframe/issues/new).
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