Update README.md

README.md

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----
-license: apache-2.0
----
+---
+license: apache-2.0
+pipeline_tag: tabular-classification
+---
+
+# Mitra Classifier
+
+Mitra classifier is a tabular foundation model that is pre-trained on purely synthetic datasets sampled from a mix of random classifiers. 
+
+## Architecture
+
+Mitra is based on a 12-layer Transformer of 72 M parameters, pre-trained by incorporating an in-context learning paradigm.
+
+## Usage
+
+To use Mitra classifier, install AutoGluon by running:
+
+```sh
+pip install uv
+uv pip install autogluon.tabular[mitra]   
+```
+
+A minimal example showing how to perform inference using the Mitra classifier:
+
+```python
+import pandas as pd
+from autogluon.tabular import TabularDataset, TabularPredictor
+from sklearn.model_selection import train_test_split
+from sklearn.datasets import load_wine
+
+# Load datasets
+wine_data = load_wine()
+wine_df = pd.DataFrame(wine_data.data, columns=wine_data.feature_names)
+wine_df['target'] = wine_data.target
+
+print("Dataset shapes:")
+print(f"Wine: {wine_df.shape}")
+
+# Create train/test splits (80/20)
+wine_train, wine_test = train_test_split(wine_df, test_size=0.2, random_state=42, stratify=wine_df['target'])
+
+print("Training set sizes:")
+print(f"Wine: {len(wine_train)} samples")
+
+# Convert to TabularDataset
+wine_train_data = TabularDataset(wine_train)
+wine_test_data = TabularDataset(wine_test)
+
+# Create predictor with Mitra
+print("Training Mitra classifier on classification dataset...")
+mitra_predictor = TabularPredictor(label='target')
+mitra_predictor.fit(
+    wine_train_data,
+    hyperparameters={
+        'MITRA': {'fine_tune': False}
+    },
+   )
+
+print("\nMitra training completed!")
+
+# Make predictions
+mitra_predictions = mitra_predictor.predict(wine_test_data)
+print("Sample Mitra predictions:")
+print(mitra_predictions.head(10))
+
+# Show prediction probabilities for first few samples
+mitra_predictions = mitra_predictor.predict_proba(wine_test_data)
+print(mitra_predictions.head())
+
+# Show model leaderboard
+print("\nMitra Model Leaderboard:")
+mitra_predictor.leaderboard(wine_test_data)
+```
+
+A minimal example showing how to perform fine-tuning using the Mitra classifier:
+
+```python
+mitra_predictor_ft = TabularPredictor(label='target')
+mitra_predictor_ft.fit(
+    wine_train_data,
+    hyperparameters={
+        'MITRA': {'fine_tune': True, 'fine_tune_steps': 10}
+    },
+    time_limit=120,  # 2 minutes
+   )
+
+print("\nMitra fine-tuning completed!")
+
+# Show model leaderboard
+print("\nMitra Model Leaderboard:")
+mitra_predictor_ft.leaderboard(wine_test_data)
+```
+
+## License
+
+This project is licensed under the Apache-2.0 License.
+
+## Reference
+
+```
+@article{zhang2025mitra,
+  title={Mitra: Mixed synthetic priors for enhancing tabular foundation models},
+  author={Zhang, Xiyuan and Maddix, Danielle C and Yin, Junming and Erickson, Nick and Ansari, Abdul Fatir and Han, Boran and Zhang, Shuai and Akoglu, Leman and Faloutsos, Christos and Mahoney, Michael W and others},
+  journal={arXiv preprint arXiv:2510.21204},
+  year={2025}
+}
+```
+
+Amazon Science blog: [Mitra: Mixed synthetic priors for enhancing tabular foundation models](https://www.amazon.science/blog/mitra-mixed-synthetic-priors-for-enhancing-tabular-foundation-models?utm_campaign=mitra-mixed-synthetic-priors-for-enhancing-tabular-foundation-models&utm_medium=organic-asw&utm_source=linkedin&utm_content=2025-7-22-mitra-mixed-synthetic-priors-for-enhancing-tabular-foundation-models&utm_term=2025-july)