Update README.md

README.md

@@ -2,7 +2,6 @@
 pipeline_tag: tabular-classification
 ---
 
-
 # TabPFN v2: A Tabular Foundation Model
 
 TabPFN is a transformer-based foundation model for tabular data that leverages prior-data based learning to achieve strong performance on small tabular datasets without requiring task-specific training.
@@ -19,18 +18,29 @@ pip install tabpfn
 - **Paper:** Published in Nature (January 2024)
 - **Repository:** [GitHub - priorlabs/tabpfn](https://github.com/priorlabs/tabpfn)
 
-### Citation
-TBD
+### 📚 Citation
+
+```bibtex
+@article{hollmann2024tabpfn,
+ title={Accurate predictions on small data with a tabular foundation model},
+ author={Hollmann, Noah and M{\"u}ller, Samuel and Purucker, Lennart and
+         Krishnakumar, Arjun and K{\"o}rfer, Max and Hoo, Shi Bin and
+         Schirrmeister, Robin Tibor and Hutter, Frank},
+ journal={Nature},
+ year={2024},
+ month={01},
+ day={09},
+ doi={10.1038/s41586-024-08328-6},
+ publisher={Springer Nature},
+ url={https://www.nature.com/articles/s41586-024-08328-6},
+}
+```
+
 
 ## Quick Start
-```python
-from tabpfn import TabPFNClassifier
 
-# Initialize model
-classifier = TabPFNClassifier()
-classifier.fit(X_train, y_train)
-predictions = classifier.predict(X_test)
-```
+📚 For detailed usage examples and best practices, check out:
+- [Interactive Colab Tutorial](https://tinyurl.com/tabpfn-colab-local)
 
 ## Technical Requirements
 - Python ≥ 3.9