Add dataset card with curriculum learning information

README.md

@@ -1,73 +1,39 @@
----
-dataset_info:
-  features:
-  - name: audio
-    dtype: audio
-  - name: label
-    dtype: int64
-  - name: valence
-    dtype: float64
-  - name: arousal
-    dtype: float64
-  - name: arousal_norm
-    dtype: float64
-  - name: valence_norm
-    dtype: float64
-  - name: speakerID
-    dtype: int64
-  - name: utterance
-    dtype: string
-  - name: transcript
-    dtype: string
-  - name: emotion
-    dtype: string
-  - name: difficulty
-    dtype: float64
-  - name: curriculum_order
-    dtype: int64
-  - name: overall_agreement
-    dtype: float64
-  - name: fleiss_kappa
-    dtype: float64
-  - name: krippendorff_alpha
-    dtype: float64
-  - name: valence_std
-    dtype: float64
-  - name: arousal_std
-    dtype: float64
-  - name: dominance_std
-    dtype: float64
-  - name: valence_icc
-    dtype: float64
-  - name: arousal_icc
-    dtype: float64
-  - name: dominance_icc
-    dtype: float64
-  - name: n_categorical_evaluators
-    dtype: int64
-  - name: n_dimensional_evaluators
-    dtype: int64
-  - name: consensus_valence
-    dtype: float64
-  - name: consensus_arousal
-    dtype: float64
-  - name: consensus_dominance
-    dtype: float64
-  - name: naturalness_std
-    dtype: float64
-  - name: naturalness_icc
-    dtype: float64
-  - name: consensus_naturalness
-    dtype: float64
-  splits:
-  - name: train
-    num_bytes: 2803902170.0
-    num_examples: 7798
-  download_size: 2739152711
-  dataset_size: 2803902170.0
-configs:
-- config_name: default
-  data_files:
-  - split: train
-    path: data/train-*
----
+
+# 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