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harun27
/
overall_binary

Text Classification
Transformers
Safetensors
modernbert
Generated from Trainer
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overall_binary

This model is a fine-tuned version of answerdotai/ModernBERT-large on an unknown dataset. It achieves the following results on the evaluation set:

  • Loss: 2.0200
  • Classification Report: {'0': {'precision': 0.6451612903225806, 'recall': 0.9090909090909091, 'f1-score': 0.7547169811320755, 'support': 22.0}, '1': {'precision': 0.9130434782608695, 'recall': 0.65625, 'f1-score': 0.7636363636363637, 'support': 32.0}, 'accuracy': 0.7592592592592593, 'macro avg': {'precision': 0.7791023842917251, 'recall': 0.7826704545454546, 'f1-score': 0.7591766723842196, 'support': 54.0}, 'weighted avg': {'precision': 0.8039062905823073, 'recall': 0.7592592592592593, 'f1-score': 0.7600025411346166, 'support': 54.0}}

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

  • learning_rate: 5e-06
  • train_batch_size: 10
  • eval_batch_size: 10
  • seed: 42
  • distributed_type: multi-GPU
  • num_devices: 4
  • total_train_batch_size: 40
  • total_eval_batch_size: 40
  • optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
  • lr_scheduler_type: linear
  • lr_scheduler_warmup_ratio: 0.1
  • num_epochs: 100

Training results

Training Loss Epoch Step Validation Loss Classification Report
No log 1.0 5 1.0506 {'0': {'precision': 0.0, 'recall': 0.0, 'f1-score': 0.0, 'support': 22.0}, '1': {'precision': 0.5925925925925926, 'recall': 1.0, 'f1-score': 0.7441860465116279, 'support': 32.0}, 'accuracy': 0.5925925925925926, 'macro avg': {'precision': 0.2962962962962963, 'recall': 0.5, 'f1-score': 0.37209302325581395, 'support': 54.0}, 'weighted avg': {'precision': 0.3511659807956104, 'recall': 0.5925925925925926, 'f1-score': 0.4409991386735573, 'support': 54.0}}
No log 2.0 10 0.6791 {'0': {'precision': 0.5, 'recall': 0.22727272727272727, 'f1-score': 0.3125, 'support': 22.0}, '1': {'precision': 0.6136363636363636, 'recall': 0.84375, 'f1-score': 0.7105263157894737, 'support': 32.0}, 'accuracy': 0.5925925925925926, 'macro avg': {'precision': 0.5568181818181819, 'recall': 0.5355113636363636, 'f1-score': 0.5115131578947368, 'support': 54.0}, 'weighted avg': {'precision': 0.5673400673400674, 'recall': 0.5925925925925926, 'f1-score': 0.5483674463937622, 'support': 54.0}}
No log 3.0 15 0.7922 {'0': {'precision': 0.4074074074074074, 'recall': 1.0, 'f1-score': 0.5789473684210527, 'support': 22.0}, '1': {'precision': 0.0, 'recall': 0.0, 'f1-score': 0.0, 'support': 32.0}, 'accuracy': 0.4074074074074074, 'macro avg': {'precision': 0.2037037037037037, 'recall': 0.5, 'f1-score': 0.2894736842105263, 'support': 54.0}, 'weighted avg': {'precision': 0.16598079561042522, 'recall': 0.4074074074074074, 'f1-score': 0.23586744639376217, 'support': 54.0}}
No log 4.0 20 0.6558 {'0': {'precision': 1.0, 'recall': 0.09090909090909091, 'f1-score': 0.16666666666666666, 'support': 22.0}, '1': {'precision': 0.6153846153846154, 'recall': 1.0, 'f1-score': 0.7619047619047619, 'support': 32.0}, 'accuracy': 0.6296296296296297, 'macro avg': {'precision': 0.8076923076923077, 'recall': 0.5454545454545454, 'f1-score': 0.46428571428571425, 'support': 54.0}, 'weighted avg': {'precision': 0.7720797720797721, 'recall': 0.6296296296296297, 'f1-score': 0.519400352733686, 'support': 54.0}}
No log 5.0 25 0.6612 {'0': {'precision': 0.0, 'recall': 0.0, 'f1-score': 0.0, 'support': 22.0}, '1': {'precision': 0.5925925925925926, 'recall': 1.0, 'f1-score': 0.7441860465116279, 'support': 32.0}, 'accuracy': 0.5925925925925926, 'macro avg': {'precision': 0.2962962962962963, 'recall': 0.5, 'f1-score': 0.37209302325581395, 'support': 54.0}, 'weighted avg': {'precision': 0.3511659807956104, 'recall': 0.5925925925925926, 'f1-score': 0.4409991386735573, 'support': 54.0}}
No log 6.0 30 0.6382 {'0': {'precision': 0.5526315789473685, 'recall': 0.9545454545454546, 'f1-score': 0.7, 'support': 22.0}, '1': {'precision': 0.9375, 'recall': 0.46875, 'f1-score': 0.625, 'support': 32.0}, 'accuracy': 0.6666666666666666, 'macro avg': {'precision': 0.7450657894736843, 'recall': 0.7116477272727273, 'f1-score': 0.6625, 'support': 54.0}, 'weighted avg': {'precision': 0.780701754385965, 'recall': 0.6666666666666666, 'f1-score': 0.6555555555555556, 'support': 54.0}}
No log 7.0 35 0.6408 {'0': {'precision': 0.0, 'recall': 0.0, 'f1-score': 0.0, 'support': 22.0}, '1': {'precision': 0.5925925925925926, 'recall': 1.0, 'f1-score': 0.7441860465116279, 'support': 32.0}, 'accuracy': 0.5925925925925926, 'macro avg': {'precision': 0.2962962962962963, 'recall': 0.5, 'f1-score': 0.37209302325581395, 'support': 54.0}, 'weighted avg': {'precision': 0.3511659807956104, 'recall': 0.5925925925925926, 'f1-score': 0.4409991386735573, 'support': 54.0}}
No log 8.0 40 0.5803 {'0': {'precision': 0.6, 'recall': 0.8181818181818182, 'f1-score': 0.6923076923076923, 'support': 22.0}, '1': {'precision': 0.8333333333333334, 'recall': 0.625, 'f1-score': 0.7142857142857143, 'support': 32.0}, 'accuracy': 0.7037037037037037, 'macro avg': {'precision': 0.7166666666666667, 'recall': 0.7215909090909092, 'f1-score': 0.7032967032967032, 'support': 54.0}, 'weighted avg': {'precision': 0.7382716049382716, 'recall': 0.7037037037037037, 'f1-score': 0.7053317053317053, 'support': 54.0}}
No log 9.0 45 0.5561 {'0': {'precision': 0.5714285714285714, 'recall': 0.9090909090909091, 'f1-score': 0.7017543859649122, 'support': 22.0}, '1': {'precision': 0.8947368421052632, 'recall': 0.53125, 'f1-score': 0.6666666666666666, 'support': 32.0}, 'accuracy': 0.6851851851851852, 'macro avg': {'precision': 0.7330827067669172, 'recall': 0.7201704545454546, 'f1-score': 0.6842105263157894, 'support': 54.0}, 'weighted avg': {'precision': 0.7630186577554999, 'recall': 0.6851851851851852, 'f1-score': 0.6809616634178036, 'support': 54.0}}
No log 10.0 50 0.5522 {'0': {'precision': 0.6923076923076923, 'recall': 0.4090909090909091, 'f1-score': 0.5142857142857142, 'support': 22.0}, '1': {'precision': 0.6829268292682927, 'recall': 0.875, 'f1-score': 0.7671232876712328, 'support': 32.0}, 'accuracy': 0.6851851851851852, 'macro avg': {'precision': 0.6876172607879925, 'recall': 0.6420454545454546, 'f1-score': 0.6407045009784735, 'support': 54.0}, 'weighted avg': {'precision': 0.6867486623584185, 'recall': 0.6851851851851852, 'f1-score': 0.6641153874030586, 'support': 54.0}}
No log 11.0 55 0.5738 {'0': {'precision': 0.5714285714285714, 'recall': 0.9090909090909091, 'f1-score': 0.7017543859649122, 'support': 22.0}, '1': {'precision': 0.8947368421052632, 'recall': 0.53125, 'f1-score': 0.6666666666666666, 'support': 32.0}, 'accuracy': 0.6851851851851852, 'macro avg': {'precision': 0.7330827067669172, 'recall': 0.7201704545454546, 'f1-score': 0.6842105263157894, 'support': 54.0}, 'weighted avg': {'precision': 0.7630186577554999, 'recall': 0.6851851851851852, 'f1-score': 0.6809616634178036, 'support': 54.0}}
No log 12.0 60 0.5587 {'0': {'precision': 0.6923076923076923, 'recall': 0.4090909090909091, 'f1-score': 0.5142857142857142, 'support': 22.0}, '1': {'precision': 0.6829268292682927, 'recall': 0.875, 'f1-score': 0.7671232876712328, 'support': 32.0}, 'accuracy': 0.6851851851851852, 'macro avg': {'precision': 0.6876172607879925, 'recall': 0.6420454545454546, 'f1-score': 0.6407045009784735, 'support': 54.0}, 'weighted avg': {'precision': 0.6867486623584185, 'recall': 0.6851851851851852, 'f1-score': 0.6641153874030586, 'support': 54.0}}
No log 13.0 65 0.5724 {'0': {'precision': 0.59375, 'recall': 0.8636363636363636, 'f1-score': 0.7037037037037037, 'support': 22.0}, '1': {'precision': 0.8636363636363636, 'recall': 0.59375, 'f1-score': 0.7037037037037037, 'support': 32.0}, 'accuracy': 0.7037037037037037, 'macro avg': {'precision': 0.7286931818181819, 'recall': 0.7286931818181819, 'f1-score': 0.7037037037037037, 'support': 54.0}, 'weighted avg': {'precision': 0.75368265993266, 'recall': 0.7037037037037037, 'f1-score': 0.7037037037037037, 'support': 54.0}}
No log 14.0 70 0.5276 {'0': {'precision': 0.7, 'recall': 0.6363636363636364, 'f1-score': 0.6666666666666666, 'support': 22.0}, '1': {'precision': 0.7647058823529411, 'recall': 0.8125, 'f1-score': 0.7878787878787878, 'support': 32.0}, 'accuracy': 0.7407407407407407, 'macro avg': {'precision': 0.7323529411764705, 'recall': 0.7244318181818181, 'f1-score': 0.7272727272727273, 'support': 54.0}, 'weighted avg': {'precision': 0.7383442265795206, 'recall': 0.7407407407407407, 'f1-score': 0.7384960718294051, 'support': 54.0}}
No log 15.0 75 0.7660 {'0': {'precision': 0.5121951219512195, 'recall': 0.9545454545454546, 'f1-score': 0.6666666666666666, 'support': 22.0}, '1': {'precision': 0.9230769230769231, 'recall': 0.375, 'f1-score': 0.5333333333333333, 'support': 32.0}, 'accuracy': 0.6111111111111112, 'macro avg': {'precision': 0.7176360225140713, 'recall': 0.6647727272727273, 'f1-score': 0.6, 'support': 54.0}, 'weighted avg': {'precision': 0.7556806337294143, 'recall': 0.6111111111111112, 'f1-score': 0.5876543209876544, 'support': 54.0}}
No log 16.0 80 0.5260 {'0': {'precision': 0.6666666666666666, 'recall': 0.5454545454545454, 'f1-score': 0.6, 'support': 22.0}, '1': {'precision': 0.7222222222222222, 'recall': 0.8125, 'f1-score': 0.7647058823529411, 'support': 32.0}, 'accuracy': 0.7037037037037037, 'macro avg': {'precision': 0.6944444444444444, 'recall': 0.6789772727272727, 'f1-score': 0.6823529411764706, 'support': 54.0}, 'weighted avg': {'precision': 0.6995884773662552, 'recall': 0.7037037037037037, 'f1-score': 0.69760348583878, 'support': 54.0}}
No log 17.0 85 0.5658 {'0': {'precision': 0.7142857142857143, 'recall': 0.45454545454545453, 'f1-score': 0.5555555555555556, 'support': 22.0}, '1': {'precision': 0.7, 'recall': 0.875, 'f1-score': 0.7777777777777778, 'support': 32.0}, 'accuracy': 0.7037037037037037, 'macro avg': {'precision': 0.7071428571428571, 'recall': 0.6647727272727273, 'f1-score': 0.6666666666666667, 'support': 54.0}, 'weighted avg': {'precision': 0.7058201058201058, 'recall': 0.7037037037037037, 'f1-score': 0.6872427983539096, 'support': 54.0}}
No log 18.0 90 0.5516 {'0': {'precision': 0.7058823529411765, 'recall': 0.5454545454545454, 'f1-score': 0.6153846153846154, 'support': 22.0}, '1': {'precision': 0.7297297297297297, 'recall': 0.84375, 'f1-score': 0.782608695652174, 'support': 32.0}, 'accuracy': 0.7222222222222222, 'macro avg': {'precision': 0.7178060413354531, 'recall': 0.6946022727272727, 'f1-score': 0.6989966555183946, 'support': 54.0}, 'weighted avg': {'precision': 0.7200141317788378, 'recall': 0.7222222222222222, 'f1-score': 0.7144803666542798, 'support': 54.0}}
No log 19.0 95 0.7988 {'0': {'precision': 0.5, 'recall': 0.9545454545454546, 'f1-score': 0.65625, 'support': 22.0}, '1': {'precision': 0.9166666666666666, 'recall': 0.34375, 'f1-score': 0.5, 'support': 32.0}, 'accuracy': 0.5925925925925926, 'macro avg': {'precision': 0.7083333333333333, 'recall': 0.6491477272727273, 'f1-score': 0.578125, 'support': 54.0}, 'weighted avg': {'precision': 0.7469135802469135, 'recall': 0.5925925925925926, 'f1-score': 0.5636574074074074, 'support': 54.0}}
No log 20.0 100 0.5415 {'0': {'precision': 0.7058823529411765, 'recall': 0.5454545454545454, 'f1-score': 0.6153846153846154, 'support': 22.0}, '1': {'precision': 0.7297297297297297, 'recall': 0.84375, 'f1-score': 0.782608695652174, 'support': 32.0}, 'accuracy': 0.7222222222222222, 'macro avg': {'precision': 0.7178060413354531, 'recall': 0.6946022727272727, 'f1-score': 0.6989966555183946, 'support': 54.0}, 'weighted avg': {'precision': 0.7200141317788378, 'recall': 0.7222222222222222, 'f1-score': 0.7144803666542798, 'support': 54.0}}
No log 21.0 105 0.9034 {'0': {'precision': 0.4883720930232558, 'recall': 0.9545454545454546, 'f1-score': 0.6461538461538462, 'support': 22.0}, '1': {'precision': 0.9090909090909091, 'recall': 0.3125, 'f1-score': 0.46511627906976744, 'support': 32.0}, 'accuracy': 0.5740740740740741, 'macro avg': {'precision': 0.6987315010570825, 'recall': 0.6335227272727273, 'f1-score': 0.5556350626118068, 'support': 54.0}, 'weighted avg': {'precision': 0.7376869469892725, 'recall': 0.5740740740740741, 'f1-score': 0.5388723249188365, 'support': 54.0}}
No log 22.0 110 0.5899 {'0': {'precision': 0.7058823529411765, 'recall': 0.5454545454545454, 'f1-score': 0.6153846153846154, 'support': 22.0}, '1': {'precision': 0.7297297297297297, 'recall': 0.84375, 'f1-score': 0.782608695652174, 'support': 32.0}, 'accuracy': 0.7222222222222222, 'macro avg': {'precision': 0.7178060413354531, 'recall': 0.6946022727272727, 'f1-score': 0.6989966555183946, 'support': 54.0}, 'weighted avg': {'precision': 0.7200141317788378, 'recall': 0.7222222222222222, 'f1-score': 0.7144803666542798, 'support': 54.0}}
No log 23.0 115 0.5948 {'0': {'precision': 0.9, 'recall': 0.4090909090909091, 'f1-score': 0.5625, 'support': 22.0}, '1': {'precision': 0.7045454545454546, 'recall': 0.96875, 'f1-score': 0.8157894736842105, 'support': 32.0}, 'accuracy': 0.7407407407407407, 'macro avg': {'precision': 0.8022727272727272, 'recall': 0.6889204545454546, 'f1-score': 0.6891447368421053, 'support': 54.0}, 'weighted avg': {'precision': 0.7841750841750842, 'recall': 0.7407407407407407, 'f1-score': 0.7125974658869396, 'support': 54.0}}
No log 24.0 120 1.0579 {'0': {'precision': 0.4772727272727273, 'recall': 0.9545454545454546, 'f1-score': 0.6363636363636364, 'support': 22.0}, '1': {'precision': 0.9, 'recall': 0.28125, 'f1-score': 0.42857142857142855, 'support': 32.0}, 'accuracy': 0.5555555555555556, 'macro avg': {'precision': 0.6886363636363637, 'recall': 0.6178977272727273, 'f1-score': 0.5324675324675324, 'support': 54.0}, 'weighted avg': {'precision': 0.7277777777777777, 'recall': 0.5555555555555556, 'f1-score': 0.5132275132275133, 'support': 54.0}}
No log 25.0 125 0.8061 {'0': {'precision': 1.0, 'recall': 0.045454545454545456, 'f1-score': 0.08695652173913043, 'support': 22.0}, '1': {'precision': 0.6037735849056604, 'recall': 1.0, 'f1-score': 0.7529411764705882, 'support': 32.0}, 'accuracy': 0.6111111111111112, 'macro avg': {'precision': 0.8018867924528301, 'recall': 0.5227272727272727, 'f1-score': 0.4199488491048593, 'support': 54.0}, 'weighted avg': {'precision': 0.7651991614255764, 'recall': 0.6111111111111112, 'f1-score': 0.4816140949133277, 'support': 54.0}}
No log 26.0 130 0.7016 {'0': {'precision': 0.5, 'recall': 0.9545454545454546, 'f1-score': 0.65625, 'support': 22.0}, '1': {'precision': 0.9166666666666666, 'recall': 0.34375, 'f1-score': 0.5, 'support': 32.0}, 'accuracy': 0.5925925925925926, 'macro avg': {'precision': 0.7083333333333333, 'recall': 0.6491477272727273, 'f1-score': 0.578125, 'support': 54.0}, 'weighted avg': {'precision': 0.7469135802469135, 'recall': 0.5925925925925926, 'f1-score': 0.5636574074074074, 'support': 54.0}}
No log 27.0 135 0.5236 {'0': {'precision': 0.7368421052631579, 'recall': 0.6363636363636364, 'f1-score': 0.6829268292682927, 'support': 22.0}, '1': {'precision': 0.7714285714285715, 'recall': 0.84375, 'f1-score': 0.8059701492537313, 'support': 32.0}, 'accuracy': 0.7592592592592593, 'macro avg': {'precision': 0.7541353383458647, 'recall': 0.7400568181818181, 'f1-score': 0.744448489261012, 'support': 54.0}, 'weighted avg': {'precision': 0.7573377889167363, 'recall': 0.7592592592592593, 'f1-score': 0.7558413892596638, 'support': 54.0}}
No log 28.0 140 0.5446 {'0': {'precision': 0.8461538461538461, 'recall': 0.5, 'f1-score': 0.6285714285714286, 'support': 22.0}, '1': {'precision': 0.7317073170731707, 'recall': 0.9375, 'f1-score': 0.821917808219178, 'support': 32.0}, 'accuracy': 0.7592592592592593, 'macro avg': {'precision': 0.7889305816135084, 'recall': 0.71875, 'f1-score': 0.7252446183953033, 'support': 54.0}, 'weighted avg': {'precision': 0.7783336807727053, 'recall': 0.7592592592592593, 'f1-score': 0.7431470609552802, 'support': 54.0}}
No log 29.0 145 0.8297 {'0': {'precision': 0.5, 'recall': 0.9545454545454546, 'f1-score': 0.65625, 'support': 22.0}, '1': {'precision': 0.9166666666666666, 'recall': 0.34375, 'f1-score': 0.5, 'support': 32.0}, 'accuracy': 0.5925925925925926, 'macro avg': {'precision': 0.7083333333333333, 'recall': 0.6491477272727273, 'f1-score': 0.578125, 'support': 54.0}, 'weighted avg': {'precision': 0.7469135802469135, 'recall': 0.5925925925925926, 'f1-score': 0.5636574074074074, 'support': 54.0}}
No log 30.0 150 0.5771 {'0': {'precision': 0.9, 'recall': 0.4090909090909091, 'f1-score': 0.5625, 'support': 22.0}, '1': {'precision': 0.7045454545454546, 'recall': 0.96875, 'f1-score': 0.8157894736842105, 'support': 32.0}, 'accuracy': 0.7407407407407407, 'macro avg': {'precision': 0.8022727272727272, 'recall': 0.6889204545454546, 'f1-score': 0.6891447368421053, 'support': 54.0}, 'weighted avg': {'precision': 0.7841750841750842, 'recall': 0.7407407407407407, 'f1-score': 0.7125974658869396, 'support': 54.0}}
No log 31.0 155 0.6400 {'0': {'precision': 0.5428571428571428, 'recall': 0.8636363636363636, 'f1-score': 0.6666666666666666, 'support': 22.0}, '1': {'precision': 0.8421052631578947, 'recall': 0.5, 'f1-score': 0.6274509803921569, 'support': 32.0}, 'accuracy': 0.6481481481481481, 'macro avg': {'precision': 0.6924812030075187, 'recall': 0.6818181818181819, 'f1-score': 0.6470588235294117, 'support': 54.0}, 'weighted avg': {'precision': 0.7201893622946254, 'recall': 0.6481481481481481, 'f1-score': 0.6434277414669571, 'support': 54.0}}
No log 32.0 160 0.5397 {'0': {'precision': 0.6296296296296297, 'recall': 0.7727272727272727, 'f1-score': 0.6938775510204082, 'support': 22.0}, '1': {'precision': 0.8148148148148148, 'recall': 0.6875, 'f1-score': 0.7457627118644068, 'support': 32.0}, 'accuracy': 0.7222222222222222, 'macro avg': {'precision': 0.7222222222222222, 'recall': 0.7301136363636364, 'f1-score': 0.7198201314424075, 'support': 54.0}, 'weighted avg': {'precision': 0.7393689986282579, 'recall': 0.7222222222222222, 'f1-score': 0.724624313002037, 'support': 54.0}}
No log 33.0 165 0.5095 {'0': {'precision': 0.64, 'recall': 0.7272727272727273, 'f1-score': 0.6808510638297872, 'support': 22.0}, '1': {'precision': 0.7931034482758621, 'recall': 0.71875, 'f1-score': 0.7540983606557377, 'support': 32.0}, 'accuracy': 0.7222222222222222, 'macro avg': {'precision': 0.7165517241379311, 'recall': 0.7230113636363636, 'f1-score': 0.7174747122427625, 'support': 54.0}, 'weighted avg': {'precision': 0.730727969348659, 'recall': 0.7222222222222222, 'f1-score': 0.7242568693562764, 'support': 54.0}}
No log 34.0 170 0.5339 {'0': {'precision': 0.7692307692307693, 'recall': 0.45454545454545453, 'f1-score': 0.5714285714285714, 'support': 22.0}, '1': {'precision': 0.7073170731707317, 'recall': 0.90625, 'f1-score': 0.7945205479452054, 'support': 32.0}, 'accuracy': 0.7222222222222222, 'macro avg': {'precision': 0.7382739212007505, 'recall': 0.6803977272727273, 'f1-score': 0.6829745596868884, 'support': 54.0}, 'weighted avg': {'precision': 0.7325411715655619, 'recall': 0.7222222222222222, 'f1-score': 0.7036312241791693, 'support': 54.0}}
No log 35.0 175 0.5481 {'0': {'precision': 0.7692307692307693, 'recall': 0.45454545454545453, 'f1-score': 0.5714285714285714, 'support': 22.0}, '1': {'precision': 0.7073170731707317, 'recall': 0.90625, 'f1-score': 0.7945205479452054, 'support': 32.0}, 'accuracy': 0.7222222222222222, 'macro avg': {'precision': 0.7382739212007505, 'recall': 0.6803977272727273, 'f1-score': 0.6829745596868884, 'support': 54.0}, 'weighted avg': {'precision': 0.7325411715655619, 'recall': 0.7222222222222222, 'f1-score': 0.7036312241791693, 'support': 54.0}}
No log 36.0 180 0.7269 {'0': {'precision': 0.5277777777777778, 'recall': 0.8636363636363636, 'f1-score': 0.6551724137931034, 'support': 22.0}, '1': {'precision': 0.8333333333333334, 'recall': 0.46875, 'f1-score': 0.6, 'support': 32.0}, 'accuracy': 0.6296296296296297, 'macro avg': {'precision': 0.6805555555555556, 'recall': 0.6661931818181819, 'f1-score': 0.6275862068965516, 'support': 54.0}, 'weighted avg': {'precision': 0.7088477366255144, 'recall': 0.6296296296296297, 'f1-score': 0.6224776500638569, 'support': 54.0}}
No log 37.0 185 0.7850 {'0': {'precision': 0.8461538461538461, 'recall': 0.5, 'f1-score': 0.6285714285714286, 'support': 22.0}, '1': {'precision': 0.7317073170731707, 'recall': 0.9375, 'f1-score': 0.821917808219178, 'support': 32.0}, 'accuracy': 0.7592592592592593, 'macro avg': {'precision': 0.7889305816135084, 'recall': 0.71875, 'f1-score': 0.7252446183953033, 'support': 54.0}, 'weighted avg': {'precision': 0.7783336807727053, 'recall': 0.7592592592592593, 'f1-score': 0.7431470609552802, 'support': 54.0}}
No log 38.0 190 0.8831 {'0': {'precision': 0.5135135135135135, 'recall': 0.8636363636363636, 'f1-score': 0.6440677966101694, 'support': 22.0}, '1': {'precision': 0.8235294117647058, 'recall': 0.4375, 'f1-score': 0.5714285714285714, 'support': 32.0}, 'accuracy': 0.6111111111111112, 'macro avg': {'precision': 0.6685214626391096, 'recall': 0.6505681818181819, 'f1-score': 0.6077481840193704, 'support': 54.0}, 'weighted avg': {'precision': 0.697226638403109, 'recall': 0.6111111111111112, 'f1-score': 0.6010223298358892, 'support': 54.0}}
No log 39.0 195 0.8050 {'0': {'precision': 0.5454545454545454, 'recall': 0.8181818181818182, 'f1-score': 0.6545454545454545, 'support': 22.0}, '1': {'precision': 0.8095238095238095, 'recall': 0.53125, 'f1-score': 0.6415094339622641, 'support': 32.0}, 'accuracy': 0.6481481481481481, 'macro avg': {'precision': 0.6774891774891775, 'recall': 0.6747159090909092, 'f1-score': 0.6480274442538594, 'support': 54.0}, 'weighted avg': {'precision': 0.7019400352733686, 'recall': 0.6481481481481481, 'f1-score': 0.6468204053109714, 'support': 54.0}}
No log 40.0 200 0.8223 {'0': {'precision': 0.64, 'recall': 0.7272727272727273, 'f1-score': 0.6808510638297872, 'support': 22.0}, '1': {'precision': 0.7931034482758621, 'recall': 0.71875, 'f1-score': 0.7540983606557377, 'support': 32.0}, 'accuracy': 0.7222222222222222, 'macro avg': {'precision': 0.7165517241379311, 'recall': 0.7230113636363636, 'f1-score': 0.7174747122427625, 'support': 54.0}, 'weighted avg': {'precision': 0.730727969348659, 'recall': 0.7222222222222222, 'f1-score': 0.7242568693562764, 'support': 54.0}}
No log 41.0 205 0.8443 {'0': {'precision': 0.625, 'recall': 0.6818181818181818, 'f1-score': 0.6521739130434783, 'support': 22.0}, '1': {'precision': 0.7666666666666667, 'recall': 0.71875, 'f1-score': 0.7419354838709677, 'support': 32.0}, 'accuracy': 0.7037037037037037, 'macro avg': {'precision': 0.6958333333333333, 'recall': 0.7002840909090908, 'f1-score': 0.697054698457223, 'support': 54.0}, 'weighted avg': {'precision': 0.7089506172839506, 'recall': 0.7037037037037037, 'f1-score': 0.705365955015324, 'support': 54.0}}
No log 42.0 210 0.8875 {'0': {'precision': 0.8, 'recall': 0.5454545454545454, 'f1-score': 0.6486486486486487, 'support': 22.0}, '1': {'precision': 0.7435897435897436, 'recall': 0.90625, 'f1-score': 0.8169014084507042, 'support': 32.0}, 'accuracy': 0.7592592592592593, 'macro avg': {'precision': 0.7717948717948718, 'recall': 0.7258522727272727, 'f1-score': 0.7327750285496765, 'support': 54.0}, 'weighted avg': {'precision': 0.7665716999050333, 'recall': 0.7592592592592593, 'f1-score': 0.7483539877906075, 'support': 54.0}}
No log 43.0 215 0.9117 {'0': {'precision': 0.6842105263157895, 'recall': 0.5909090909090909, 'f1-score': 0.6341463414634146, 'support': 22.0}, '1': {'precision': 0.7428571428571429, 'recall': 0.8125, 'f1-score': 0.7761194029850746, 'support': 32.0}, 'accuracy': 0.7222222222222222, 'macro avg': {'precision': 0.7135338345864661, 'recall': 0.7017045454545454, 'f1-score': 0.7051328722242447, 'support': 54.0}, 'weighted avg': {'precision': 0.7189640768588137, 'recall': 0.7222222222222222, 'f1-score': 0.7182785260688428, 'support': 54.0}}
No log 44.0 220 0.9242 {'0': {'precision': 0.625, 'recall': 0.6818181818181818, 'f1-score': 0.6521739130434783, 'support': 22.0}, '1': {'precision': 0.7666666666666667, 'recall': 0.71875, 'f1-score': 0.7419354838709677, 'support': 32.0}, 'accuracy': 0.7037037037037037, 'macro avg': {'precision': 0.6958333333333333, 'recall': 0.7002840909090908, 'f1-score': 0.697054698457223, 'support': 54.0}, 'weighted avg': {'precision': 0.7089506172839506, 'recall': 0.7037037037037037, 'f1-score': 0.705365955015324, 'support': 54.0}}
No log 45.0 225 1.1645 {'0': {'precision': 0.7142857142857143, 'recall': 0.45454545454545453, 'f1-score': 0.5555555555555556, 'support': 22.0}, '1': {'precision': 0.7, 'recall': 0.875, 'f1-score': 0.7777777777777778, 'support': 32.0}, 'accuracy': 0.7037037037037037, 'macro avg': {'precision': 0.7071428571428571, 'recall': 0.6647727272727273, 'f1-score': 0.6666666666666667, 'support': 54.0}, 'weighted avg': {'precision': 0.7058201058201058, 'recall': 0.7037037037037037, 'f1-score': 0.6872427983539096, 'support': 54.0}}
No log 46.0 230 0.9202 {'0': {'precision': 0.6666666666666666, 'recall': 0.6363636363636364, 'f1-score': 0.6511627906976745, 'support': 22.0}, '1': {'precision': 0.7575757575757576, 'recall': 0.78125, 'f1-score': 0.7692307692307693, 'support': 32.0}, 'accuracy': 0.7222222222222222, 'macro avg': {'precision': 0.7121212121212122, 'recall': 0.7088068181818181, 'f1-score': 0.7101967799642219, 'support': 54.0}, 'weighted avg': {'precision': 0.7205387205387205, 'recall': 0.7222222222222222, 'f1-score': 0.7211290001987677, 'support': 54.0}}
No log 47.0 235 0.9492 {'0': {'precision': 0.5882352941176471, 'recall': 0.9090909090909091, 'f1-score': 0.7142857142857143, 'support': 22.0}, '1': {'precision': 0.9, 'recall': 0.5625, 'f1-score': 0.6923076923076923, 'support': 32.0}, 'accuracy': 0.7037037037037037, 'macro avg': {'precision': 0.7441176470588236, 'recall': 0.7357954545454546, 'f1-score': 0.7032967032967032, 'support': 54.0}, 'weighted avg': {'precision': 0.7729847494553377, 'recall': 0.7037037037037037, 'f1-score': 0.7012617012617013, 'support': 54.0}}
No log 48.0 240 0.8809 {'0': {'precision': 0.75, 'recall': 0.4090909090909091, 'f1-score': 0.5294117647058824, 'support': 22.0}, '1': {'precision': 0.6904761904761905, 'recall': 0.90625, 'f1-score': 0.7837837837837838, 'support': 32.0}, 'accuracy': 0.7037037037037037, 'macro avg': {'precision': 0.7202380952380952, 'recall': 0.6576704545454546, 'f1-score': 0.656597774244833, 'support': 54.0}, 'weighted avg': {'precision': 0.7147266313932981, 'recall': 0.7037037037037037, 'f1-score': 0.6801507389742684, 'support': 54.0}}
No log 49.0 245 0.8856 {'0': {'precision': 0.5714285714285714, 'recall': 0.9090909090909091, 'f1-score': 0.7017543859649122, 'support': 22.0}, '1': {'precision': 0.8947368421052632, 'recall': 0.53125, 'f1-score': 0.6666666666666666, 'support': 32.0}, 'accuracy': 0.6851851851851852, 'macro avg': {'precision': 0.7330827067669172, 'recall': 0.7201704545454546, 'f1-score': 0.6842105263157894, 'support': 54.0}, 'weighted avg': {'precision': 0.7630186577554999, 'recall': 0.6851851851851852, 'f1-score': 0.6809616634178036, 'support': 54.0}}
No log 50.0 250 0.7144 {'0': {'precision': 0.6818181818181818, 'recall': 0.6818181818181818, 'f1-score': 0.6818181818181818, 'support': 22.0}, '1': {'precision': 0.78125, 'recall': 0.78125, 'f1-score': 0.78125, 'support': 32.0}, 'accuracy': 0.7407407407407407, 'macro avg': {'precision': 0.7315340909090908, 'recall': 0.7315340909090908, 'f1-score': 0.7315340909090908, 'support': 54.0}, 'weighted avg': {'precision': 0.7407407407407407, 'recall': 0.7407407407407407, 'f1-score': 0.7407407407407407, 'support': 54.0}}
No log 51.0 255 0.7674 {'0': {'precision': 0.6296296296296297, 'recall': 0.7727272727272727, 'f1-score': 0.6938775510204082, 'support': 22.0}, '1': {'precision': 0.8148148148148148, 'recall': 0.6875, 'f1-score': 0.7457627118644068, 'support': 32.0}, 'accuracy': 0.7222222222222222, 'macro avg': {'precision': 0.7222222222222222, 'recall': 0.7301136363636364, 'f1-score': 0.7198201314424075, 'support': 54.0}, 'weighted avg': {'precision': 0.7393689986282579, 'recall': 0.7222222222222222, 'f1-score': 0.724624313002037, 'support': 54.0}}
No log 52.0 260 0.7872 {'0': {'precision': 0.6666666666666666, 'recall': 0.8181818181818182, 'f1-score': 0.7346938775510204, 'support': 22.0}, '1': {'precision': 0.8518518518518519, 'recall': 0.71875, 'f1-score': 0.7796610169491526, 'support': 32.0}, 'accuracy': 0.7592592592592593, 'macro avg': {'precision': 0.7592592592592593, 'recall': 0.7684659090909092, 'f1-score': 0.7571774472500865, 'support': 54.0}, 'weighted avg': {'precision': 0.7764060356652949, 'recall': 0.7592592592592593, 'f1-score': 0.7613410712684321, 'support': 54.0}}
No log 53.0 265 0.7531 {'0': {'precision': 0.7, 'recall': 0.6363636363636364, 'f1-score': 0.6666666666666666, 'support': 22.0}, '1': {'precision': 0.7647058823529411, 'recall': 0.8125, 'f1-score': 0.7878787878787878, 'support': 32.0}, 'accuracy': 0.7407407407407407, 'macro avg': {'precision': 0.7323529411764705, 'recall': 0.7244318181818181, 'f1-score': 0.7272727272727273, 'support': 54.0}, 'weighted avg': {'precision': 0.7383442265795206, 'recall': 0.7407407407407407, 'f1-score': 0.7384960718294051, 'support': 54.0}}
No log 54.0 270 0.9944 {'0': {'precision': 0.5882352941176471, 'recall': 0.9090909090909091, 'f1-score': 0.7142857142857143, 'support': 22.0}, '1': {'precision': 0.9, 'recall': 0.5625, 'f1-score': 0.6923076923076923, 'support': 32.0}, 'accuracy': 0.7037037037037037, 'macro avg': {'precision': 0.7441176470588236, 'recall': 0.7357954545454546, 'f1-score': 0.7032967032967032, 'support': 54.0}, 'weighted avg': {'precision': 0.7729847494553377, 'recall': 0.7037037037037037, 'f1-score': 0.7012617012617013, 'support': 54.0}}
No log 55.0 275 0.8734 {'0': {'precision': 0.6206896551724138, 'recall': 0.8181818181818182, 'f1-score': 0.7058823529411765, 'support': 22.0}, '1': {'precision': 0.84, 'recall': 0.65625, 'f1-score': 0.7368421052631579, 'support': 32.0}, 'accuracy': 0.7222222222222222, 'macro avg': {'precision': 0.7303448275862069, 'recall': 0.7372159090909092, 'f1-score': 0.7213622291021672, 'support': 54.0}, 'weighted avg': {'precision': 0.7506513409961686, 'recall': 0.7222222222222222, 'f1-score': 0.724228872835684, 'support': 54.0}}
No log 56.0 280 1.0454 {'0': {'precision': 0.6333333333333333, 'recall': 0.8636363636363636, 'f1-score': 0.7307692307692307, 'support': 22.0}, '1': {'precision': 0.875, 'recall': 0.65625, 'f1-score': 0.75, 'support': 32.0}, 'accuracy': 0.7407407407407407, 'macro avg': {'precision': 0.7541666666666667, 'recall': 0.7599431818181819, 'f1-score': 0.7403846153846154, 'support': 54.0}, 'weighted avg': {'precision': 0.7765432098765432, 'recall': 0.7407407407407407, 'f1-score': 0.7421652421652423, 'support': 54.0}}
No log 57.0 285 0.9684 {'0': {'precision': 0.6538461538461539, 'recall': 0.7727272727272727, 'f1-score': 0.7083333333333334, 'support': 22.0}, '1': {'precision': 0.8214285714285714, 'recall': 0.71875, 'f1-score': 0.7666666666666667, 'support': 32.0}, 'accuracy': 0.7407407407407407, 'macro avg': {'precision': 0.7376373626373627, 'recall': 0.7457386363636364, 'f1-score': 0.7375, 'support': 54.0}, 'weighted avg': {'precision': 0.7531542531542532, 'recall': 0.7407407407407407, 'f1-score': 0.7429012345679012, 'support': 54.0}}
No log 58.0 290 2.4239 {'0': {'precision': 0.5116279069767442, 'recall': 1.0, 'f1-score': 0.676923076923077, 'support': 22.0}, '1': {'precision': 1.0, 'recall': 0.34375, 'f1-score': 0.5116279069767442, 'support': 32.0}, 'accuracy': 0.6111111111111112, 'macro avg': {'precision': 0.7558139534883721, 'recall': 0.671875, 'f1-score': 0.5942754919499106, 'support': 54.0}, 'weighted avg': {'precision': 0.8010335917312661, 'recall': 0.6111111111111112, 'f1-score': 0.5789703836215464, 'support': 54.0}}
No log 59.0 295 1.1134 {'0': {'precision': 0.6666666666666666, 'recall': 0.36363636363636365, 'f1-score': 0.47058823529411764, 'support': 22.0}, '1': {'precision': 0.6666666666666666, 'recall': 0.875, 'f1-score': 0.7567567567567568, 'support': 32.0}, 'accuracy': 0.6666666666666666, 'macro avg': {'precision': 0.6666666666666666, 'recall': 0.6193181818181819, 'f1-score': 0.6136724960254372, 'support': 54.0}, 'weighted avg': {'precision': 0.6666666666666666, 'recall': 0.6666666666666666, 'f1-score': 0.640169581346052, 'support': 54.0}}
No log 60.0 300 1.9298 {'0': {'precision': 0.4883720930232558, 'recall': 0.9545454545454546, 'f1-score': 0.6461538461538462, 'support': 22.0}, '1': {'precision': 0.9090909090909091, 'recall': 0.3125, 'f1-score': 0.46511627906976744, 'support': 32.0}, 'accuracy': 0.5740740740740741, 'macro avg': {'precision': 0.6987315010570825, 'recall': 0.6335227272727273, 'f1-score': 0.5556350626118068, 'support': 54.0}, 'weighted avg': {'precision': 0.7376869469892725, 'recall': 0.5740740740740741, 'f1-score': 0.5388723249188365, 'support': 54.0}}
No log 61.0 305 0.9612 {'0': {'precision': 0.7222222222222222, 'recall': 0.5909090909090909, 'f1-score': 0.65, 'support': 22.0}, '1': {'precision': 0.75, 'recall': 0.84375, 'f1-score': 0.7941176470588235, 'support': 32.0}, 'accuracy': 0.7407407407407407, 'macro avg': {'precision': 0.7361111111111112, 'recall': 0.7173295454545454, 'f1-score': 0.7220588235294118, 'support': 54.0}, 'weighted avg': {'precision': 0.7386831275720164, 'recall': 0.7407407407407407, 'f1-score': 0.7354030501089325, 'support': 54.0}}
No log 62.0 310 0.9672 {'0': {'precision': 0.625, 'recall': 0.6818181818181818, 'f1-score': 0.6521739130434783, 'support': 22.0}, '1': {'precision': 0.7666666666666667, 'recall': 0.71875, 'f1-score': 0.7419354838709677, 'support': 32.0}, 'accuracy': 0.7037037037037037, 'macro avg': {'precision': 0.6958333333333333, 'recall': 0.7002840909090908, 'f1-score': 0.697054698457223, 'support': 54.0}, 'weighted avg': {'precision': 0.7089506172839506, 'recall': 0.7037037037037037, 'f1-score': 0.705365955015324, 'support': 54.0}}
No log 63.0 315 1.0044 {'0': {'precision': 0.6666666666666666, 'recall': 0.7272727272727273, 'f1-score': 0.6956521739130435, 'support': 22.0}, '1': {'precision': 0.8, 'recall': 0.75, 'f1-score': 0.7741935483870968, 'support': 32.0}, 'accuracy': 0.7407407407407407, 'macro avg': {'precision': 0.7333333333333334, 'recall': 0.7386363636363636, 'f1-score': 0.7349228611500701, 'support': 54.0}, 'weighted avg': {'precision': 0.745679012345679, 'recall': 0.7407407407407407, 'f1-score': 0.7421952106384084, 'support': 54.0}}
No log 64.0 320 1.7548 {'0': {'precision': 0.5714285714285714, 'recall': 0.9090909090909091, 'f1-score': 0.7017543859649122, 'support': 22.0}, '1': {'precision': 0.8947368421052632, 'recall': 0.53125, 'f1-score': 0.6666666666666666, 'support': 32.0}, 'accuracy': 0.6851851851851852, 'macro avg': {'precision': 0.7330827067669172, 'recall': 0.7201704545454546, 'f1-score': 0.6842105263157894, 'support': 54.0}, 'weighted avg': {'precision': 0.7630186577554999, 'recall': 0.6851851851851852, 'f1-score': 0.6809616634178036, 'support': 54.0}}
No log 65.0 325 1.2609 {'0': {'precision': 0.6666666666666666, 'recall': 0.9090909090909091, 'f1-score': 0.7692307692307693, 'support': 22.0}, '1': {'precision': 0.9166666666666666, 'recall': 0.6875, 'f1-score': 0.7857142857142857, 'support': 32.0}, 'accuracy': 0.7777777777777778, 'macro avg': {'precision': 0.7916666666666666, 'recall': 0.7982954545454546, 'f1-score': 0.7774725274725275, 'support': 54.0}, 'weighted avg': {'precision': 0.8148148148148148, 'recall': 0.7777777777777778, 'f1-score': 0.778998778998779, 'support': 54.0}}
No log 66.0 330 1.2265 {'0': {'precision': 0.6666666666666666, 'recall': 0.7272727272727273, 'f1-score': 0.6956521739130435, 'support': 22.0}, '1': {'precision': 0.8, 'recall': 0.75, 'f1-score': 0.7741935483870968, 'support': 32.0}, 'accuracy': 0.7407407407407407, 'macro avg': {'precision': 0.7333333333333334, 'recall': 0.7386363636363636, 'f1-score': 0.7349228611500701, 'support': 54.0}, 'weighted avg': {'precision': 0.745679012345679, 'recall': 0.7407407407407407, 'f1-score': 0.7421952106384084, 'support': 54.0}}
No log 67.0 335 1.5731 {'0': {'precision': 0.6666666666666666, 'recall': 0.9090909090909091, 'f1-score': 0.7692307692307693, 'support': 22.0}, '1': {'precision': 0.9166666666666666, 'recall': 0.6875, 'f1-score': 0.7857142857142857, 'support': 32.0}, 'accuracy': 0.7777777777777778, 'macro avg': {'precision': 0.7916666666666666, 'recall': 0.7982954545454546, 'f1-score': 0.7774725274725275, 'support': 54.0}, 'weighted avg': {'precision': 0.8148148148148148, 'recall': 0.7777777777777778, 'f1-score': 0.778998778998779, 'support': 54.0}}
No log 68.0 340 1.9839 {'0': {'precision': 0.625, 'recall': 0.9090909090909091, 'f1-score': 0.7407407407407407, 'support': 22.0}, '1': {'precision': 0.9090909090909091, 'recall': 0.625, 'f1-score': 0.7407407407407407, 'support': 32.0}, 'accuracy': 0.7407407407407407, 'macro avg': {'precision': 0.7670454545454546, 'recall': 0.7670454545454546, 'f1-score': 0.7407407407407407, 'support': 54.0}, 'weighted avg': {'precision': 0.7933501683501684, 'recall': 0.7407407407407407, 'f1-score': 0.7407407407407407, 'support': 54.0}}
No log 69.0 345 1.9818 {'0': {'precision': 0.6451612903225806, 'recall': 0.9090909090909091, 'f1-score': 0.7547169811320755, 'support': 22.0}, '1': {'precision': 0.9130434782608695, 'recall': 0.65625, 'f1-score': 0.7636363636363637, 'support': 32.0}, 'accuracy': 0.7592592592592593, 'macro avg': {'precision': 0.7791023842917251, 'recall': 0.7826704545454546, 'f1-score': 0.7591766723842196, 'support': 54.0}, 'weighted avg': {'precision': 0.8039062905823073, 'recall': 0.7592592592592593, 'f1-score': 0.7600025411346166, 'support': 54.0}}
No log 70.0 350 1.8547 {'0': {'precision': 0.6551724137931034, 'recall': 0.8636363636363636, 'f1-score': 0.7450980392156863, 'support': 22.0}, '1': {'precision': 0.88, 'recall': 0.6875, 'f1-score': 0.7719298245614035, 'support': 32.0}, 'accuracy': 0.7592592592592593, 'macro avg': {'precision': 0.7675862068965518, 'recall': 0.7755681818181819, 'f1-score': 0.7585139318885449, 'support': 54.0}, 'weighted avg': {'precision': 0.7884035759897828, 'recall': 0.7592592592592593, 'f1-score': 0.7609983564575927, 'support': 54.0}}
No log 71.0 355 1.7769 {'0': {'precision': 0.6551724137931034, 'recall': 0.8636363636363636, 'f1-score': 0.7450980392156863, 'support': 22.0}, '1': {'precision': 0.88, 'recall': 0.6875, 'f1-score': 0.7719298245614035, 'support': 32.0}, 'accuracy': 0.7592592592592593, 'macro avg': {'precision': 0.7675862068965518, 'recall': 0.7755681818181819, 'f1-score': 0.7585139318885449, 'support': 54.0}, 'weighted avg': {'precision': 0.7884035759897828, 'recall': 0.7592592592592593, 'f1-score': 0.7609983564575927, 'support': 54.0}}
No log 72.0 360 2.1536 {'0': {'precision': 0.6060606060606061, 'recall': 0.9090909090909091, 'f1-score': 0.7272727272727273, 'support': 22.0}, '1': {'precision': 0.9047619047619048, 'recall': 0.59375, 'f1-score': 0.7169811320754716, 'support': 32.0}, 'accuracy': 0.7222222222222222, 'macro avg': {'precision': 0.7554112554112554, 'recall': 0.7514204545454546, 'f1-score': 0.7221269296740995, 'support': 54.0}, 'weighted avg': {'precision': 0.783068783068783, 'recall': 0.7222222222222222, 'f1-score': 0.721174004192872, 'support': 54.0}}
No log 73.0 365 1.7173 {'0': {'precision': 0.6785714285714286, 'recall': 0.8636363636363636, 'f1-score': 0.76, 'support': 22.0}, '1': {'precision': 0.8846153846153846, 'recall': 0.71875, 'f1-score': 0.7931034482758621, 'support': 32.0}, 'accuracy': 0.7777777777777778, 'macro avg': {'precision': 0.7815934065934066, 'recall': 0.7911931818181819, 'f1-score': 0.776551724137931, 'support': 54.0}, 'weighted avg': {'precision': 0.8006715506715507, 'recall': 0.7777777777777778, 'f1-score': 0.7796168582375479, 'support': 54.0}}
No log 74.0 370 2.0864 {'0': {'precision': 0.6333333333333333, 'recall': 0.8636363636363636, 'f1-score': 0.7307692307692307, 'support': 22.0}, '1': {'precision': 0.875, 'recall': 0.65625, 'f1-score': 0.75, 'support': 32.0}, 'accuracy': 0.7407407407407407, 'macro avg': {'precision': 0.7541666666666667, 'recall': 0.7599431818181819, 'f1-score': 0.7403846153846154, 'support': 54.0}, 'weighted avg': {'precision': 0.7765432098765432, 'recall': 0.7407407407407407, 'f1-score': 0.7421652421652423, 'support': 54.0}}
No log 75.0 375 2.0198 {'0': {'precision': 0.6333333333333333, 'recall': 0.8636363636363636, 'f1-score': 0.7307692307692307, 'support': 22.0}, '1': {'precision': 0.875, 'recall': 0.65625, 'f1-score': 0.75, 'support': 32.0}, 'accuracy': 0.7407407407407407, 'macro avg': {'precision': 0.7541666666666667, 'recall': 0.7599431818181819, 'f1-score': 0.7403846153846154, 'support': 54.0}, 'weighted avg': {'precision': 0.7765432098765432, 'recall': 0.7407407407407407, 'f1-score': 0.7421652421652423, 'support': 54.0}}
No log 76.0 380 2.0463 {'0': {'precision': 0.6129032258064516, 'recall': 0.8636363636363636, 'f1-score': 0.7169811320754716, 'support': 22.0}, '1': {'precision': 0.8695652173913043, 'recall': 0.625, 'f1-score': 0.7272727272727273, 'support': 32.0}, 'accuracy': 0.7222222222222222, 'macro avg': {'precision': 0.741234221598878, 'recall': 0.7443181818181819, 'f1-score': 0.7221269296740995, 'support': 54.0}, 'weighted avg': {'precision': 0.7649992208196976, 'recall': 0.7222222222222222, 'f1-score': 0.7230798551553269, 'support': 54.0}}
No log 77.0 385 2.6211 {'0': {'precision': 0.6060606060606061, 'recall': 0.9090909090909091, 'f1-score': 0.7272727272727273, 'support': 22.0}, '1': {'precision': 0.9047619047619048, 'recall': 0.59375, 'f1-score': 0.7169811320754716, 'support': 32.0}, 'accuracy': 0.7222222222222222, 'macro avg': {'precision': 0.7554112554112554, 'recall': 0.7514204545454546, 'f1-score': 0.7221269296740995, 'support': 54.0}, 'weighted avg': {'precision': 0.783068783068783, 'recall': 0.7222222222222222, 'f1-score': 0.721174004192872, 'support': 54.0}}
No log 78.0 390 2.1733 {'0': {'precision': 0.625, 'recall': 0.9090909090909091, 'f1-score': 0.7407407407407407, 'support': 22.0}, '1': {'precision': 0.9090909090909091, 'recall': 0.625, 'f1-score': 0.7407407407407407, 'support': 32.0}, 'accuracy': 0.7407407407407407, 'macro avg': {'precision': 0.7670454545454546, 'recall': 0.7670454545454546, 'f1-score': 0.7407407407407407, 'support': 54.0}, 'weighted avg': {'precision': 0.7933501683501684, 'recall': 0.7407407407407407, 'f1-score': 0.7407407407407407, 'support': 54.0}}
No log 79.0 395 1.7658 {'0': {'precision': 0.6428571428571429, 'recall': 0.8181818181818182, 'f1-score': 0.72, 'support': 22.0}, '1': {'precision': 0.8461538461538461, 'recall': 0.6875, 'f1-score': 0.7586206896551724, 'support': 32.0}, 'accuracy': 0.7407407407407407, 'macro avg': {'precision': 0.7445054945054945, 'recall': 0.7528409090909092, 'f1-score': 0.7393103448275862, 'support': 54.0}, 'weighted avg': {'precision': 0.7633292633292633, 'recall': 0.7407407407407407, 'f1-score': 0.7428863346104725, 'support': 54.0}}
No log 80.0 400 1.6756 {'0': {'precision': 0.6923076923076923, 'recall': 0.8181818181818182, 'f1-score': 0.75, 'support': 22.0}, '1': {'precision': 0.8571428571428571, 'recall': 0.75, 'f1-score': 0.8, 'support': 32.0}, 'accuracy': 0.7777777777777778, 'macro avg': {'precision': 0.7747252747252746, 'recall': 0.7840909090909092, 'f1-score': 0.775, 'support': 54.0}, 'weighted avg': {'precision': 0.78998778998779, 'recall': 0.7777777777777778, 'f1-score': 0.7796296296296297, 'support': 54.0}}
No log 81.0 405 1.6864 {'0': {'precision': 0.6785714285714286, 'recall': 0.8636363636363636, 'f1-score': 0.76, 'support': 22.0}, '1': {'precision': 0.8846153846153846, 'recall': 0.71875, 'f1-score': 0.7931034482758621, 'support': 32.0}, 'accuracy': 0.7777777777777778, 'macro avg': {'precision': 0.7815934065934066, 'recall': 0.7911931818181819, 'f1-score': 0.776551724137931, 'support': 54.0}, 'weighted avg': {'precision': 0.8006715506715507, 'recall': 0.7777777777777778, 'f1-score': 0.7796168582375479, 'support': 54.0}}
No log 82.0 410 1.7794 {'0': {'precision': 0.6785714285714286, 'recall': 0.8636363636363636, 'f1-score': 0.76, 'support': 22.0}, '1': {'precision': 0.8846153846153846, 'recall': 0.71875, 'f1-score': 0.7931034482758621, 'support': 32.0}, 'accuracy': 0.7777777777777778, 'macro avg': {'precision': 0.7815934065934066, 'recall': 0.7911931818181819, 'f1-score': 0.776551724137931, 'support': 54.0}, 'weighted avg': {'precision': 0.8006715506715507, 'recall': 0.7777777777777778, 'f1-score': 0.7796168582375479, 'support': 54.0}}
No log 83.0 415 1.9978 {'0': {'precision': 0.625, 'recall': 0.9090909090909091, 'f1-score': 0.7407407407407407, 'support': 22.0}, '1': {'precision': 0.9090909090909091, 'recall': 0.625, 'f1-score': 0.7407407407407407, 'support': 32.0}, 'accuracy': 0.7407407407407407, 'macro avg': {'precision': 0.7670454545454546, 'recall': 0.7670454545454546, 'f1-score': 0.7407407407407407, 'support': 54.0}, 'weighted avg': {'precision': 0.7933501683501684, 'recall': 0.7407407407407407, 'f1-score': 0.7407407407407407, 'support': 54.0}}
No log 84.0 420 2.0396 {'0': {'precision': 0.6451612903225806, 'recall': 0.9090909090909091, 'f1-score': 0.7547169811320755, 'support': 22.0}, '1': {'precision': 0.9130434782608695, 'recall': 0.65625, 'f1-score': 0.7636363636363637, 'support': 32.0}, 'accuracy': 0.7592592592592593, 'macro avg': {'precision': 0.7791023842917251, 'recall': 0.7826704545454546, 'f1-score': 0.7591766723842196, 'support': 54.0}, 'weighted avg': {'precision': 0.8039062905823073, 'recall': 0.7592592592592593, 'f1-score': 0.7600025411346166, 'support': 54.0}}
No log 85.0 425 1.9938 {'0': {'precision': 0.6451612903225806, 'recall': 0.9090909090909091, 'f1-score': 0.7547169811320755, 'support': 22.0}, '1': {'precision': 0.9130434782608695, 'recall': 0.65625, 'f1-score': 0.7636363636363637, 'support': 32.0}, 'accuracy': 0.7592592592592593, 'macro avg': {'precision': 0.7791023842917251, 'recall': 0.7826704545454546, 'f1-score': 0.7591766723842196, 'support': 54.0}, 'weighted avg': {'precision': 0.8039062905823073, 'recall': 0.7592592592592593, 'f1-score': 0.7600025411346166, 'support': 54.0}}
No log 86.0 430 1.9613 {'0': {'precision': 0.625, 'recall': 0.9090909090909091, 'f1-score': 0.7407407407407407, 'support': 22.0}, '1': {'precision': 0.9090909090909091, 'recall': 0.625, 'f1-score': 0.7407407407407407, 'support': 32.0}, 'accuracy': 0.7407407407407407, 'macro avg': {'precision': 0.7670454545454546, 'recall': 0.7670454545454546, 'f1-score': 0.7407407407407407, 'support': 54.0}, 'weighted avg': {'precision': 0.7933501683501684, 'recall': 0.7407407407407407, 'f1-score': 0.7407407407407407, 'support': 54.0}}
No log 87.0 435 1.9588 {'0': {'precision': 0.625, 'recall': 0.9090909090909091, 'f1-score': 0.7407407407407407, 'support': 22.0}, '1': {'precision': 0.9090909090909091, 'recall': 0.625, 'f1-score': 0.7407407407407407, 'support': 32.0}, 'accuracy': 0.7407407407407407, 'macro avg': {'precision': 0.7670454545454546, 'recall': 0.7670454545454546, 'f1-score': 0.7407407407407407, 'support': 54.0}, 'weighted avg': {'precision': 0.7933501683501684, 'recall': 0.7407407407407407, 'f1-score': 0.7407407407407407, 'support': 54.0}}
No log 88.0 440 2.0127 {'0': {'precision': 0.6451612903225806, 'recall': 0.9090909090909091, 'f1-score': 0.7547169811320755, 'support': 22.0}, '1': {'precision': 0.9130434782608695, 'recall': 0.65625, 'f1-score': 0.7636363636363637, 'support': 32.0}, 'accuracy': 0.7592592592592593, 'macro avg': {'precision': 0.7791023842917251, 'recall': 0.7826704545454546, 'f1-score': 0.7591766723842196, 'support': 54.0}, 'weighted avg': {'precision': 0.8039062905823073, 'recall': 0.7592592592592593, 'f1-score': 0.7600025411346166, 'support': 54.0}}
No log 89.0 445 1.9300 {'0': {'precision': 0.6451612903225806, 'recall': 0.9090909090909091, 'f1-score': 0.7547169811320755, 'support': 22.0}, '1': {'precision': 0.9130434782608695, 'recall': 0.65625, 'f1-score': 0.7636363636363637, 'support': 32.0}, 'accuracy': 0.7592592592592593, 'macro avg': {'precision': 0.7791023842917251, 'recall': 0.7826704545454546, 'f1-score': 0.7591766723842196, 'support': 54.0}, 'weighted avg': {'precision': 0.8039062905823073, 'recall': 0.7592592592592593, 'f1-score': 0.7600025411346166, 'support': 54.0}}
No log 90.0 450 1.9803 {'0': {'precision': 0.6451612903225806, 'recall': 0.9090909090909091, 'f1-score': 0.7547169811320755, 'support': 22.0}, '1': {'precision': 0.9130434782608695, 'recall': 0.65625, 'f1-score': 0.7636363636363637, 'support': 32.0}, 'accuracy': 0.7592592592592593, 'macro avg': {'precision': 0.7791023842917251, 'recall': 0.7826704545454546, 'f1-score': 0.7591766723842196, 'support': 54.0}, 'weighted avg': {'precision': 0.8039062905823073, 'recall': 0.7592592592592593, 'f1-score': 0.7600025411346166, 'support': 54.0}}
No log 91.0 455 1.9776 {'0': {'precision': 0.6666666666666666, 'recall': 0.9090909090909091, 'f1-score': 0.7692307692307693, 'support': 22.0}, '1': {'precision': 0.9166666666666666, 'recall': 0.6875, 'f1-score': 0.7857142857142857, 'support': 32.0}, 'accuracy': 0.7777777777777778, 'macro avg': {'precision': 0.7916666666666666, 'recall': 0.7982954545454546, 'f1-score': 0.7774725274725275, 'support': 54.0}, 'weighted avg': {'precision': 0.8148148148148148, 'recall': 0.7777777777777778, 'f1-score': 0.778998778998779, 'support': 54.0}}
No log 92.0 460 1.9394 {'0': {'precision': 0.6666666666666666, 'recall': 0.9090909090909091, 'f1-score': 0.7692307692307693, 'support': 22.0}, '1': {'precision': 0.9166666666666666, 'recall': 0.6875, 'f1-score': 0.7857142857142857, 'support': 32.0}, 'accuracy': 0.7777777777777778, 'macro avg': {'precision': 0.7916666666666666, 'recall': 0.7982954545454546, 'f1-score': 0.7774725274725275, 'support': 54.0}, 'weighted avg': {'precision': 0.8148148148148148, 'recall': 0.7777777777777778, 'f1-score': 0.778998778998779, 'support': 54.0}}
No log 93.0 465 1.9882 {'0': {'precision': 0.6451612903225806, 'recall': 0.9090909090909091, 'f1-score': 0.7547169811320755, 'support': 22.0}, '1': {'precision': 0.9130434782608695, 'recall': 0.65625, 'f1-score': 0.7636363636363637, 'support': 32.0}, 'accuracy': 0.7592592592592593, 'macro avg': {'precision': 0.7791023842917251, 'recall': 0.7826704545454546, 'f1-score': 0.7591766723842196, 'support': 54.0}, 'weighted avg': {'precision': 0.8039062905823073, 'recall': 0.7592592592592593, 'f1-score': 0.7600025411346166, 'support': 54.0}}
No log 94.0 470 1.9196 {'0': {'precision': 0.6451612903225806, 'recall': 0.9090909090909091, 'f1-score': 0.7547169811320755, 'support': 22.0}, '1': {'precision': 0.9130434782608695, 'recall': 0.65625, 'f1-score': 0.7636363636363637, 'support': 32.0}, 'accuracy': 0.7592592592592593, 'macro avg': {'precision': 0.7791023842917251, 'recall': 0.7826704545454546, 'f1-score': 0.7591766723842196, 'support': 54.0}, 'weighted avg': {'precision': 0.8039062905823073, 'recall': 0.7592592592592593, 'f1-score': 0.7600025411346166, 'support': 54.0}}
No log 95.0 475 1.9760 {'0': {'precision': 0.625, 'recall': 0.9090909090909091, 'f1-score': 0.7407407407407407, 'support': 22.0}, '1': {'precision': 0.9090909090909091, 'recall': 0.625, 'f1-score': 0.7407407407407407, 'support': 32.0}, 'accuracy': 0.7407407407407407, 'macro avg': {'precision': 0.7670454545454546, 'recall': 0.7670454545454546, 'f1-score': 0.7407407407407407, 'support': 54.0}, 'weighted avg': {'precision': 0.7933501683501684, 'recall': 0.7407407407407407, 'f1-score': 0.7407407407407407, 'support': 54.0}}
No log 96.0 480 2.0279 {'0': {'precision': 0.6451612903225806, 'recall': 0.9090909090909091, 'f1-score': 0.7547169811320755, 'support': 22.0}, '1': {'precision': 0.9130434782608695, 'recall': 0.65625, 'f1-score': 0.7636363636363637, 'support': 32.0}, 'accuracy': 0.7592592592592593, 'macro avg': {'precision': 0.7791023842917251, 'recall': 0.7826704545454546, 'f1-score': 0.7591766723842196, 'support': 54.0}, 'weighted avg': {'precision': 0.8039062905823073, 'recall': 0.7592592592592593, 'f1-score': 0.7600025411346166, 'support': 54.0}}
No log 97.0 485 2.0663 {'0': {'precision': 0.625, 'recall': 0.9090909090909091, 'f1-score': 0.7407407407407407, 'support': 22.0}, '1': {'precision': 0.9090909090909091, 'recall': 0.625, 'f1-score': 0.7407407407407407, 'support': 32.0}, 'accuracy': 0.7407407407407407, 'macro avg': {'precision': 0.7670454545454546, 'recall': 0.7670454545454546, 'f1-score': 0.7407407407407407, 'support': 54.0}, 'weighted avg': {'precision': 0.7933501683501684, 'recall': 0.7407407407407407, 'f1-score': 0.7407407407407407, 'support': 54.0}}
No log 98.0 490 1.9973 {'0': {'precision': 0.6666666666666666, 'recall': 0.9090909090909091, 'f1-score': 0.7692307692307693, 'support': 22.0}, '1': {'precision': 0.9166666666666666, 'recall': 0.6875, 'f1-score': 0.7857142857142857, 'support': 32.0}, 'accuracy': 0.7777777777777778, 'macro avg': {'precision': 0.7916666666666666, 'recall': 0.7982954545454546, 'f1-score': 0.7774725274725275, 'support': 54.0}, 'weighted avg': {'precision': 0.8148148148148148, 'recall': 0.7777777777777778, 'f1-score': 0.778998778998779, 'support': 54.0}}
No log 99.0 495 1.9832 {'0': {'precision': 0.6451612903225806, 'recall': 0.9090909090909091, 'f1-score': 0.7547169811320755, 'support': 22.0}, '1': {'precision': 0.9130434782608695, 'recall': 0.65625, 'f1-score': 0.7636363636363637, 'support': 32.0}, 'accuracy': 0.7592592592592593, 'macro avg': {'precision': 0.7791023842917251, 'recall': 0.7826704545454546, 'f1-score': 0.7591766723842196, 'support': 54.0}, 'weighted avg': {'precision': 0.8039062905823073, 'recall': 0.7592592592592593, 'f1-score': 0.7600025411346166, 'support': 54.0}}
0.2328 100.0 500 2.0200 {'0': {'precision': 0.6451612903225806, 'recall': 0.9090909090909091, 'f1-score': 0.7547169811320755, 'support': 22.0}, '1': {'precision': 0.9130434782608695, 'recall': 0.65625, 'f1-score': 0.7636363636363637, 'support': 32.0}, 'accuracy': 0.7592592592592593, 'macro avg': {'precision': 0.7791023842917251, 'recall': 0.7826704545454546, 'f1-score': 0.7591766723842196, 'support': 54.0}, 'weighted avg': {'precision': 0.8039062905823073, 'recall': 0.7592592592592593, 'f1-score': 0.7600025411346166, 'support': 54.0}}

Framework versions

  • Transformers 4.51.1
  • Pytorch 2.6.0+cu124
  • Datasets 3.5.0
  • Tokenizers 0.21.1
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