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Module 0 Scene Encoder
Joint pretraining for movie-scene understanding with:
- supervised contrastive learning
- masked language modeling on annotation scene_text
- auxiliary scene-label prediction
Backbone
- base_model: microsoft/deberta-v3-base
- embedding_dim: 256
Dataset Summary
- all_scenes: 11118
- train_scenes: 9593
- val_scenes: 1525
- all_films: 80
- train_positive_avg_candidates: 8.0
- val_positive_avg_candidates: 7.9993
- mlm_chunks: 13596
Best Validation Metrics
- val_loss: 3.0135
- val_contrastive_loss: 2.5612
- val_aux_loss: 0.6957
- val_r_at_1: 0.1348
- avg_cls_acc: 0.5545
- avg_reg_mae: 0.8708
- avg_bin_acc: 0.6279
- val_task/emotional_valence: 1.0642
- val_task/scene_interaction_tone: 1.1227
- val_task/conflict_nature: 1.1504
- val_task/acoustic_space: 1.4399
- val_task/reality_layer: 0.2565
- val_task/score_dynamic_shape: 1.2453
- val_task/narrative_arc_position: 1.0132
- val_task/foreshadowing_type: 1.2081
- val_task/transition_type: 1.1950
- val_task/scene_tension_raw: 0.3548
- val_task/scene_arousal: 0.3117
- val_task/scene_valence_continuous: 0.2986
- val_task/pacing_intensity: 0.2708
- val_task/action_intensity: 0.3631
- val_task/emotional_shift_trigger: 0.6338
- val_task/emotion_tags: 0.4502
Intended Use
Use this encoder as the backbone for Module 1 by setting:
CFG["backbone"] = "/kaggle/working/module0_backbone"
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