| --- |
| license: other |
| license_name: tabpfn-3-license-v1.0 |
| license_link: LICENSE |
| pipeline_tag: tabular-regression |
| tags: |
| - tabular |
| - regression |
| - agriculture |
| - maize-yield |
| - tabpfn-3 |
| --- |
| |
| ### Model Overview |
| `tabpfn-v3-regressor-cn_finetuned_full.ckpt` is the **full training-snapshot** of the |
| fine-tuned derivative of [TabPFN-v3](https://github.com/PriorLabs/TabPFN) |
| (`tabpfn-v3-regressor-v3_default`), fine-tuned on China provincial maize yield data |
| (new dataset: `feature_dataset.csv`). |
|
|
| **This is the resumable checkpoint.** Besides the fine-tuned `state_dict` it also |
| contains the **AdamW optimizer state** (`exp_avg` / `exp_avg_sq`), epoch and loss |
| metadata — i.e. the full ~444 MB of optimizer buffers that make a 668 MB file. Use this |
| file to **continue fine-tuning** from the best epoch. |
|
|
| ### Fine-tuning summary |
| - Data: `feature_dataset.csv`, years 2003–2022, 602 rows, 31 provinces |
| - Split: train 2003–2016 (422) / val 2017–2019 (90) / test 2020–2022 (90); 96 features |
| - Best **val MSE 0.2716** at epoch 26 of 30 (early-stop patience 5) |
| - lr 3e-6, wd 0.01, MSE 1.0 / CRPS 1.0 / MAE 0.3, context 256, seed 42 |
|
|
| ### Internal test (2020–2022, 90 rows, 16-ensemble) vs base model |
|
|
| | Metric | Base | CN-finetuned | |
| | --- | --- | --- | |
| | RMSE | 0.6865 | **0.6207** | |
| | R² | 0.6308 | **0.6981** | |
| | MAE | 0.4592 | **0.4494** | |
| | Pearson r | 0.8681 | **0.8914** | |
| | KGE | 0.8507 | **0.8554** | |
|
|
| ### Usage for inference |
| ```python |
| from tabpfn import TabPFNRegressor |
| reg = TabPFNRegressor(model_path="tabpfn-v3-regressor-cn_finetuned_full.ckpt", |
| device="cuda", n_estimators=16, random_state=42) |
| reg.fit(X_train, y_train) |
| y_pred = reg.predict(X_test) |
| ``` |
|
|
| ### File integrity |
| - Full snapshot ckpt SHA-256: `53119ca48958a7b7eb5ca06752eecc2348f0b2ffa58d45d4f5ae79f02793f630` |
|
|
| ### Licensing |
|
|
| Model weights are released under the **TABPFN-3 Non-Commercial License v1.0**. The full |
| license text is included in [`LICENSE`](LICENSE) and must be passed on to any third party |
| who receives this model or a derivative of it. |
|
|
| **This model is a modified derivative.** `tabpfn-v3-regressor-cn_finetuned_full.ckpt` is a |
| fine-tuned version of `tabpfn-v3-regressor-v3_default`. It is **not** an official product |
| of Prior Labs GmbH and has not been endorsed, approved, or validated by the Company. |
|
|
| Research and internal evaluation are allowed; **any commercial or production use is |
| prohibited**. For commercial use contact sales@priorlabs.ai. |
|
|
| Per §3.d of the license, this model and its derivatives must not be distributed, hosted, |
| or made available as part of a hosted, managed, API, or SaaS service. |
|
|
| Required attribution notice: |
|
|
| ``` |
| The TABPFN-3 Model is licensed by Prior Labs GmbH under the TABPFN-3 Non-Commercial License. |
| Copyright © Prior Labs GmbH 2026. |
| THE SERVICES ARE PROVIDED FREE OF CHARGE: COMPANY SHALL NOT BE LIABLE FOR DAMAGES RESULTING FROM SLIGHT NEGLIGENCE. LIABILITY FOR GROSS NEGLIGENCE AND INTENTIONAL MISCONDUCT REMAINS UNAFFECTED. |
| ``` |
|
|
