README.md

# 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