Eval Results
precision recall f1-score support
Alakasiz 0.87 0.91 0.89 734
Barinma 0.79 0.89 0.84 207
Elektronik 0.69 0.83 0.75 130
Giysi 0.71 0.81 0.76 94
Kurtarma 0.82 0.85 0.83 362
Lojistik 0.57 0.67 0.62 112
Saglik 0.68 0.85 0.75 108
Su 0.56 0.76 0.64 78
Yagma 0.60 0.77 0.68 31
Yemek 0.71 0.89 0.79 117
micro avg 0.77 0.86 0.81 1973
macro avg 0.70 0.82 0.76 1973
weighted avg 0.78 0.86 0.82 1973
samples avg 0.83 0.88 0.84 1973
Training Params:
{'per_device_train_batch_size': 32,
'per_device_eval_batch_size': 32,
'learning_rate': 5.8679699888213376e-05,
'weight_decay': 0.03530961718117487,
'num_train_epochs': 4,
'lr_scheduler_type': 'cosine',
'warmup_steps': 40,
'seed': 42,
'fp16': True,
'load_best_model_at_end': True,
'metric_for_best_model': 'macro f1',
'greater_is_better': True
}
Threshold:
Class Loss Weights
[1.0,
1.5167249178108022,
1.7547338578655642,
1.9610520059358458,
1.8684086209021484,
1.8019018017117145,
2.110648663094536,
3.081208739200435,
1.7994815143101963]
