Update README.md

README.md

@@ -1,9 +1,95 @@
 ---
-tags:
-- model_hub_mixin
-- pytorch_model_hub_mixin
+license: apache-2.0
+pipeline_tag: tabular-regression
 ---
 
-This model has been pushed to the Hub using the [PytorchModelHubMixin](https://huggingface.co/docs/huggingface_hub/package_reference/mixins#huggingface_hub.PyTorchModelHubMixin) integration:
-- Library: [More Information Needed]
-- Docs: [More Information Needed]
+# TabPFNMix Regressor
+
+TabPFNMix regressor is a tabular foundation model that is pre-trained on purely synthetic datasets sampled from a mix of random regressors. 
+
+## Architecture
+
+TabPFNMix is based on a 12-layer encoder-decoder Transformer of 37 M parameters. We use a pre-training strategy incorporating in-context learning, similar to that used by TabPFN and TabForestPFN.
+
+## Usage
+
+To use TabPFNMix regressor, install AutoGluon by running:
+
+```sh
+pip install autogluon
+```
+
+A minimal example showing how to perform fine-tuning and inference using TabPFNMix regressor
+
+```python
+import pandas as pd
+
+from autogluon.tabular import TabularPredictor
+
+
+if __name__ == '__main__':  
+    train_data = pd.read_csv('https://autogluon.s3.amazonaws.com/datasets/Inc/train.csv')
+    subsample_size = 5000
+    if subsample_size is not None and subsample_size < len(train_data):
+        train_data = train_data.sample(n=subsample_size, random_state=0)
+    test_data = pd.read_csv('https://autogluon.s3.amazonaws.com/datasets/Inc/test.csv')
+
+    tabpfnmix_default = {
+        "model_path_classifier": "autogluon/tabpfn-mix-1.0-classifier",
+        "model_path_regressor": "autogluon/tabpfn-mix-1.0-regressor",
+        "n_ensembles": 1,
+        "max_epochs": 30,
+    }
+
+    hyperparameters = {
+        "TABPFNMIX": [
+            tabpfnmix_default,
+        ],
+    }
+
+    label = "age"
+    problem_type = "regression"
+
+    predictor = TabularPredictor(
+        label=label,
+        problem_type=problem_type,
+    )
+    predictor = predictor.fit(
+        train_data=train_data,
+        hyperparameters=hyperparameters,
+        verbosity=3,
+    )
+
+    predictor.leaderboard(test_data, display=True)
+```
+
+## Citation
+
+If you find TabPFNMix useful for your research, please consider citing the associated papers:
+
+```
+@article{erickson2020autogluon,
+  title={Autogluon-tabular: Robust and accurate automl for structured data},
+  author={Erickson, Nick and Mueller, Jonas and Shirkov, Alexander and Zhang, Hang and Larroy, Pedro and Li, Mu and Smola, Alexander},
+  journal={arXiv preprint arXiv:2003.06505},
+  year={2020}
+}
+
+@article{hollmann2022tabpfn,
+  title={Tabpfn: A transformer that solves small tabular classification problems in a second},
+  author={Hollmann, Noah and M{\"u}ller, Samuel and Eggensperger, Katharina and Hutter, Frank},
+  journal={arXiv preprint arXiv:2207.01848},
+  year={2022}
+}
+
+@article{breejen2024context,
+  title={Why In-Context Learning Transformers are Tabular Data Classifiers},
+  author={Breejen, Felix den and Bae, Sangmin and Cha, Stephen and Yun, Se-Young},
+  journal={arXiv preprint arXiv:2405.13396},
+  year={2024}
+}
+```
+
+## License
+
+This project is licensed under the Apache-2.0 License.