Create README.md

README.md

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+# Model Card for Mozart vs Beethoven Classifier
+
+This model predicts whether a classical piano piece was composed by **Mozart** or **Beethoven**, based on numerical features extracted from the score (counts of right-hand notes, left-hand notes, measures, key centers, and markings).
+
+## Model Details
+
+### Model Description
+
+- **Developed by:** Scotty McGee (PhD student, CMU)
+- **Shared by [optional]:** Scotty McGee
+- **Model type:** Tabular classification (AutoML with AutoGluon)
+- **Language(s) (NLP):** Not applicable (tabular/numeric features only)
+- **License:** MIT (update as needed)
+- **Finetuned from model:** N/A
+
+## Uses
+
+### Direct Use
+Demonstration of machine-learning classification on musical data. Predicts a binary composer label (Mozart or Beethoven) from numeric score features.
+
+### Downstream Use [optional]
+Could be adapted for broader composer classification tasks, musicology studies, or automated metadata tagging.
+
+### Out-of-Scope Use
+- Not intended as a general music recognition tool.
+- Not reliable for real performance or music audio classification.
+- Not suitable for commercial music rights enforcement.
+
+## Bias, Risks, and Limitations
+- Limited to Mozart and Beethoven; not generalizable to other composers.
+- Features are simplistic (counts of notes, measures, key centers, markings).
+- May not capture stylistic nuance.
+- Risk of overfitting to the dataset used.
+
+### Recommendations
+Use for small-scale experiments and demos. Do not apply to large-scale music classification tasks without retraining and validation.
+
+## How to Get Started with the Model
+
+```python
+from autogluon.tabular import TabularPredictor
+preds = predictor.predict(df_test)  # Mozart or Beethoven