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# 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/MSPI_WAV_Diff_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
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