| # 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 | |