CAM-AH / ensemble_config.json
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Add CAM-AH ensemble-5 original weights (official Macro-F1 0.7456 @ thr 0.595)
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{
"name": "CAM-AH",
"full_name": "Cross-Attention-Multimodal-Ambivalence-Hesitation",
"task": "video-level binary classification (1 = ambivalence/hesitancy present)",
"members": [
"members/20260711_191444/best-epoch=00-val_ap=0.8790.ckpt",
"members/20260711_191555/best-epoch=00-val_ap=0.8749.ckpt",
"members/20260711_191626/best-epoch=01-val_ap=0.8639.ckpt",
"members/20260711_191656/best-epoch=00-val_ap=0.8769.ckpt",
"members/20260711_191725/best-epoch=01-val_ap=0.8747.ckpt"
],
"seeds": [42, 1, 2, 3, 4],
"state_dict_prefix": "model.",
"combine": "mean of per-video sigmoid probabilities",
"threshold": 0.595,
"threshold_note": "Fixed decision threshold used in the best official submission (predict 1 if mean probability >= 0.595). Overrides the per-member thresholds stored in each trainer_state.json.",
"architecture": {
"dim_a": 320,
"dim_b": 768,
"dim_tab": 74,
"common_dim": 512,
"num_heads": 8,
"dropout": 0.3,
"pool": "attention",
"tab_fusion": "token",
"use_tabular": true,
"num_outputs": 1
},
"inputs": {
"window": {"size_s": 5.0, "hop_s": 2.5},
"feat_a": "librosa prosodic features per window (80 features x 4 stats = 320-d)",
"feat_b": "cardiffnlp/twitter-roberta-base-emotion, masked mean pool + L2 norm, max_length 128 (768-d)",
"feat_tab": "74 support features per window: question type (7) + acoustic hesitation (18) + textual A/H cues (49)",
"key_padding_mask": "(B, T) bool, True = padded window"
},
"training": {
"train_splits": ["train", "val"],
"n_train_videos": 902,
"monitor_split": "test (internal, 525 videos)",
"monitor": "val_ap (max)",
"optimizer": "AdamW",
"lr": 3e-4,
"weight_decay": 0.05,
"gradient_clip_val": 1.0,
"batch_size": 32,
"max_epochs": 400,
"early_stopping_patience": 20
},
"results": {
"official_private_test": {"n_videos": 152, "macro_f1": 0.745595, "average_precision": 0.818211, "threshold": 0.595},
"internal_test_525": {"average_precision": 0.877}
}
}