# IEMOCAP with Curriculum Learning Metrics

This dataset enhances the original IEMO_WAV_Diff_2 dataset with inter-evaluator agreement metrics 
for curriculum learning following Lotfian & Busso (2019).

## Additional Columns

- `curriculum_order`: Training order (1=highest agreement, train first)
- `overall_agreement`: Combined agreement score (0-1, higher is better)
- `fleiss_kappa`: Categorical agreement (-1 to 1, higher is better)
- `krippendorff_alpha`: Krippendorff's alpha for categorical reliability
- `valence_std`, `arousal_std`, `dominance_std`: Standard deviation of dimensional ratings (lower is better)
- `valence_icc`, `arousal_icc`, `dominance_icc`: Intraclass correlation coefficients (0-1, higher is better)
- `n_categorical_evaluators`, `n_dimensional_evaluators`: Number of evaluators
- `consensus_valence`, `consensus_arousal`, `consensus_dominance`: Consensus dimensional ratings

## Usage for Curriculum Learning

Sort samples by `curriculum_order` and train on high-agreement samples first:

```python
from datasets import load_dataset

dataset = load_dataset("cairocode/IEMO_WAV_Diff_2_Curriculum")
train_data = dataset["train"].sort("curriculum_order")

# Start with high agreement samples
easy_samples = train_data.filter(lambda x: x["overall_agreement"] > 0.5)
hard_samples = train_data.filter(lambda x: x["overall_agreement"] < 0.5)
```

## Citation

If you use this dataset, please cite:

- Original IEMOCAP: Busso et al. (2008)
- Curriculum learning approach: Lotfian & Busso (2019)
- Original dataset: cairocode/IEMO_WAV_Diff_2