README.md
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1 ---
2 pipeline_tag: tabular-regression
3 library_name: tabpfn
4 license: other
5 license_name: priorlabs-1-1
6 license_link: https://github.com/PriorLabs/TabPFN/blob/49394b053a6759cfe68e90c21a2d51c31b396768/LICENSE
7 ---
8
9 # TabPFN v2: A Tabular Foundation Model
10
11 TabPFN is a transformer-based foundation model for tabular data that leverages prior-data based learning to achieve strong performance on small tabular regression tasks without requiring task-specific training.
12
13 ## Installation
14 ```bash
15 pip install tabpfn
16 ```
17
18 ## Model Details
19 - **Developed by:** Prior Labs
20 - **Model type:** Transformer-based foundation model for tabular data
21 - **License:** [Prior Labs License (Apache 2.0 with additional attribution requirement)](https://priorlabs.ai/tabpfn-license/)
22 - **Paper:** Published in Nature (January 2025)
23 - **Repository:** [GitHub - priorlabs/tabpfn](https://github.com/priorlabs/tabpfn)
24
25 ### 📚 Citation
26
27 ```bibtex
28 @article{hollmann2025tabpfn,
29 title={Accurate predictions on small data with a tabular foundation model},
30 author={Hollmann, Noah and M{\"u}ller, Samuel and Purucker, Lennart and
31 Krishnakumar, Arjun and K{\"o}rfer, Max and Hoo, Shi Bin and
32 Schirrmeister, Robin Tibor and Hutter, Frank},
33 journal={Nature},
34 year={2025},
35 month={01},
36 day={09},
37 doi={10.1038/s41586-024-08328-6},
38 publisher={Springer Nature},
39 url={https://www.nature.com/articles/s41586-024-08328-6},
40 }
41 ```
42
43 ## Quick Start
44
45 📚 For detailed usage examples and best practices, check out:
46 - [Interactive Colab Tutorial](https://tinyurl.com/tabpfn-colab-api)
47
48
49 ## Technical Requirements
50 - Python ≥ 3.9
51 - PyTorch ≥ 2.1
52 - scikit-learn ≥ 1.0
53 - Hardware: 16GB+ RAM, CPU (GPU optional)
54
55 ## Limitations
56 - Not designed for very large datasets
57 - Not suitable for non-tabular data formats
58
59 ## Resources
60 - **Documentation:** https://priorlabs.ai/docs
61 - **Source:** https://github.com/priorlabs/tabpfn
62 - **Paper:** https://www.nature.com/articles/s41586-024-08328-6
63
64 ### Team
65 - Noah Hollmann
66 - Samuel Müller
67 - Lennart Purucker
68 - Arjun Krishnakumar
69 - Max Körfer
70 - Shi Bin Hoo
71 - Robin Tibor Schirrmeister
72 - Frank Hutter
73 - Eddie Bergman
74 - Léo Grinsztajn