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: 1.8529
• Classification Report: {'0': {'precision': 0.6190476190476191, 'recall': 0.5909090909090909, 'f1-score': 0.6046511627906976, 'support': 22.0}, '1': {'precision': 0.7272727272727273, 'recall': 0.75, 'f1-score': 0.7384615384615385, 'support': 32.0}, 'accuracy': 0.6851851851851852, 'macro avg': {'precision': 0.6731601731601732, 'recall': 0.6704545454545454, 'f1-score': 0.6715563506261181, 'support': 54.0}, 'weighted avg': {'precision': 0.6831810165143499, 'recall': 0.6851851851851852, 'f1-score': 0.6839462002252701, '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: 2e-05
• train_batch_size: 4
• eval_batch_size: 4
• seed: 42
• distributed_type: multi-GPU
• num_devices: 4
• gradient_accumulation_steps: 6
• total_train_batch_size: 96
• total_eval_batch_size: 16
• 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: 200

Training results

Training Loss	Epoch	Step	Validation Loss	Classification Report
No log	1.0	2	0.7209	{'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	4	0.6979	{'0': {'precision': 0.37037037037037035, 'recall': 0.45454545454545453, 'f1-score': 0.40816326530612246, 'support': 22.0}, '1': {'precision': 0.5555555555555556, 'recall': 0.46875, 'f1-score': 0.5084745762711864, 'support': 32.0}, 'accuracy': 0.46296296296296297, 'macro avg': {'precision': 0.46296296296296297, 'recall': 0.4616477272727273, 'f1-score': 0.45831892078865444, 'support': 54.0}, 'weighted avg': {'precision': 0.4801097393689986, 'recall': 0.46296296296296297, 'f1-score': 0.4676070051372715, 'support': 54.0}}
No log	3.0	6	0.6671	{'0': {'precision': 0.5833333333333334, 'recall': 0.3181818181818182, 'f1-score': 0.4117647058823529, 'support': 22.0}, '1': {'precision': 0.6428571428571429, 'recall': 0.84375, 'f1-score': 0.7297297297297297, 'support': 32.0}, 'accuracy': 0.6296296296296297, 'macro avg': {'precision': 0.6130952380952381, 'recall': 0.5809659090909091, 'f1-score': 0.5707472178060413, 'support': 54.0}, 'weighted avg': {'precision': 0.6186067019400353, 'recall': 0.6296296296296297, 'f1-score': 0.6001884237178354, 'support': 54.0}}
No log	4.0	8	0.6354	{'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	5.0	10	0.6677	{'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	12	0.6094	{'0': {'precision': 0.5757575757575758, 'recall': 0.8636363636363636, 'f1-score': 0.6909090909090909, 'support': 22.0}, '1': {'precision': 0.8571428571428571, 'recall': 0.5625, 'f1-score': 0.6792452830188679, 'support': 32.0}, 'accuracy': 0.6851851851851852, 'macro avg': {'precision': 0.7164502164502164, 'recall': 0.7130681818181819, 'f1-score': 0.6850771869639793, 'support': 54.0}, 'weighted avg': {'precision': 0.7425044091710759, 'recall': 0.6851851851851852, 'f1-score': 0.6839972047519217, 'support': 54.0}}
No log	7.0	14	0.5932	{'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	8.0	16	0.5522	{'0': {'precision': 0.6, 'recall': 0.5454545454545454, 'f1-score': 0.5714285714285714, 'support': 22.0}, '1': {'precision': 0.7058823529411765, 'recall': 0.75, 'f1-score': 0.7272727272727273, 'support': 32.0}, 'accuracy': 0.6666666666666666, 'macro avg': {'precision': 0.6529411764705882, 'recall': 0.6477272727272727, 'f1-score': 0.6493506493506493, 'support': 54.0}, 'weighted avg': {'precision': 0.6627450980392157, 'recall': 0.6666666666666666, 'f1-score': 0.6637806637806638, 'support': 54.0}}
No log	9.0	18	0.5464	{'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	10.0	20	0.5100	{'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	11.0	22	0.6511	{'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	12.0	24	0.5125	{'0': {'precision': 0.5909090909090909, 'recall': 0.5909090909090909, 'f1-score': 0.5909090909090909, 'support': 22.0}, '1': {'precision': 0.71875, 'recall': 0.71875, 'f1-score': 0.71875, 'support': 32.0}, 'accuracy': 0.6666666666666666, 'macro avg': {'precision': 0.6548295454545454, 'recall': 0.6548295454545454, 'f1-score': 0.6548295454545454, 'support': 54.0}, 'weighted avg': {'precision': 0.6666666666666666, 'recall': 0.6666666666666666, 'f1-score': 0.6666666666666666, 'support': 54.0}}
No log	13.0	26	0.8356	{'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	14.0	28	0.5437	{'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	15.0	30	1.0100	{'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	16.0	32	0.5221	{'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	17.0	34	0.9995	{'0': {'precision': 0.46808510638297873, 'recall': 1.0, 'f1-score': 0.6376811594202898, 'support': 22.0}, '1': {'precision': 1.0, 'recall': 0.21875, 'f1-score': 0.358974358974359, 'support': 32.0}, 'accuracy': 0.5370370370370371, 'macro avg': {'precision': 0.7340425531914894, 'recall': 0.609375, 'f1-score': 0.4983277591973244, 'support': 54.0}, 'weighted avg': {'precision': 0.7832939322301024, 'recall': 0.5370370370370371, 'f1-score': 0.4725215739708493, 'support': 54.0}}
No log	18.0	36	0.5530	{'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	19.0	38	0.9546	{'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	20.0	40	0.6552	{'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	21.0	42	0.8746	{'0': {'precision': 0.4230769230769231, 'recall': 1.0, 'f1-score': 0.5945945945945946, 'support': 22.0}, '1': {'precision': 1.0, 'recall': 0.0625, 'f1-score': 0.11764705882352941, 'support': 32.0}, 'accuracy': 0.4444444444444444, 'macro avg': {'precision': 0.7115384615384616, 'recall': 0.53125, 'f1-score': 0.35612082670906203, 'support': 54.0}, 'weighted avg': {'precision': 0.7649572649572649, 'recall': 0.4444444444444444, 'f1-score': 0.3119590178413708, 'support': 54.0}}
No log	22.0	44	1.0083	{'0': {'precision': 0.41509433962264153, 'recall': 1.0, 'f1-score': 0.5866666666666667, 'support': 22.0}, '1': {'precision': 1.0, 'recall': 0.03125, 'f1-score': 0.06060606060606061, 'support': 32.0}, 'accuracy': 0.42592592592592593, 'macro avg': {'precision': 0.7075471698113207, 'recall': 0.515625, 'f1-score': 0.3236363636363636, 'support': 54.0}, 'weighted avg': {'precision': 0.7617051013277428, 'recall': 0.42592592592592593, 'f1-score': 0.2749270482603816, 'support': 54.0}}
No log	23.0	46	0.5660	{'0': {'precision': 0.6, 'recall': 0.5454545454545454, 'f1-score': 0.5714285714285714, 'support': 22.0}, '1': {'precision': 0.7058823529411765, 'recall': 0.75, 'f1-score': 0.7272727272727273, 'support': 32.0}, 'accuracy': 0.6666666666666666, 'macro avg': {'precision': 0.6529411764705882, 'recall': 0.6477272727272727, 'f1-score': 0.6493506493506493, 'support': 54.0}, 'weighted avg': {'precision': 0.6627450980392157, 'recall': 0.6666666666666666, 'f1-score': 0.6637806637806638, 'support': 54.0}}
No log	24.0	48	0.6164	{'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	25.0	50	0.6519	{'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	26.0	52	0.6560	{'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	54	0.5364	{'0': {'precision': 0.6470588235294118, 'recall': 0.5, 'f1-score': 0.5641025641025641, 'support': 22.0}, '1': {'precision': 0.7027027027027027, 'recall': 0.8125, 'f1-score': 0.7536231884057971, 'support': 32.0}, 'accuracy': 0.6851851851851852, 'macro avg': {'precision': 0.6748807631160573, 'recall': 0.65625, 'f1-score': 0.6588628762541806, 'support': 54.0}, 'weighted avg': {'precision': 0.6800329741506212, 'recall': 0.6851851851851852, 'f1-score': 0.6764110822081837, 'support': 54.0}}
No log	28.0	56	0.5465	{'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	29.0	58	0.5741	{'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	30.0	60	0.5584	{'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	31.0	62	0.6725	{'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	32.0	64	0.6997	{'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	33.0	66	0.5377	{'0': {'precision': 0.7333333333333333, 'recall': 0.5, 'f1-score': 0.5945945945945946, 'support': 22.0}, '1': {'precision': 0.717948717948718, 'recall': 0.875, 'f1-score': 0.7887323943661971, 'support': 32.0}, 'accuracy': 0.7222222222222222, 'macro avg': {'precision': 0.7256410256410256, 'recall': 0.6875, 'f1-score': 0.6916634944803959, 'support': 54.0}, 'weighted avg': {'precision': 0.7242165242165242, 'recall': 0.7222222222222222, 'f1-score': 0.7096392166814702, 'support': 54.0}}
No log	34.0	68	0.5292	{'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	35.0	70	0.5622	{'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	36.0	72	0.5362	{'0': {'precision': 0.6071428571428571, 'recall': 0.7727272727272727, 'f1-score': 0.68, 'support': 22.0}, '1': {'precision': 0.8076923076923077, 'recall': 0.65625, 'f1-score': 0.7241379310344828, 'support': 32.0}, 'accuracy': 0.7037037037037037, 'macro avg': {'precision': 0.7074175824175823, 'recall': 0.7144886363636364, 'f1-score': 0.7020689655172414, 'support': 54.0}, 'weighted avg': {'precision': 0.7259869759869759, 'recall': 0.7037037037037037, 'f1-score': 0.7061558109833973, 'support': 54.0}}
No log	37.0	74	0.5145	{'0': {'precision': 0.6190476190476191, 'recall': 0.5909090909090909, 'f1-score': 0.6046511627906976, 'support': 22.0}, '1': {'precision': 0.7272727272727273, 'recall': 0.75, 'f1-score': 0.7384615384615385, 'support': 32.0}, 'accuracy': 0.6851851851851852, 'macro avg': {'precision': 0.6731601731601732, 'recall': 0.6704545454545454, 'f1-score': 0.6715563506261181, 'support': 54.0}, 'weighted avg': {'precision': 0.6831810165143499, 'recall': 0.6851851851851852, 'f1-score': 0.6839462002252701, 'support': 54.0}}
No log	38.0	76	0.5138	{'0': {'precision': 0.6, 'recall': 0.5454545454545454, 'f1-score': 0.5714285714285714, 'support': 22.0}, '1': {'precision': 0.7058823529411765, 'recall': 0.75, 'f1-score': 0.7272727272727273, 'support': 32.0}, 'accuracy': 0.6666666666666666, 'macro avg': {'precision': 0.6529411764705882, 'recall': 0.6477272727272727, 'f1-score': 0.6493506493506493, 'support': 54.0}, 'weighted avg': {'precision': 0.6627450980392157, 'recall': 0.6666666666666666, 'f1-score': 0.6637806637806638, 'support': 54.0}}
No log	39.0	78	0.5959	{'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	40.0	80	0.6088	{'0': {'precision': 0.5757575757575758, 'recall': 0.8636363636363636, 'f1-score': 0.6909090909090909, 'support': 22.0}, '1': {'precision': 0.8571428571428571, 'recall': 0.5625, 'f1-score': 0.6792452830188679, 'support': 32.0}, 'accuracy': 0.6851851851851852, 'macro avg': {'precision': 0.7164502164502164, 'recall': 0.7130681818181819, 'f1-score': 0.6850771869639793, 'support': 54.0}, 'weighted avg': {'precision': 0.7425044091710759, 'recall': 0.6851851851851852, 'f1-score': 0.6839972047519217, 'support': 54.0}}
No log	41.0	82	0.5925	{'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	42.0	84	0.5495	{'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	43.0	86	0.5601	{'0': {'precision': 0.7857142857142857, 'recall': 0.5, 'f1-score': 0.6111111111111112, 'support': 22.0}, '1': {'precision': 0.725, 'recall': 0.90625, 'f1-score': 0.8055555555555556, 'support': 32.0}, 'accuracy': 0.7407407407407407, 'macro avg': {'precision': 0.7553571428571428, 'recall': 0.703125, 'f1-score': 0.7083333333333334, 'support': 54.0}, 'weighted avg': {'precision': 0.7497354497354496, 'recall': 0.7407407407407407, 'f1-score': 0.7263374485596709, 'support': 54.0}}
No log	44.0	88	0.6877	{'0': {'precision': 0.5, 'recall': 0.9090909090909091, 'f1-score': 0.6451612903225806, 'support': 22.0}, '1': {'precision': 0.8571428571428571, 'recall': 0.375, 'f1-score': 0.5217391304347826, 'support': 32.0}, 'accuracy': 0.5925925925925926, 'macro avg': {'precision': 0.6785714285714286, 'recall': 0.6420454545454546, 'f1-score': 0.5834502103786816, 'support': 54.0}, 'weighted avg': {'precision': 0.7116402116402116, 'recall': 0.5925925925925926, 'f1-score': 0.5720222326112929, 'support': 54.0}}
No log	45.0	90	0.5248	{'0': {'precision': 0.7142857142857143, 'recall': 0.6818181818181818, 'f1-score': 0.6976744186046512, 'support': 22.0}, '1': {'precision': 0.7878787878787878, 'recall': 0.8125, 'f1-score': 0.8, 'support': 32.0}, 'accuracy': 0.7592592592592593, 'macro avg': {'precision': 0.751082251082251, 'recall': 0.7471590909090908, 'f1-score': 0.7488372093023257, 'support': 54.0}, 'weighted avg': {'precision': 0.7578964245630913, 'recall': 0.7592592592592593, 'f1-score': 0.7583118001722653, 'support': 54.0}}
No log	46.0	92	0.5311	{'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	47.0	94	0.5751	{'0': {'precision': 0.5862068965517241, 'recall': 0.7727272727272727, 'f1-score': 0.6666666666666666, 'support': 22.0}, '1': {'precision': 0.8, 'recall': 0.625, 'f1-score': 0.7017543859649122, 'support': 32.0}, 'accuracy': 0.6851851851851852, 'macro avg': {'precision': 0.693103448275862, 'recall': 0.6988636363636364, 'f1-score': 0.6842105263157894, 'support': 54.0}, 'weighted avg': {'precision': 0.7128991060025542, 'recall': 0.6851851851851852, 'f1-score': 0.6874593892137751, 'support': 54.0}}
No log	48.0	96	0.7343	{'0': {'precision': 0.525, 'recall': 0.9545454545454546, 'f1-score': 0.6774193548387096, 'support': 22.0}, '1': {'precision': 0.9285714285714286, 'recall': 0.40625, 'f1-score': 0.5652173913043478, 'support': 32.0}, 'accuracy': 0.6296296296296297, 'macro avg': {'precision': 0.7267857142857144, 'recall': 0.6803977272727273, 'f1-score': 0.6213183730715287, 'support': 54.0}, 'weighted avg': {'precision': 0.7641534391534393, 'recall': 0.6296296296296297, 'f1-score': 0.6109293023739026, 'support': 54.0}}
No log	49.0	98	0.5835	{'0': {'precision': 0.7333333333333333, 'recall': 0.5, 'f1-score': 0.5945945945945946, 'support': 22.0}, '1': {'precision': 0.717948717948718, 'recall': 0.875, 'f1-score': 0.7887323943661971, 'support': 32.0}, 'accuracy': 0.7222222222222222, 'macro avg': {'precision': 0.7256410256410256, 'recall': 0.6875, 'f1-score': 0.6916634944803959, 'support': 54.0}, 'weighted avg': {'precision': 0.7242165242165242, 'recall': 0.7222222222222222, 'f1-score': 0.7096392166814702, 'support': 54.0}}
No log	50.0	100	0.6479	{'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	51.0	102	0.6786	{'0': {'precision': 0.8888888888888888, 'recall': 0.36363636363636365, 'f1-score': 0.5161290322580645, 'support': 22.0}, '1': {'precision': 0.6888888888888889, 'recall': 0.96875, 'f1-score': 0.8051948051948052, 'support': 32.0}, 'accuracy': 0.7222222222222222, 'macro avg': {'precision': 0.7888888888888889, 'recall': 0.6661931818181819, 'f1-score': 0.6606619187264349, 'support': 54.0}, 'weighted avg': {'precision': 0.7703703703703703, 'recall': 0.7222222222222222, 'f1-score': 0.6874272680724295, 'support': 54.0}}
No log	52.0	104	0.7005	{'0': {'precision': 0.5294117647058824, 'recall': 0.8181818181818182, 'f1-score': 0.6428571428571429, 'support': 22.0}, '1': {'precision': 0.8, 'recall': 0.5, 'f1-score': 0.6153846153846154, 'support': 32.0}, 'accuracy': 0.6296296296296297, 'macro avg': {'precision': 0.6647058823529413, 'recall': 0.6590909090909092, 'f1-score': 0.6291208791208791, 'support': 54.0}, 'weighted avg': {'precision': 0.689760348583878, 'recall': 0.6296296296296297, 'f1-score': 0.6265771265771266, 'support': 54.0}}
No log	53.0	106	0.6526	{'0': {'precision': 0.8333333333333334, 'recall': 0.45454545454545453, 'f1-score': 0.5882352941176471, 'support': 22.0}, '1': {'precision': 0.7142857142857143, 'recall': 0.9375, 'f1-score': 0.8108108108108109, 'support': 32.0}, 'accuracy': 0.7407407407407407, 'macro avg': {'precision': 0.7738095238095238, 'recall': 0.6960227272727273, 'f1-score': 0.699523052464229, 'support': 54.0}, 'weighted avg': {'precision': 0.7627865961199295, 'recall': 0.7407407407407407, 'f1-score': 0.7201318966024849, 'support': 54.0}}
No log	54.0	108	0.6463	{'0': {'precision': 0.5, 'recall': 0.8181818181818182, 'f1-score': 0.6206896551724138, 'support': 22.0}, '1': {'precision': 0.7777777777777778, 'recall': 0.4375, 'f1-score': 0.56, 'support': 32.0}, 'accuracy': 0.5925925925925926, 'macro avg': {'precision': 0.6388888888888888, 'recall': 0.6278409090909092, 'f1-score': 0.5903448275862069, 'support': 54.0}, 'weighted avg': {'precision': 0.6646090534979423, 'recall': 0.5925925925925926, 'f1-score': 0.5847254150702427, 'support': 54.0}}
No log	55.0	110	0.5458	{'0': {'precision': 0.75, 'recall': 0.5454545454545454, 'f1-score': 0.631578947368421, 'support': 22.0}, '1': {'precision': 0.7368421052631579, 'recall': 0.875, 'f1-score': 0.8, 'support': 32.0}, 'accuracy': 0.7407407407407407, 'macro avg': {'precision': 0.743421052631579, 'recall': 0.7102272727272727, 'f1-score': 0.7157894736842105, 'support': 54.0}, 'weighted avg': {'precision': 0.7422027290448343, 'recall': 0.7407407407407407, 'f1-score': 0.7313840155945419, 'support': 54.0}}
No log	56.0	112	0.8544	{'0': {'precision': 0.5, 'recall': 0.9090909090909091, 'f1-score': 0.6451612903225806, 'support': 22.0}, '1': {'precision': 0.8571428571428571, 'recall': 0.375, 'f1-score': 0.5217391304347826, 'support': 32.0}, 'accuracy': 0.5925925925925926, 'macro avg': {'precision': 0.6785714285714286, 'recall': 0.6420454545454546, 'f1-score': 0.5834502103786816, 'support': 54.0}, 'weighted avg': {'precision': 0.7116402116402116, 'recall': 0.5925925925925926, 'f1-score': 0.5720222326112929, 'support': 54.0}}
No log	57.0	114	0.5544	{'0': {'precision': 0.7777777777777778, 'recall': 0.6363636363636364, 'f1-score': 0.7, 'support': 22.0}, '1': {'precision': 0.7777777777777778, 'recall': 0.875, 'f1-score': 0.8235294117647058, 'support': 32.0}, 'accuracy': 0.7777777777777778, 'macro avg': {'precision': 0.7777777777777778, 'recall': 0.7556818181818181, 'f1-score': 0.7617647058823529, 'support': 54.0}, 'weighted avg': {'precision': 0.7777777777777778, 'recall': 0.7777777777777778, 'f1-score': 0.7732026143790849, 'support': 54.0}}
No log	58.0	116	1.0415	{'0': {'precision': 0.5, 'recall': 0.9090909090909091, 'f1-score': 0.6451612903225806, 'support': 22.0}, '1': {'precision': 0.8571428571428571, 'recall': 0.375, 'f1-score': 0.5217391304347826, 'support': 32.0}, 'accuracy': 0.5925925925925926, 'macro avg': {'precision': 0.6785714285714286, 'recall': 0.6420454545454546, 'f1-score': 0.5834502103786816, 'support': 54.0}, 'weighted avg': {'precision': 0.7116402116402116, 'recall': 0.5925925925925926, 'f1-score': 0.5720222326112929, 'support': 54.0}}
No log	59.0	118	0.6777	{'0': {'precision': 0.7647058823529411, 'recall': 0.5909090909090909, 'f1-score': 0.6666666666666666, 'support': 22.0}, '1': {'precision': 0.7567567567567568, 'recall': 0.875, 'f1-score': 0.8115942028985508, 'support': 32.0}, 'accuracy': 0.7592592592592593, 'macro avg': {'precision': 0.760731319554849, 'recall': 0.7329545454545454, 'f1-score': 0.7391304347826086, 'support': 54.0}, 'weighted avg': {'precision': 0.7599952894070541, 'recall': 0.7592592592592593, 'f1-score': 0.7525496511003757, 'support': 54.0}}
No log	60.0	120	1.1707	{'0': {'precision': 0.4878048780487805, 'recall': 0.9090909090909091, 'f1-score': 0.6349206349206349, 'support': 22.0}, '1': {'precision': 0.8461538461538461, 'recall': 0.34375, 'f1-score': 0.4888888888888889, 'support': 32.0}, 'accuracy': 0.5740740740740741, 'macro avg': {'precision': 0.6669793621013134, 'recall': 0.6264204545454546, 'f1-score': 0.5619047619047619, 'support': 54.0}, 'weighted avg': {'precision': 0.7001598221110416, 'recall': 0.5740740740740741, 'f1-score': 0.5483833039388595, 'support': 54.0}}
No log	61.0	122	0.7503	{'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	62.0	124	0.9343	{'0': {'precision': 0.5128205128205128, 'recall': 0.9090909090909091, 'f1-score': 0.6557377049180327, 'support': 22.0}, '1': {'precision': 0.8666666666666667, 'recall': 0.40625, 'f1-score': 0.5531914893617021, 'support': 32.0}, 'accuracy': 0.6111111111111112, 'macro avg': {'precision': 0.6897435897435897, 'recall': 0.6576704545454546, 'f1-score': 0.6044645971398674, 'support': 54.0}, 'weighted avg': {'precision': 0.7225071225071226, 'recall': 0.6111111111111112, 'f1-score': 0.5949695771809479, 'support': 54.0}}
No log	63.0	126	0.8613	{'0': {'precision': 0.7, 'recall': 0.3181818181818182, 'f1-score': 0.4375, 'support': 22.0}, '1': {'precision': 0.6590909090909091, 'recall': 0.90625, 'f1-score': 0.7631578947368421, 'support': 32.0}, 'accuracy': 0.6666666666666666, 'macro avg': {'precision': 0.6795454545454545, 'recall': 0.6122159090909091, 'f1-score': 0.600328947368421, 'support': 54.0}, 'weighted avg': {'precision': 0.6757575757575757, 'recall': 0.6666666666666666, 'f1-score': 0.6304824561403508, 'support': 54.0}}
No log	64.0	128	0.8207	{'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	130	0.8506	{'0': {'precision': 0.7272727272727273, 'recall': 0.36363636363636365, 'f1-score': 0.48484848484848486, 'support': 22.0}, '1': {'precision': 0.6744186046511628, 'recall': 0.90625, 'f1-score': 0.7733333333333333, 'support': 32.0}, 'accuracy': 0.6851851851851852, 'macro avg': {'precision': 0.7008456659619451, 'recall': 0.6349431818181819, 'f1-score': 0.6290909090909091, 'support': 54.0}, 'weighted avg': {'precision': 0.6959517657192075, 'recall': 0.6851851851851852, 'f1-score': 0.6558024691358025, 'support': 54.0}}
No log	66.0	132	0.8638	{'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	67.0	134	1.0595	{'0': {'precision': 0.7, 'recall': 0.3181818181818182, 'f1-score': 0.4375, 'support': 22.0}, '1': {'precision': 0.6590909090909091, 'recall': 0.90625, 'f1-score': 0.7631578947368421, 'support': 32.0}, 'accuracy': 0.6666666666666666, 'macro avg': {'precision': 0.6795454545454545, 'recall': 0.6122159090909091, 'f1-score': 0.600328947368421, 'support': 54.0}, 'weighted avg': {'precision': 0.6757575757575757, 'recall': 0.6666666666666666, 'f1-score': 0.6304824561403508, 'support': 54.0}}
No log	68.0	136	0.8218	{'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	69.0	138	1.1223	{'0': {'precision': 0.7, 'recall': 0.3181818181818182, 'f1-score': 0.4375, 'support': 22.0}, '1': {'precision': 0.6590909090909091, 'recall': 0.90625, 'f1-score': 0.7631578947368421, 'support': 32.0}, 'accuracy': 0.6666666666666666, 'macro avg': {'precision': 0.6795454545454545, 'recall': 0.6122159090909091, 'f1-score': 0.600328947368421, 'support': 54.0}, 'weighted avg': {'precision': 0.6757575757575757, 'recall': 0.6666666666666666, 'f1-score': 0.6304824561403508, 'support': 54.0}}
No log	70.0	140	0.9777	{'0': {'precision': 0.5555555555555556, 'recall': 0.9090909090909091, 'f1-score': 0.6896551724137931, 'support': 22.0}, '1': {'precision': 0.8888888888888888, 'recall': 0.5, 'f1-score': 0.64, 'support': 32.0}, 'accuracy': 0.6666666666666666, 'macro avg': {'precision': 0.7222222222222222, 'recall': 0.7045454545454546, 'f1-score': 0.6648275862068966, 'support': 54.0}, 'weighted avg': {'precision': 0.7530864197530863, 'recall': 0.6666666666666666, 'f1-score': 0.6602298850574713, 'support': 54.0}}
No log	71.0	142	1.0496	{'0': {'precision': 0.7272727272727273, 'recall': 0.36363636363636365, 'f1-score': 0.48484848484848486, 'support': 22.0}, '1': {'precision': 0.6744186046511628, 'recall': 0.90625, 'f1-score': 0.7733333333333333, 'support': 32.0}, 'accuracy': 0.6851851851851852, 'macro avg': {'precision': 0.7008456659619451, 'recall': 0.6349431818181819, 'f1-score': 0.6290909090909091, 'support': 54.0}, 'weighted avg': {'precision': 0.6959517657192075, 'recall': 0.6851851851851852, 'f1-score': 0.6558024691358025, 'support': 54.0}}
No log	72.0	144	1.1527	{'0': {'precision': 0.5405405405405406, 'recall': 0.9090909090909091, 'f1-score': 0.6779661016949152, 'support': 22.0}, '1': {'precision': 0.8823529411764706, 'recall': 0.46875, 'f1-score': 0.6122448979591837, 'support': 32.0}, 'accuracy': 0.6481481481481481, 'macro avg': {'precision': 0.7114467408585056, 'recall': 0.6889204545454546, 'f1-score': 0.6451054998270495, 'support': 54.0}, 'weighted avg': {'precision': 0.7430960372136843, 'recall': 0.6481481481481481, 'f1-score': 0.639020203184852, 'support': 54.0}}
No log	73.0	146	1.1923	{'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	74.0	148	1.2597	{'0': {'precision': 0.5757575757575758, 'recall': 0.8636363636363636, 'f1-score': 0.6909090909090909, 'support': 22.0}, '1': {'precision': 0.8571428571428571, 'recall': 0.5625, 'f1-score': 0.6792452830188679, 'support': 32.0}, 'accuracy': 0.6851851851851852, 'macro avg': {'precision': 0.7164502164502164, 'recall': 0.7130681818181819, 'f1-score': 0.6850771869639793, 'support': 54.0}, 'weighted avg': {'precision': 0.7425044091710759, 'recall': 0.6851851851851852, 'f1-score': 0.6839972047519217, 'support': 54.0}}
No log	75.0	150	1.5068	{'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	76.0	152	1.3156	{'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	77.0	154	1.1601	{'0': {'precision': 0.65, 'recall': 0.5909090909090909, 'f1-score': 0.6190476190476191, 'support': 22.0}, '1': {'precision': 0.7352941176470589, 'recall': 0.78125, 'f1-score': 0.7575757575757576, 'support': 32.0}, 'accuracy': 0.7037037037037037, 'macro avg': {'precision': 0.6926470588235294, 'recall': 0.6860795454545454, 'f1-score': 0.6883116883116883, 'support': 54.0}, 'weighted avg': {'precision': 0.7005446623093682, 'recall': 0.7037037037037037, 'f1-score': 0.7011383678050345, 'support': 54.0}}
No log	78.0	156	1.3362	{'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	79.0	158	1.5329	{'0': {'precision': 0.6428571428571429, 'recall': 0.4090909090909091, 'f1-score': 0.5, 'support': 22.0}, '1': {'precision': 0.675, 'recall': 0.84375, 'f1-score': 0.75, 'support': 32.0}, 'accuracy': 0.6666666666666666, 'macro avg': {'precision': 0.6589285714285715, 'recall': 0.6264204545454546, 'f1-score': 0.625, 'support': 54.0}, 'weighted avg': {'precision': 0.661904761904762, 'recall': 0.6666666666666666, 'f1-score': 0.6481481481481481, 'support': 54.0}}
No log	80.0	160	1.3651	{'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	81.0	162	1.5115	{'0': {'precision': 0.6071428571428571, 'recall': 0.7727272727272727, 'f1-score': 0.68, 'support': 22.0}, '1': {'precision': 0.8076923076923077, 'recall': 0.65625, 'f1-score': 0.7241379310344828, 'support': 32.0}, 'accuracy': 0.7037037037037037, 'macro avg': {'precision': 0.7074175824175823, 'recall': 0.7144886363636364, 'f1-score': 0.7020689655172414, 'support': 54.0}, 'weighted avg': {'precision': 0.7259869759869759, 'recall': 0.7037037037037037, 'f1-score': 0.7061558109833973, 'support': 54.0}}
No log	82.0	164	1.4976	{'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	83.0	166	1.7071	{'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	84.0	168	1.6877	{'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	85.0	170	1.6891	{'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	86.0	172	1.8116	{'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	87.0	174	1.7178	{'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	88.0	176	1.8128	{'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	89.0	178	2.0186	{'0': {'precision': 0.6875, 'recall': 0.5, 'f1-score': 0.5789473684210527, 'support': 22.0}, '1': {'precision': 0.7105263157894737, 'recall': 0.84375, 'f1-score': 0.7714285714285715, 'support': 32.0}, 'accuracy': 0.7037037037037037, 'macro avg': {'precision': 0.6990131578947368, 'recall': 0.671875, 'f1-score': 0.675187969924812, 'support': 54.0}, 'weighted avg': {'precision': 0.7011452241715399, 'recall': 0.7037037037037037, 'f1-score': 0.6930103035366194, 'support': 54.0}}
No log	90.0	180	1.9870	{'0': {'precision': 0.6875, 'recall': 0.5, 'f1-score': 0.5789473684210527, 'support': 22.0}, '1': {'precision': 0.7105263157894737, 'recall': 0.84375, 'f1-score': 0.7714285714285715, 'support': 32.0}, 'accuracy': 0.7037037037037037, 'macro avg': {'precision': 0.6990131578947368, 'recall': 0.671875, 'f1-score': 0.675187969924812, 'support': 54.0}, 'weighted avg': {'precision': 0.7011452241715399, 'recall': 0.7037037037037037, 'f1-score': 0.6930103035366194, 'support': 54.0}}
No log	91.0	182	1.8450	{'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	92.0	184	1.7591	{'0': {'precision': 0.6, 'recall': 0.5454545454545454, 'f1-score': 0.5714285714285714, 'support': 22.0}, '1': {'precision': 0.7058823529411765, 'recall': 0.75, 'f1-score': 0.7272727272727273, 'support': 32.0}, 'accuracy': 0.6666666666666666, 'macro avg': {'precision': 0.6529411764705882, 'recall': 0.6477272727272727, 'f1-score': 0.6493506493506493, 'support': 54.0}, 'weighted avg': {'precision': 0.6627450980392157, 'recall': 0.6666666666666666, 'f1-score': 0.6637806637806638, 'support': 54.0}}
No log	93.0	186	1.7957	{'0': {'precision': 0.6153846153846154, 'recall': 0.7272727272727273, 'f1-score': 0.6666666666666666, 'support': 22.0}, '1': {'precision': 0.7857142857142857, 'recall': 0.6875, 'f1-score': 0.7333333333333333, 'support': 32.0}, 'accuracy': 0.7037037037037037, 'macro avg': {'precision': 0.7005494505494505, 'recall': 0.7073863636363636, 'f1-score': 0.7, 'support': 54.0}, 'weighted avg': {'precision': 0.7163207163207164, 'recall': 0.7037037037037037, 'f1-score': 0.7061728395061728, 'support': 54.0}}
No log	94.0	188	1.8606	{'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	95.0	190	1.9176	{'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	96.0	192	1.9135	{'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	97.0	194	1.9007	{'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	98.0	196	1.8694	{'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	99.0	198	1.8401	{'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	100.0	200	1.8217	{'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	101.0	202	1.8243	{'0': {'precision': 0.6521739130434783, 'recall': 0.6818181818181818, 'f1-score': 0.6666666666666666, 'support': 22.0}, '1': {'precision': 0.7741935483870968, 'recall': 0.75, 'f1-score': 0.7619047619047619, 'support': 32.0}, 'accuracy': 0.7222222222222222, 'macro avg': {'precision': 0.7131837307152875, 'recall': 0.7159090909090908, 'f1-score': 0.7142857142857142, 'support': 54.0}, 'weighted avg': {'precision': 0.724481845098956, 'recall': 0.7222222222222222, 'f1-score': 0.7231040564373897, 'support': 54.0}}
No log	102.0	204	1.8071	{'0': {'precision': 0.6363636363636364, 'recall': 0.6363636363636364, 'f1-score': 0.6363636363636364, 'support': 22.0}, '1': {'precision': 0.75, 'recall': 0.75, 'f1-score': 0.75, 'support': 32.0}, 'accuracy': 0.7037037037037037, 'macro avg': {'precision': 0.6931818181818181, 'recall': 0.6931818181818181, 'f1-score': 0.6931818181818181, 'support': 54.0}, 'weighted avg': {'precision': 0.7037037037037037, 'recall': 0.7037037037037037, 'f1-score': 0.7037037037037037, 'support': 54.0}}
No log	103.0	206	1.8314	{'0': {'precision': 0.6, 'recall': 0.5454545454545454, 'f1-score': 0.5714285714285714, 'support': 22.0}, '1': {'precision': 0.7058823529411765, 'recall': 0.75, 'f1-score': 0.7272727272727273, 'support': 32.0}, 'accuracy': 0.6666666666666666, 'macro avg': {'precision': 0.6529411764705882, 'recall': 0.6477272727272727, 'f1-score': 0.6493506493506493, 'support': 54.0}, 'weighted avg': {'precision': 0.6627450980392157, 'recall': 0.6666666666666666, 'f1-score': 0.6637806637806638, 'support': 54.0}}
No log	104.0	208	1.8233	{'0': {'precision': 0.65, 'recall': 0.5909090909090909, 'f1-score': 0.6190476190476191, 'support': 22.0}, '1': {'precision': 0.7352941176470589, 'recall': 0.78125, 'f1-score': 0.7575757575757576, 'support': 32.0}, 'accuracy': 0.7037037037037037, 'macro avg': {'precision': 0.6926470588235294, 'recall': 0.6860795454545454, 'f1-score': 0.6883116883116883, 'support': 54.0}, 'weighted avg': {'precision': 0.7005446623093682, 'recall': 0.7037037037037037, 'f1-score': 0.7011383678050345, 'support': 54.0}}
No log	105.0	210	1.8241	{'0': {'precision': 0.631578947368421, 'recall': 0.5454545454545454, 'f1-score': 0.5853658536585366, 'support': 22.0}, '1': {'precision': 0.7142857142857143, 'recall': 0.78125, 'f1-score': 0.746268656716418, 'support': 32.0}, 'accuracy': 0.6851851851851852, 'macro avg': {'precision': 0.6729323308270676, 'recall': 0.6633522727272727, 'f1-score': 0.6658172551874773, 'support': 54.0}, 'weighted avg': {'precision': 0.6805903648008912, 'recall': 0.6851851851851852, 'f1-score': 0.6807156628780219, 'support': 54.0}}
No log	106.0	212	1.8202	{'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	107.0	214	1.8317	{'0': {'precision': 0.631578947368421, 'recall': 0.5454545454545454, 'f1-score': 0.5853658536585366, 'support': 22.0}, '1': {'precision': 0.7142857142857143, 'recall': 0.78125, 'f1-score': 0.746268656716418, 'support': 32.0}, 'accuracy': 0.6851851851851852, 'macro avg': {'precision': 0.6729323308270676, 'recall': 0.6633522727272727, 'f1-score': 0.6658172551874773, 'support': 54.0}, 'weighted avg': {'precision': 0.6805903648008912, 'recall': 0.6851851851851852, 'f1-score': 0.6807156628780219, 'support': 54.0}}
No log	108.0	216	1.8290	{'0': {'precision': 0.65, 'recall': 0.5909090909090909, 'f1-score': 0.6190476190476191, 'support': 22.0}, '1': {'precision': 0.7352941176470589, 'recall': 0.78125, 'f1-score': 0.7575757575757576, 'support': 32.0}, 'accuracy': 0.7037037037037037, 'macro avg': {'precision': 0.6926470588235294, 'recall': 0.6860795454545454, 'f1-score': 0.6883116883116883, 'support': 54.0}, 'weighted avg': {'precision': 0.7005446623093682, 'recall': 0.7037037037037037, 'f1-score': 0.7011383678050345, 'support': 54.0}}
No log	109.0	218	1.8441	{'0': {'precision': 0.631578947368421, 'recall': 0.5454545454545454, 'f1-score': 0.5853658536585366, 'support': 22.0}, '1': {'precision': 0.7142857142857143, 'recall': 0.78125, 'f1-score': 0.746268656716418, 'support': 32.0}, 'accuracy': 0.6851851851851852, 'macro avg': {'precision': 0.6729323308270676, 'recall': 0.6633522727272727, 'f1-score': 0.6658172551874773, 'support': 54.0}, 'weighted avg': {'precision': 0.6805903648008912, 'recall': 0.6851851851851852, 'f1-score': 0.6807156628780219, 'support': 54.0}}
No log	110.0	220	1.8544	{'0': {'precision': 0.631578947368421, 'recall': 0.5454545454545454, 'f1-score': 0.5853658536585366, 'support': 22.0}, '1': {'precision': 0.7142857142857143, 'recall': 0.78125, 'f1-score': 0.746268656716418, 'support': 32.0}, 'accuracy': 0.6851851851851852, 'macro avg': {'precision': 0.6729323308270676, 'recall': 0.6633522727272727, 'f1-score': 0.6658172551874773, 'support': 54.0}, 'weighted avg': {'precision': 0.6805903648008912, 'recall': 0.6851851851851852, 'f1-score': 0.6807156628780219, 'support': 54.0}}
No log	111.0	222	1.8491	{'0': {'precision': 0.631578947368421, 'recall': 0.5454545454545454, 'f1-score': 0.5853658536585366, 'support': 22.0}, '1': {'precision': 0.7142857142857143, 'recall': 0.78125, 'f1-score': 0.746268656716418, 'support': 32.0}, 'accuracy': 0.6851851851851852, 'macro avg': {'precision': 0.6729323308270676, 'recall': 0.6633522727272727, 'f1-score': 0.6658172551874773, 'support': 54.0}, 'weighted avg': {'precision': 0.6805903648008912, 'recall': 0.6851851851851852, 'f1-score': 0.6807156628780219, 'support': 54.0}}
No log	112.0	224	1.8381	{'0': {'precision': 0.65, 'recall': 0.5909090909090909, 'f1-score': 0.6190476190476191, 'support': 22.0}, '1': {'precision': 0.7352941176470589, 'recall': 0.78125, 'f1-score': 0.7575757575757576, 'support': 32.0}, 'accuracy': 0.7037037037037037, 'macro avg': {'precision': 0.6926470588235294, 'recall': 0.6860795454545454, 'f1-score': 0.6883116883116883, 'support': 54.0}, 'weighted avg': {'precision': 0.7005446623093682, 'recall': 0.7037037037037037, 'f1-score': 0.7011383678050345, 'support': 54.0}}
No log	113.0	226	1.8562	{'0': {'precision': 0.631578947368421, 'recall': 0.5454545454545454, 'f1-score': 0.5853658536585366, 'support': 22.0}, '1': {'precision': 0.7142857142857143, 'recall': 0.78125, 'f1-score': 0.746268656716418, 'support': 32.0}, 'accuracy': 0.6851851851851852, 'macro avg': {'precision': 0.6729323308270676, 'recall': 0.6633522727272727, 'f1-score': 0.6658172551874773, 'support': 54.0}, 'weighted avg': {'precision': 0.6805903648008912, 'recall': 0.6851851851851852, 'f1-score': 0.6807156628780219, 'support': 54.0}}
No log	114.0	228	1.8425	{'0': {'precision': 0.631578947368421, 'recall': 0.5454545454545454, 'f1-score': 0.5853658536585366, 'support': 22.0}, '1': {'precision': 0.7142857142857143, 'recall': 0.78125, 'f1-score': 0.746268656716418, 'support': 32.0}, 'accuracy': 0.6851851851851852, 'macro avg': {'precision': 0.6729323308270676, 'recall': 0.6633522727272727, 'f1-score': 0.6658172551874773, 'support': 54.0}, 'weighted avg': {'precision': 0.6805903648008912, 'recall': 0.6851851851851852, 'f1-score': 0.6807156628780219, 'support': 54.0}}
No log	115.0	230	1.8307	{'0': {'precision': 0.6190476190476191, 'recall': 0.5909090909090909, 'f1-score': 0.6046511627906976, 'support': 22.0}, '1': {'precision': 0.7272727272727273, 'recall': 0.75, 'f1-score': 0.7384615384615385, 'support': 32.0}, 'accuracy': 0.6851851851851852, 'macro avg': {'precision': 0.6731601731601732, 'recall': 0.6704545454545454, 'f1-score': 0.6715563506261181, 'support': 54.0}, 'weighted avg': {'precision': 0.6831810165143499, 'recall': 0.6851851851851852, 'f1-score': 0.6839462002252701, 'support': 54.0}}
No log	116.0	232	1.8397	{'0': {'precision': 0.5909090909090909, 'recall': 0.5909090909090909, 'f1-score': 0.5909090909090909, 'support': 22.0}, '1': {'precision': 0.71875, 'recall': 0.71875, 'f1-score': 0.71875, 'support': 32.0}, 'accuracy': 0.6666666666666666, 'macro avg': {'precision': 0.6548295454545454, 'recall': 0.6548295454545454, 'f1-score': 0.6548295454545454, 'support': 54.0}, 'weighted avg': {'precision': 0.6666666666666666, 'recall': 0.6666666666666666, 'f1-score': 0.6666666666666666, 'support': 54.0}}
No log	117.0	234	1.8385	{'0': {'precision': 0.65, 'recall': 0.5909090909090909, 'f1-score': 0.6190476190476191, 'support': 22.0}, '1': {'precision': 0.7352941176470589, 'recall': 0.78125, 'f1-score': 0.7575757575757576, 'support': 32.0}, 'accuracy': 0.7037037037037037, 'macro avg': {'precision': 0.6926470588235294, 'recall': 0.6860795454545454, 'f1-score': 0.6883116883116883, 'support': 54.0}, 'weighted avg': {'precision': 0.7005446623093682, 'recall': 0.7037037037037037, 'f1-score': 0.7011383678050345, 'support': 54.0}}
No log	118.0	236	1.8344	{'0': {'precision': 0.6190476190476191, 'recall': 0.5909090909090909, 'f1-score': 0.6046511627906976, 'support': 22.0}, '1': {'precision': 0.7272727272727273, 'recall': 0.75, 'f1-score': 0.7384615384615385, 'support': 32.0}, 'accuracy': 0.6851851851851852, 'macro avg': {'precision': 0.6731601731601732, 'recall': 0.6704545454545454, 'f1-score': 0.6715563506261181, 'support': 54.0}, 'weighted avg': {'precision': 0.6831810165143499, 'recall': 0.6851851851851852, 'f1-score': 0.6839462002252701, 'support': 54.0}}
No log	119.0	238	1.8443	{'0': {'precision': 0.6, 'recall': 0.5454545454545454, 'f1-score': 0.5714285714285714, 'support': 22.0}, '1': {'precision': 0.7058823529411765, 'recall': 0.75, 'f1-score': 0.7272727272727273, 'support': 32.0}, 'accuracy': 0.6666666666666666, 'macro avg': {'precision': 0.6529411764705882, 'recall': 0.6477272727272727, 'f1-score': 0.6493506493506493, 'support': 54.0}, 'weighted avg': {'precision': 0.6627450980392157, 'recall': 0.6666666666666666, 'f1-score': 0.6637806637806638, 'support': 54.0}}
No log	120.0	240	1.8417	{'0': {'precision': 0.6190476190476191, 'recall': 0.5909090909090909, 'f1-score': 0.6046511627906976, 'support': 22.0}, '1': {'precision': 0.7272727272727273, 'recall': 0.75, 'f1-score': 0.7384615384615385, 'support': 32.0}, 'accuracy': 0.6851851851851852, 'macro avg': {'precision': 0.6731601731601732, 'recall': 0.6704545454545454, 'f1-score': 0.6715563506261181, 'support': 54.0}, 'weighted avg': {'precision': 0.6831810165143499, 'recall': 0.6851851851851852, 'f1-score': 0.6839462002252701, 'support': 54.0}}
No log	121.0	242	1.8562	{'0': {'precision': 0.5714285714285714, 'recall': 0.5454545454545454, 'f1-score': 0.5581395348837209, 'support': 22.0}, '1': {'precision': 0.696969696969697, 'recall': 0.71875, 'f1-score': 0.7076923076923077, 'support': 32.0}, 'accuracy': 0.6481481481481481, 'macro avg': {'precision': 0.6341991341991342, 'recall': 0.6321022727272727, 'f1-score': 0.6329159212880143, 'support': 54.0}, 'weighted avg': {'precision': 0.6458233124899792, 'recall': 0.6481481481481481, 'f1-score': 0.6467634002517723, 'support': 54.0}}
No log	122.0	244	1.8369	{'0': {'precision': 0.5909090909090909, 'recall': 0.5909090909090909, 'f1-score': 0.5909090909090909, 'support': 22.0}, '1': {'precision': 0.71875, 'recall': 0.71875, 'f1-score': 0.71875, 'support': 32.0}, 'accuracy': 0.6666666666666666, 'macro avg': {'precision': 0.6548295454545454, 'recall': 0.6548295454545454, 'f1-score': 0.6548295454545454, 'support': 54.0}, 'weighted avg': {'precision': 0.6666666666666666, 'recall': 0.6666666666666666, 'f1-score': 0.6666666666666666, 'support': 54.0}}
No log	123.0	246	1.8531	{'0': {'precision': 0.5714285714285714, 'recall': 0.5454545454545454, 'f1-score': 0.5581395348837209, 'support': 22.0}, '1': {'precision': 0.696969696969697, 'recall': 0.71875, 'f1-score': 0.7076923076923077, 'support': 32.0}, 'accuracy': 0.6481481481481481, 'macro avg': {'precision': 0.6341991341991342, 'recall': 0.6321022727272727, 'f1-score': 0.6329159212880143, 'support': 54.0}, 'weighted avg': {'precision': 0.6458233124899792, 'recall': 0.6481481481481481, 'f1-score': 0.6467634002517723, 'support': 54.0}}
No log	124.0	248	1.8423	{'0': {'precision': 0.6190476190476191, 'recall': 0.5909090909090909, 'f1-score': 0.6046511627906976, 'support': 22.0}, '1': {'precision': 0.7272727272727273, 'recall': 0.75, 'f1-score': 0.7384615384615385, 'support': 32.0}, 'accuracy': 0.6851851851851852, 'macro avg': {'precision': 0.6731601731601732, 'recall': 0.6704545454545454, 'f1-score': 0.6715563506261181, 'support': 54.0}, 'weighted avg': {'precision': 0.6831810165143499, 'recall': 0.6851851851851852, 'f1-score': 0.6839462002252701, 'support': 54.0}}
No log	125.0	250	1.8553	{'0': {'precision': 0.5909090909090909, 'recall': 0.5909090909090909, 'f1-score': 0.5909090909090909, 'support': 22.0}, '1': {'precision': 0.71875, 'recall': 0.71875, 'f1-score': 0.71875, 'support': 32.0}, 'accuracy': 0.6666666666666666, 'macro avg': {'precision': 0.6548295454545454, 'recall': 0.6548295454545454, 'f1-score': 0.6548295454545454, 'support': 54.0}, 'weighted avg': {'precision': 0.6666666666666666, 'recall': 0.6666666666666666, 'f1-score': 0.6666666666666666, 'support': 54.0}}
No log	126.0	252	1.8325	{'0': {'precision': 0.5909090909090909, 'recall': 0.5909090909090909, 'f1-score': 0.5909090909090909, 'support': 22.0}, '1': {'precision': 0.71875, 'recall': 0.71875, 'f1-score': 0.71875, 'support': 32.0}, 'accuracy': 0.6666666666666666, 'macro avg': {'precision': 0.6548295454545454, 'recall': 0.6548295454545454, 'f1-score': 0.6548295454545454, 'support': 54.0}, 'weighted avg': {'precision': 0.6666666666666666, 'recall': 0.6666666666666666, 'f1-score': 0.6666666666666666, 'support': 54.0}}
No log	127.0	254	1.8464	{'0': {'precision': 0.5909090909090909, 'recall': 0.5909090909090909, 'f1-score': 0.5909090909090909, 'support': 22.0}, '1': {'precision': 0.71875, 'recall': 0.71875, 'f1-score': 0.71875, 'support': 32.0}, 'accuracy': 0.6666666666666666, 'macro avg': {'precision': 0.6548295454545454, 'recall': 0.6548295454545454, 'f1-score': 0.6548295454545454, 'support': 54.0}, 'weighted avg': {'precision': 0.6666666666666666, 'recall': 0.6666666666666666, 'f1-score': 0.6666666666666666, 'support': 54.0}}
No log	128.0	256	1.8315	{'0': {'precision': 0.6363636363636364, 'recall': 0.6363636363636364, 'f1-score': 0.6363636363636364, 'support': 22.0}, '1': {'precision': 0.75, 'recall': 0.75, 'f1-score': 0.75, 'support': 32.0}, 'accuracy': 0.7037037037037037, 'macro avg': {'precision': 0.6931818181818181, 'recall': 0.6931818181818181, 'f1-score': 0.6931818181818181, 'support': 54.0}, 'weighted avg': {'precision': 0.7037037037037037, 'recall': 0.7037037037037037, 'f1-score': 0.7037037037037037, 'support': 54.0}}
No log	129.0	258	1.8275	{'0': {'precision': 0.5909090909090909, 'recall': 0.5909090909090909, 'f1-score': 0.5909090909090909, 'support': 22.0}, '1': {'precision': 0.71875, 'recall': 0.71875, 'f1-score': 0.71875, 'support': 32.0}, 'accuracy': 0.6666666666666666, 'macro avg': {'precision': 0.6548295454545454, 'recall': 0.6548295454545454, 'f1-score': 0.6548295454545454, 'support': 54.0}, 'weighted avg': {'precision': 0.6666666666666666, 'recall': 0.6666666666666666, 'f1-score': 0.6666666666666666, 'support': 54.0}}
No log	130.0	260	1.8332	{'0': {'precision': 0.6190476190476191, 'recall': 0.5909090909090909, 'f1-score': 0.6046511627906976, 'support': 22.0}, '1': {'precision': 0.7272727272727273, 'recall': 0.75, 'f1-score': 0.7384615384615385, 'support': 32.0}, 'accuracy': 0.6851851851851852, 'macro avg': {'precision': 0.6731601731601732, 'recall': 0.6704545454545454, 'f1-score': 0.6715563506261181, 'support': 54.0}, 'weighted avg': {'precision': 0.6831810165143499, 'recall': 0.6851851851851852, 'f1-score': 0.6839462002252701, 'support': 54.0}}
No log	131.0	262	1.8385	{'0': {'precision': 0.5909090909090909, 'recall': 0.5909090909090909, 'f1-score': 0.5909090909090909, 'support': 22.0}, '1': {'precision': 0.71875, 'recall': 0.71875, 'f1-score': 0.71875, 'support': 32.0}, 'accuracy': 0.6666666666666666, 'macro avg': {'precision': 0.6548295454545454, 'recall': 0.6548295454545454, 'f1-score': 0.6548295454545454, 'support': 54.0}, 'weighted avg': {'precision': 0.6666666666666666, 'recall': 0.6666666666666666, 'f1-score': 0.6666666666666666, 'support': 54.0}}
No log	132.0	264	1.8557	{'0': {'precision': 0.5909090909090909, 'recall': 0.5909090909090909, 'f1-score': 0.5909090909090909, 'support': 22.0}, '1': {'precision': 0.71875, 'recall': 0.71875, 'f1-score': 0.71875, 'support': 32.0}, 'accuracy': 0.6666666666666666, 'macro avg': {'precision': 0.6548295454545454, 'recall': 0.6548295454545454, 'f1-score': 0.6548295454545454, 'support': 54.0}, 'weighted avg': {'precision': 0.6666666666666666, 'recall': 0.6666666666666666, 'f1-score': 0.6666666666666666, 'support': 54.0}}
No log	133.0	266	1.8318	{'0': {'precision': 0.6190476190476191, 'recall': 0.5909090909090909, 'f1-score': 0.6046511627906976, 'support': 22.0}, '1': {'precision': 0.7272727272727273, 'recall': 0.75, 'f1-score': 0.7384615384615385, 'support': 32.0}, 'accuracy': 0.6851851851851852, 'macro avg': {'precision': 0.6731601731601732, 'recall': 0.6704545454545454, 'f1-score': 0.6715563506261181, 'support': 54.0}, 'weighted avg': {'precision': 0.6831810165143499, 'recall': 0.6851851851851852, 'f1-score': 0.6839462002252701, 'support': 54.0}}
No log	134.0	268	1.8446	{'0': {'precision': 0.5909090909090909, 'recall': 0.5909090909090909, 'f1-score': 0.5909090909090909, 'support': 22.0}, '1': {'precision': 0.71875, 'recall': 0.71875, 'f1-score': 0.71875, 'support': 32.0}, 'accuracy': 0.6666666666666666, 'macro avg': {'precision': 0.6548295454545454, 'recall': 0.6548295454545454, 'f1-score': 0.6548295454545454, 'support': 54.0}, 'weighted avg': {'precision': 0.6666666666666666, 'recall': 0.6666666666666666, 'f1-score': 0.6666666666666666, 'support': 54.0}}
No log	135.0	270	1.8302	{'0': {'precision': 0.6190476190476191, 'recall': 0.5909090909090909, 'f1-score': 0.6046511627906976, 'support': 22.0}, '1': {'precision': 0.7272727272727273, 'recall': 0.75, 'f1-score': 0.7384615384615385, 'support': 32.0}, 'accuracy': 0.6851851851851852, 'macro avg': {'precision': 0.6731601731601732, 'recall': 0.6704545454545454, 'f1-score': 0.6715563506261181, 'support': 54.0}, 'weighted avg': {'precision': 0.6831810165143499, 'recall': 0.6851851851851852, 'f1-score': 0.6839462002252701, 'support': 54.0}}
No log	136.0	272	1.8277	{'0': {'precision': 0.6190476190476191, 'recall': 0.5909090909090909, 'f1-score': 0.6046511627906976, 'support': 22.0}, '1': {'precision': 0.7272727272727273, 'recall': 0.75, 'f1-score': 0.7384615384615385, 'support': 32.0}, 'accuracy': 0.6851851851851852, 'macro avg': {'precision': 0.6731601731601732, 'recall': 0.6704545454545454, 'f1-score': 0.6715563506261181, 'support': 54.0}, 'weighted avg': {'precision': 0.6831810165143499, 'recall': 0.6851851851851852, 'f1-score': 0.6839462002252701, 'support': 54.0}}
No log	137.0	274	1.8392	{'0': {'precision': 0.5909090909090909, 'recall': 0.5909090909090909, 'f1-score': 0.5909090909090909, 'support': 22.0}, '1': {'precision': 0.71875, 'recall': 0.71875, 'f1-score': 0.71875, 'support': 32.0}, 'accuracy': 0.6666666666666666, 'macro avg': {'precision': 0.6548295454545454, 'recall': 0.6548295454545454, 'f1-score': 0.6548295454545454, 'support': 54.0}, 'weighted avg': {'precision': 0.6666666666666666, 'recall': 0.6666666666666666, 'f1-score': 0.6666666666666666, 'support': 54.0}}
No log	138.0	276	1.8433	{'0': {'precision': 0.6190476190476191, 'recall': 0.5909090909090909, 'f1-score': 0.6046511627906976, 'support': 22.0}, '1': {'precision': 0.7272727272727273, 'recall': 0.75, 'f1-score': 0.7384615384615385, 'support': 32.0}, 'accuracy': 0.6851851851851852, 'macro avg': {'precision': 0.6731601731601732, 'recall': 0.6704545454545454, 'f1-score': 0.6715563506261181, 'support': 54.0}, 'weighted avg': {'precision': 0.6831810165143499, 'recall': 0.6851851851851852, 'f1-score': 0.6839462002252701, 'support': 54.0}}
No log	139.0	278	1.8456	{'0': {'precision': 0.6086956521739131, 'recall': 0.6363636363636364, 'f1-score': 0.6222222222222222, 'support': 22.0}, '1': {'precision': 0.7419354838709677, 'recall': 0.71875, 'f1-score': 0.7301587301587301, 'support': 32.0}, 'accuracy': 0.6851851851851852, 'macro avg': {'precision': 0.6753155680224404, 'recall': 0.6775568181818181, 'f1-score': 0.6761904761904762, 'support': 54.0}, 'weighted avg': {'precision': 0.6876525894758714, 'recall': 0.6851851851851852, 'f1-score': 0.6861845972957084, 'support': 54.0}}
No log	140.0	280	1.8351	{'0': {'precision': 0.5909090909090909, 'recall': 0.5909090909090909, 'f1-score': 0.5909090909090909, 'support': 22.0}, '1': {'precision': 0.71875, 'recall': 0.71875, 'f1-score': 0.71875, 'support': 32.0}, 'accuracy': 0.6666666666666666, 'macro avg': {'precision': 0.6548295454545454, 'recall': 0.6548295454545454, 'f1-score': 0.6548295454545454, 'support': 54.0}, 'weighted avg': {'precision': 0.6666666666666666, 'recall': 0.6666666666666666, 'f1-score': 0.6666666666666666, 'support': 54.0}}
No log	141.0	282	1.8503	{'0': {'precision': 0.6, 'recall': 0.5454545454545454, 'f1-score': 0.5714285714285714, 'support': 22.0}, '1': {'precision': 0.7058823529411765, 'recall': 0.75, 'f1-score': 0.7272727272727273, 'support': 32.0}, 'accuracy': 0.6666666666666666, 'macro avg': {'precision': 0.6529411764705882, 'recall': 0.6477272727272727, 'f1-score': 0.6493506493506493, 'support': 54.0}, 'weighted avg': {'precision': 0.6627450980392157, 'recall': 0.6666666666666666, 'f1-score': 0.6637806637806638, 'support': 54.0}}
No log	142.0	284	1.8450	{'0': {'precision': 0.6190476190476191, 'recall': 0.5909090909090909, 'f1-score': 0.6046511627906976, 'support': 22.0}, '1': {'precision': 0.7272727272727273, 'recall': 0.75, 'f1-score': 0.7384615384615385, 'support': 32.0}, 'accuracy': 0.6851851851851852, 'macro avg': {'precision': 0.6731601731601732, 'recall': 0.6704545454545454, 'f1-score': 0.6715563506261181, 'support': 54.0}, 'weighted avg': {'precision': 0.6831810165143499, 'recall': 0.6851851851851852, 'f1-score': 0.6839462002252701, 'support': 54.0}}
No log	143.0	286	1.8336	{'0': {'precision': 0.5909090909090909, 'recall': 0.5909090909090909, 'f1-score': 0.5909090909090909, 'support': 22.0}, '1': {'precision': 0.71875, 'recall': 0.71875, 'f1-score': 0.71875, 'support': 32.0}, 'accuracy': 0.6666666666666666, 'macro avg': {'precision': 0.6548295454545454, 'recall': 0.6548295454545454, 'f1-score': 0.6548295454545454, 'support': 54.0}, 'weighted avg': {'precision': 0.6666666666666666, 'recall': 0.6666666666666666, 'f1-score': 0.6666666666666666, 'support': 54.0}}
No log	144.0	288	1.8347	{'0': {'precision': 0.6190476190476191, 'recall': 0.5909090909090909, 'f1-score': 0.6046511627906976, 'support': 22.0}, '1': {'precision': 0.7272727272727273, 'recall': 0.75, 'f1-score': 0.7384615384615385, 'support': 32.0}, 'accuracy': 0.6851851851851852, 'macro avg': {'precision': 0.6731601731601732, 'recall': 0.6704545454545454, 'f1-score': 0.6715563506261181, 'support': 54.0}, 'weighted avg': {'precision': 0.6831810165143499, 'recall': 0.6851851851851852, 'f1-score': 0.6839462002252701, 'support': 54.0}}
No log	145.0	290	1.8489	{'0': {'precision': 0.6190476190476191, 'recall': 0.5909090909090909, 'f1-score': 0.6046511627906976, 'support': 22.0}, '1': {'precision': 0.7272727272727273, 'recall': 0.75, 'f1-score': 0.7384615384615385, 'support': 32.0}, 'accuracy': 0.6851851851851852, 'macro avg': {'precision': 0.6731601731601732, 'recall': 0.6704545454545454, 'f1-score': 0.6715563506261181, 'support': 54.0}, 'weighted avg': {'precision': 0.6831810165143499, 'recall': 0.6851851851851852, 'f1-score': 0.6839462002252701, 'support': 54.0}}
No log	146.0	292	1.8469	{'0': {'precision': 0.6190476190476191, 'recall': 0.5909090909090909, 'f1-score': 0.6046511627906976, 'support': 22.0}, '1': {'precision': 0.7272727272727273, 'recall': 0.75, 'f1-score': 0.7384615384615385, 'support': 32.0}, 'accuracy': 0.6851851851851852, 'macro avg': {'precision': 0.6731601731601732, 'recall': 0.6704545454545454, 'f1-score': 0.6715563506261181, 'support': 54.0}, 'weighted avg': {'precision': 0.6831810165143499, 'recall': 0.6851851851851852, 'f1-score': 0.6839462002252701, 'support': 54.0}}
No log	147.0	294	1.8301	{'0': {'precision': 0.6190476190476191, 'recall': 0.5909090909090909, 'f1-score': 0.6046511627906976, 'support': 22.0}, '1': {'precision': 0.7272727272727273, 'recall': 0.75, 'f1-score': 0.7384615384615385, 'support': 32.0}, 'accuracy': 0.6851851851851852, 'macro avg': {'precision': 0.6731601731601732, 'recall': 0.6704545454545454, 'f1-score': 0.6715563506261181, 'support': 54.0}, 'weighted avg': {'precision': 0.6831810165143499, 'recall': 0.6851851851851852, 'f1-score': 0.6839462002252701, 'support': 54.0}}
No log	148.0	296	1.8529	{'0': {'precision': 0.6, 'recall': 0.5454545454545454, 'f1-score': 0.5714285714285714, 'support': 22.0}, '1': {'precision': 0.7058823529411765, 'recall': 0.75, 'f1-score': 0.7272727272727273, 'support': 32.0}, 'accuracy': 0.6666666666666666, 'macro avg': {'precision': 0.6529411764705882, 'recall': 0.6477272727272727, 'f1-score': 0.6493506493506493, 'support': 54.0}, 'weighted avg': {'precision': 0.6627450980392157, 'recall': 0.6666666666666666, 'f1-score': 0.6637806637806638, 'support': 54.0}}
No log	149.0	298	1.8336	{'0': {'precision': 0.6190476190476191, 'recall': 0.5909090909090909, 'f1-score': 0.6046511627906976, 'support': 22.0}, '1': {'precision': 0.7272727272727273, 'recall': 0.75, 'f1-score': 0.7384615384615385, 'support': 32.0}, 'accuracy': 0.6851851851851852, 'macro avg': {'precision': 0.6731601731601732, 'recall': 0.6704545454545454, 'f1-score': 0.6715563506261181, 'support': 54.0}, 'weighted avg': {'precision': 0.6831810165143499, 'recall': 0.6851851851851852, 'f1-score': 0.6839462002252701, 'support': 54.0}}
No log	150.0	300	1.8365	{'0': {'precision': 0.6190476190476191, 'recall': 0.5909090909090909, 'f1-score': 0.6046511627906976, 'support': 22.0}, '1': {'precision': 0.7272727272727273, 'recall': 0.75, 'f1-score': 0.7384615384615385, 'support': 32.0}, 'accuracy': 0.6851851851851852, 'macro avg': {'precision': 0.6731601731601732, 'recall': 0.6704545454545454, 'f1-score': 0.6715563506261181, 'support': 54.0}, 'weighted avg': {'precision': 0.6831810165143499, 'recall': 0.6851851851851852, 'f1-score': 0.6839462002252701, 'support': 54.0}}
No log	151.0	302	1.8388	{'0': {'precision': 0.6190476190476191, 'recall': 0.5909090909090909, 'f1-score': 0.6046511627906976, 'support': 22.0}, '1': {'precision': 0.7272727272727273, 'recall': 0.75, 'f1-score': 0.7384615384615385, 'support': 32.0}, 'accuracy': 0.6851851851851852, 'macro avg': {'precision': 0.6731601731601732, 'recall': 0.6704545454545454, 'f1-score': 0.6715563506261181, 'support': 54.0}, 'weighted avg': {'precision': 0.6831810165143499, 'recall': 0.6851851851851852, 'f1-score': 0.6839462002252701, 'support': 54.0}}
No log	152.0	304	1.8289	{'0': {'precision': 0.5909090909090909, 'recall': 0.5909090909090909, 'f1-score': 0.5909090909090909, 'support': 22.0}, '1': {'precision': 0.71875, 'recall': 0.71875, 'f1-score': 0.71875, 'support': 32.0}, 'accuracy': 0.6666666666666666, 'macro avg': {'precision': 0.6548295454545454, 'recall': 0.6548295454545454, 'f1-score': 0.6548295454545454, 'support': 54.0}, 'weighted avg': {'precision': 0.6666666666666666, 'recall': 0.6666666666666666, 'f1-score': 0.6666666666666666, 'support': 54.0}}
No log	153.0	306	1.8479	{'0': {'precision': 0.5909090909090909, 'recall': 0.5909090909090909, 'f1-score': 0.5909090909090909, 'support': 22.0}, '1': {'precision': 0.71875, 'recall': 0.71875, 'f1-score': 0.71875, 'support': 32.0}, 'accuracy': 0.6666666666666666, 'macro avg': {'precision': 0.6548295454545454, 'recall': 0.6548295454545454, 'f1-score': 0.6548295454545454, 'support': 54.0}, 'weighted avg': {'precision': 0.6666666666666666, 'recall': 0.6666666666666666, 'f1-score': 0.6666666666666666, 'support': 54.0}}
No log	154.0	308	1.8517	{'0': {'precision': 0.6190476190476191, 'recall': 0.5909090909090909, 'f1-score': 0.6046511627906976, 'support': 22.0}, '1': {'precision': 0.7272727272727273, 'recall': 0.75, 'f1-score': 0.7384615384615385, 'support': 32.0}, 'accuracy': 0.6851851851851852, 'macro avg': {'precision': 0.6731601731601732, 'recall': 0.6704545454545454, 'f1-score': 0.6715563506261181, 'support': 54.0}, 'weighted avg': {'precision': 0.6831810165143499, 'recall': 0.6851851851851852, 'f1-score': 0.6839462002252701, 'support': 54.0}}
No log	155.0	310	1.8554	{'0': {'precision': 0.5909090909090909, 'recall': 0.5909090909090909, 'f1-score': 0.5909090909090909, 'support': 22.0}, '1': {'precision': 0.71875, 'recall': 0.71875, 'f1-score': 0.71875, 'support': 32.0}, 'accuracy': 0.6666666666666666, 'macro avg': {'precision': 0.6548295454545454, 'recall': 0.6548295454545454, 'f1-score': 0.6548295454545454, 'support': 54.0}, 'weighted avg': {'precision': 0.6666666666666666, 'recall': 0.6666666666666666, 'f1-score': 0.6666666666666666, 'support': 54.0}}
No log	156.0	312	1.8439	{'0': {'precision': 0.5909090909090909, 'recall': 0.5909090909090909, 'f1-score': 0.5909090909090909, 'support': 22.0}, '1': {'precision': 0.71875, 'recall': 0.71875, 'f1-score': 0.71875, 'support': 32.0}, 'accuracy': 0.6666666666666666, 'macro avg': {'precision': 0.6548295454545454, 'recall': 0.6548295454545454, 'f1-score': 0.6548295454545454, 'support': 54.0}, 'weighted avg': {'precision': 0.6666666666666666, 'recall': 0.6666666666666666, 'f1-score': 0.6666666666666666, 'support': 54.0}}
No log	157.0	314	1.8401	{'0': {'precision': 0.5909090909090909, 'recall': 0.5909090909090909, 'f1-score': 0.5909090909090909, 'support': 22.0}, '1': {'precision': 0.71875, 'recall': 0.71875, 'f1-score': 0.71875, 'support': 32.0}, 'accuracy': 0.6666666666666666, 'macro avg': {'precision': 0.6548295454545454, 'recall': 0.6548295454545454, 'f1-score': 0.6548295454545454, 'support': 54.0}, 'weighted avg': {'precision': 0.6666666666666666, 'recall': 0.6666666666666666, 'f1-score': 0.6666666666666666, 'support': 54.0}}
No log	158.0	316	1.8299	{'0': {'precision': 0.6190476190476191, 'recall': 0.5909090909090909, 'f1-score': 0.6046511627906976, 'support': 22.0}, '1': {'precision': 0.7272727272727273, 'recall': 0.75, 'f1-score': 0.7384615384615385, 'support': 32.0}, 'accuracy': 0.6851851851851852, 'macro avg': {'precision': 0.6731601731601732, 'recall': 0.6704545454545454, 'f1-score': 0.6715563506261181, 'support': 54.0}, 'weighted avg': {'precision': 0.6831810165143499, 'recall': 0.6851851851851852, 'f1-score': 0.6839462002252701, 'support': 54.0}}
No log	159.0	318	1.8467	{'0': {'precision': 0.5909090909090909, 'recall': 0.5909090909090909, 'f1-score': 0.5909090909090909, 'support': 22.0}, '1': {'precision': 0.71875, 'recall': 0.71875, 'f1-score': 0.71875, 'support': 32.0}, 'accuracy': 0.6666666666666666, 'macro avg': {'precision': 0.6548295454545454, 'recall': 0.6548295454545454, 'f1-score': 0.6548295454545454, 'support': 54.0}, 'weighted avg': {'precision': 0.6666666666666666, 'recall': 0.6666666666666666, 'f1-score': 0.6666666666666666, 'support': 54.0}}
No log	160.0	320	1.8445	{'0': {'precision': 0.6086956521739131, 'recall': 0.6363636363636364, 'f1-score': 0.6222222222222222, 'support': 22.0}, '1': {'precision': 0.7419354838709677, 'recall': 0.71875, 'f1-score': 0.7301587301587301, 'support': 32.0}, 'accuracy': 0.6851851851851852, 'macro avg': {'precision': 0.6753155680224404, 'recall': 0.6775568181818181, 'f1-score': 0.6761904761904762, 'support': 54.0}, 'weighted avg': {'precision': 0.6876525894758714, 'recall': 0.6851851851851852, 'f1-score': 0.6861845972957084, 'support': 54.0}}
No log	161.0	322	1.8470	{'0': {'precision': 0.6190476190476191, 'recall': 0.5909090909090909, 'f1-score': 0.6046511627906976, 'support': 22.0}, '1': {'precision': 0.7272727272727273, 'recall': 0.75, 'f1-score': 0.7384615384615385, 'support': 32.0}, 'accuracy': 0.6851851851851852, 'macro avg': {'precision': 0.6731601731601732, 'recall': 0.6704545454545454, 'f1-score': 0.6715563506261181, 'support': 54.0}, 'weighted avg': {'precision': 0.6831810165143499, 'recall': 0.6851851851851852, 'f1-score': 0.6839462002252701, 'support': 54.0}}
No log	162.0	324	1.8533	{'0': {'precision': 0.5909090909090909, 'recall': 0.5909090909090909, 'f1-score': 0.5909090909090909, 'support': 22.0}, '1': {'precision': 0.71875, 'recall': 0.71875, 'f1-score': 0.71875, 'support': 32.0}, 'accuracy': 0.6666666666666666, 'macro avg': {'precision': 0.6548295454545454, 'recall': 0.6548295454545454, 'f1-score': 0.6548295454545454, 'support': 54.0}, 'weighted avg': {'precision': 0.6666666666666666, 'recall': 0.6666666666666666, 'f1-score': 0.6666666666666666, 'support': 54.0}}
No log	163.0	326	1.8370	{'0': {'precision': 0.6190476190476191, 'recall': 0.5909090909090909, 'f1-score': 0.6046511627906976, 'support': 22.0}, '1': {'precision': 0.7272727272727273, 'recall': 0.75, 'f1-score': 0.7384615384615385, 'support': 32.0}, 'accuracy': 0.6851851851851852, 'macro avg': {'precision': 0.6731601731601732, 'recall': 0.6704545454545454, 'f1-score': 0.6715563506261181, 'support': 54.0}, 'weighted avg': {'precision': 0.6831810165143499, 'recall': 0.6851851851851852, 'f1-score': 0.6839462002252701, 'support': 54.0}}
No log	164.0	328	1.8510	{'0': {'precision': 0.5714285714285714, 'recall': 0.5454545454545454, 'f1-score': 0.5581395348837209, 'support': 22.0}, '1': {'precision': 0.696969696969697, 'recall': 0.71875, 'f1-score': 0.7076923076923077, 'support': 32.0}, 'accuracy': 0.6481481481481481, 'macro avg': {'precision': 0.6341991341991342, 'recall': 0.6321022727272727, 'f1-score': 0.6329159212880143, 'support': 54.0}, 'weighted avg': {'precision': 0.6458233124899792, 'recall': 0.6481481481481481, 'f1-score': 0.6467634002517723, 'support': 54.0}}
No log	165.0	330	1.8353	{'0': {'precision': 0.6190476190476191, 'recall': 0.5909090909090909, 'f1-score': 0.6046511627906976, 'support': 22.0}, '1': {'precision': 0.7272727272727273, 'recall': 0.75, 'f1-score': 0.7384615384615385, 'support': 32.0}, 'accuracy': 0.6851851851851852, 'macro avg': {'precision': 0.6731601731601732, 'recall': 0.6704545454545454, 'f1-score': 0.6715563506261181, 'support': 54.0}, 'weighted avg': {'precision': 0.6831810165143499, 'recall': 0.6851851851851852, 'f1-score': 0.6839462002252701, 'support': 54.0}}
No log	166.0	332	1.8533	{'0': {'precision': 0.5909090909090909, 'recall': 0.5909090909090909, 'f1-score': 0.5909090909090909, 'support': 22.0}, '1': {'precision': 0.71875, 'recall': 0.71875, 'f1-score': 0.71875, 'support': 32.0}, 'accuracy': 0.6666666666666666, 'macro avg': {'precision': 0.6548295454545454, 'recall': 0.6548295454545454, 'f1-score': 0.6548295454545454, 'support': 54.0}, 'weighted avg': {'precision': 0.6666666666666666, 'recall': 0.6666666666666666, 'f1-score': 0.6666666666666666, 'support': 54.0}}
No log	167.0	334	1.8582	{'0': {'precision': 0.5909090909090909, 'recall': 0.5909090909090909, 'f1-score': 0.5909090909090909, 'support': 22.0}, '1': {'precision': 0.71875, 'recall': 0.71875, 'f1-score': 0.71875, 'support': 32.0}, 'accuracy': 0.6666666666666666, 'macro avg': {'precision': 0.6548295454545454, 'recall': 0.6548295454545454, 'f1-score': 0.6548295454545454, 'support': 54.0}, 'weighted avg': {'precision': 0.6666666666666666, 'recall': 0.6666666666666666, 'f1-score': 0.6666666666666666, 'support': 54.0}}
No log	168.0	336	1.8508	{'0': {'precision': 0.6190476190476191, 'recall': 0.5909090909090909, 'f1-score': 0.6046511627906976, 'support': 22.0}, '1': {'precision': 0.7272727272727273, 'recall': 0.75, 'f1-score': 0.7384615384615385, 'support': 32.0}, 'accuracy': 0.6851851851851852, 'macro avg': {'precision': 0.6731601731601732, 'recall': 0.6704545454545454, 'f1-score': 0.6715563506261181, 'support': 54.0}, 'weighted avg': {'precision': 0.6831810165143499, 'recall': 0.6851851851851852, 'f1-score': 0.6839462002252701, 'support': 54.0}}
No log	169.0	338	1.8416	{'0': {'precision': 0.6190476190476191, 'recall': 0.5909090909090909, 'f1-score': 0.6046511627906976, 'support': 22.0}, '1': {'precision': 0.7272727272727273, 'recall': 0.75, 'f1-score': 0.7384615384615385, 'support': 32.0}, 'accuracy': 0.6851851851851852, 'macro avg': {'precision': 0.6731601731601732, 'recall': 0.6704545454545454, 'f1-score': 0.6715563506261181, 'support': 54.0}, 'weighted avg': {'precision': 0.6831810165143499, 'recall': 0.6851851851851852, 'f1-score': 0.6839462002252701, 'support': 54.0}}
No log	170.0	340	1.8405	{'0': {'precision': 0.6086956521739131, 'recall': 0.6363636363636364, 'f1-score': 0.6222222222222222, 'support': 22.0}, '1': {'precision': 0.7419354838709677, 'recall': 0.71875, 'f1-score': 0.7301587301587301, 'support': 32.0}, 'accuracy': 0.6851851851851852, 'macro avg': {'precision': 0.6753155680224404, 'recall': 0.6775568181818181, 'f1-score': 0.6761904761904762, 'support': 54.0}, 'weighted avg': {'precision': 0.6876525894758714, 'recall': 0.6851851851851852, 'f1-score': 0.6861845972957084, 'support': 54.0}}
No log	171.0	342	1.8340	{'0': {'precision': 0.6363636363636364, 'recall': 0.6363636363636364, 'f1-score': 0.6363636363636364, 'support': 22.0}, '1': {'precision': 0.75, 'recall': 0.75, 'f1-score': 0.75, 'support': 32.0}, 'accuracy': 0.7037037037037037, 'macro avg': {'precision': 0.6931818181818181, 'recall': 0.6931818181818181, 'f1-score': 0.6931818181818181, 'support': 54.0}, 'weighted avg': {'precision': 0.7037037037037037, 'recall': 0.7037037037037037, 'f1-score': 0.7037037037037037, 'support': 54.0}}
No log	172.0	344	1.8380	{'0': {'precision': 0.5909090909090909, 'recall': 0.5909090909090909, 'f1-score': 0.5909090909090909, 'support': 22.0}, '1': {'precision': 0.71875, 'recall': 0.71875, 'f1-score': 0.71875, 'support': 32.0}, 'accuracy': 0.6666666666666666, 'macro avg': {'precision': 0.6548295454545454, 'recall': 0.6548295454545454, 'f1-score': 0.6548295454545454, 'support': 54.0}, 'weighted avg': {'precision': 0.6666666666666666, 'recall': 0.6666666666666666, 'f1-score': 0.6666666666666666, 'support': 54.0}}
No log	173.0	346	1.8567	{'0': {'precision': 0.5909090909090909, 'recall': 0.5909090909090909, 'f1-score': 0.5909090909090909, 'support': 22.0}, '1': {'precision': 0.71875, 'recall': 0.71875, 'f1-score': 0.71875, 'support': 32.0}, 'accuracy': 0.6666666666666666, 'macro avg': {'precision': 0.6548295454545454, 'recall': 0.6548295454545454, 'f1-score': 0.6548295454545454, 'support': 54.0}, 'weighted avg': {'precision': 0.6666666666666666, 'recall': 0.6666666666666666, 'f1-score': 0.6666666666666666, 'support': 54.0}}
No log	174.0	348	1.8468	{'0': {'precision': 0.6363636363636364, 'recall': 0.6363636363636364, 'f1-score': 0.6363636363636364, 'support': 22.0}, '1': {'precision': 0.75, 'recall': 0.75, 'f1-score': 0.75, 'support': 32.0}, 'accuracy': 0.7037037037037037, 'macro avg': {'precision': 0.6931818181818181, 'recall': 0.6931818181818181, 'f1-score': 0.6931818181818181, 'support': 54.0}, 'weighted avg': {'precision': 0.7037037037037037, 'recall': 0.7037037037037037, 'f1-score': 0.7037037037037037, 'support': 54.0}}
No log	175.0	350	1.8516	{'0': {'precision': 0.5909090909090909, 'recall': 0.5909090909090909, 'f1-score': 0.5909090909090909, 'support': 22.0}, '1': {'precision': 0.71875, 'recall': 0.71875, 'f1-score': 0.71875, 'support': 32.0}, 'accuracy': 0.6666666666666666, 'macro avg': {'precision': 0.6548295454545454, 'recall': 0.6548295454545454, 'f1-score': 0.6548295454545454, 'support': 54.0}, 'weighted avg': {'precision': 0.6666666666666666, 'recall': 0.6666666666666666, 'f1-score': 0.6666666666666666, 'support': 54.0}}
No log	176.0	352	1.8513	{'0': {'precision': 0.6190476190476191, 'recall': 0.5909090909090909, 'f1-score': 0.6046511627906976, 'support': 22.0}, '1': {'precision': 0.7272727272727273, 'recall': 0.75, 'f1-score': 0.7384615384615385, 'support': 32.0}, 'accuracy': 0.6851851851851852, 'macro avg': {'precision': 0.6731601731601732, 'recall': 0.6704545454545454, 'f1-score': 0.6715563506261181, 'support': 54.0}, 'weighted avg': {'precision': 0.6831810165143499, 'recall': 0.6851851851851852, 'f1-score': 0.6839462002252701, 'support': 54.0}}
No log	177.0	354	1.8428	{'0': {'precision': 0.5909090909090909, 'recall': 0.5909090909090909, 'f1-score': 0.5909090909090909, 'support': 22.0}, '1': {'precision': 0.71875, 'recall': 0.71875, 'f1-score': 0.71875, 'support': 32.0}, 'accuracy': 0.6666666666666666, 'macro avg': {'precision': 0.6548295454545454, 'recall': 0.6548295454545454, 'f1-score': 0.6548295454545454, 'support': 54.0}, 'weighted avg': {'precision': 0.6666666666666666, 'recall': 0.6666666666666666, 'f1-score': 0.6666666666666666, 'support': 54.0}}
No log	178.0	356	1.8395	{'0': {'precision': 0.6190476190476191, 'recall': 0.5909090909090909, 'f1-score': 0.6046511627906976, 'support': 22.0}, '1': {'precision': 0.7272727272727273, 'recall': 0.75, 'f1-score': 0.7384615384615385, 'support': 32.0}, 'accuracy': 0.6851851851851852, 'macro avg': {'precision': 0.6731601731601732, 'recall': 0.6704545454545454, 'f1-score': 0.6715563506261181, 'support': 54.0}, 'weighted avg': {'precision': 0.6831810165143499, 'recall': 0.6851851851851852, 'f1-score': 0.6839462002252701, 'support': 54.0}}
No log	179.0	358	1.8570	{'0': {'precision': 0.6, 'recall': 0.5454545454545454, 'f1-score': 0.5714285714285714, 'support': 22.0}, '1': {'precision': 0.7058823529411765, 'recall': 0.75, 'f1-score': 0.7272727272727273, 'support': 32.0}, 'accuracy': 0.6666666666666666, 'macro avg': {'precision': 0.6529411764705882, 'recall': 0.6477272727272727, 'f1-score': 0.6493506493506493, 'support': 54.0}, 'weighted avg': {'precision': 0.6627450980392157, 'recall': 0.6666666666666666, 'f1-score': 0.6637806637806638, 'support': 54.0}}
No log	180.0	360	1.8530	{'0': {'precision': 0.5909090909090909, 'recall': 0.5909090909090909, 'f1-score': 0.5909090909090909, 'support': 22.0}, '1': {'precision': 0.71875, 'recall': 0.71875, 'f1-score': 0.71875, 'support': 32.0}, 'accuracy': 0.6666666666666666, 'macro avg': {'precision': 0.6548295454545454, 'recall': 0.6548295454545454, 'f1-score': 0.6548295454545454, 'support': 54.0}, 'weighted avg': {'precision': 0.6666666666666666, 'recall': 0.6666666666666666, 'f1-score': 0.6666666666666666, 'support': 54.0}}
No log	181.0	362	1.8455	{'0': {'precision': 0.5909090909090909, 'recall': 0.5909090909090909, 'f1-score': 0.5909090909090909, 'support': 22.0}, '1': {'precision': 0.71875, 'recall': 0.71875, 'f1-score': 0.71875, 'support': 32.0}, 'accuracy': 0.6666666666666666, 'macro avg': {'precision': 0.6548295454545454, 'recall': 0.6548295454545454, 'f1-score': 0.6548295454545454, 'support': 54.0}, 'weighted avg': {'precision': 0.6666666666666666, 'recall': 0.6666666666666666, 'f1-score': 0.6666666666666666, 'support': 54.0}}
No log	182.0	364	1.8381	{'0': {'precision': 0.5909090909090909, 'recall': 0.5909090909090909, 'f1-score': 0.5909090909090909, 'support': 22.0}, '1': {'precision': 0.71875, 'recall': 0.71875, 'f1-score': 0.71875, 'support': 32.0}, 'accuracy': 0.6666666666666666, 'macro avg': {'precision': 0.6548295454545454, 'recall': 0.6548295454545454, 'f1-score': 0.6548295454545454, 'support': 54.0}, 'weighted avg': {'precision': 0.6666666666666666, 'recall': 0.6666666666666666, 'f1-score': 0.6666666666666666, 'support': 54.0}}
No log	183.0	366	1.8519	{'0': {'precision': 0.5909090909090909, 'recall': 0.5909090909090909, 'f1-score': 0.5909090909090909, 'support': 22.0}, '1': {'precision': 0.71875, 'recall': 0.71875, 'f1-score': 0.71875, 'support': 32.0}, 'accuracy': 0.6666666666666666, 'macro avg': {'precision': 0.6548295454545454, 'recall': 0.6548295454545454, 'f1-score': 0.6548295454545454, 'support': 54.0}, 'weighted avg': {'precision': 0.6666666666666666, 'recall': 0.6666666666666666, 'f1-score': 0.6666666666666666, 'support': 54.0}}
No log	184.0	368	1.8427	{'0': {'precision': 0.5909090909090909, 'recall': 0.5909090909090909, 'f1-score': 0.5909090909090909, 'support': 22.0}, '1': {'precision': 0.71875, 'recall': 0.71875, 'f1-score': 0.71875, 'support': 32.0}, 'accuracy': 0.6666666666666666, 'macro avg': {'precision': 0.6548295454545454, 'recall': 0.6548295454545454, 'f1-score': 0.6548295454545454, 'support': 54.0}, 'weighted avg': {'precision': 0.6666666666666666, 'recall': 0.6666666666666666, 'f1-score': 0.6666666666666666, 'support': 54.0}}
No log	185.0	370	1.8438	{'0': {'precision': 0.6190476190476191, 'recall': 0.5909090909090909, 'f1-score': 0.6046511627906976, 'support': 22.0}, '1': {'precision': 0.7272727272727273, 'recall': 0.75, 'f1-score': 0.7384615384615385, 'support': 32.0}, 'accuracy': 0.6851851851851852, 'macro avg': {'precision': 0.6731601731601732, 'recall': 0.6704545454545454, 'f1-score': 0.6715563506261181, 'support': 54.0}, 'weighted avg': {'precision': 0.6831810165143499, 'recall': 0.6851851851851852, 'f1-score': 0.6839462002252701, 'support': 54.0}}
No log	186.0	372	1.8430	{'0': {'precision': 0.6190476190476191, 'recall': 0.5909090909090909, 'f1-score': 0.6046511627906976, 'support': 22.0}, '1': {'precision': 0.7272727272727273, 'recall': 0.75, 'f1-score': 0.7384615384615385, 'support': 32.0}, 'accuracy': 0.6851851851851852, 'macro avg': {'precision': 0.6731601731601732, 'recall': 0.6704545454545454, 'f1-score': 0.6715563506261181, 'support': 54.0}, 'weighted avg': {'precision': 0.6831810165143499, 'recall': 0.6851851851851852, 'f1-score': 0.6839462002252701, 'support': 54.0}}
No log	187.0	374	1.8598	{'0': {'precision': 0.6086956521739131, 'recall': 0.6363636363636364, 'f1-score': 0.6222222222222222, 'support': 22.0}, '1': {'precision': 0.7419354838709677, 'recall': 0.71875, 'f1-score': 0.7301587301587301, 'support': 32.0}, 'accuracy': 0.6851851851851852, 'macro avg': {'precision': 0.6753155680224404, 'recall': 0.6775568181818181, 'f1-score': 0.6761904761904762, 'support': 54.0}, 'weighted avg': {'precision': 0.6876525894758714, 'recall': 0.6851851851851852, 'f1-score': 0.6861845972957084, 'support': 54.0}}
No log	188.0	376	1.8442	{'0': {'precision': 0.6363636363636364, 'recall': 0.6363636363636364, 'f1-score': 0.6363636363636364, 'support': 22.0}, '1': {'precision': 0.75, 'recall': 0.75, 'f1-score': 0.75, 'support': 32.0}, 'accuracy': 0.7037037037037037, 'macro avg': {'precision': 0.6931818181818181, 'recall': 0.6931818181818181, 'f1-score': 0.6931818181818181, 'support': 54.0}, 'weighted avg': {'precision': 0.7037037037037037, 'recall': 0.7037037037037037, 'f1-score': 0.7037037037037037, 'support': 54.0}}
No log	189.0	378	1.8466	{'0': {'precision': 0.6190476190476191, 'recall': 0.5909090909090909, 'f1-score': 0.6046511627906976, 'support': 22.0}, '1': {'precision': 0.7272727272727273, 'recall': 0.75, 'f1-score': 0.7384615384615385, 'support': 32.0}, 'accuracy': 0.6851851851851852, 'macro avg': {'precision': 0.6731601731601732, 'recall': 0.6704545454545454, 'f1-score': 0.6715563506261181, 'support': 54.0}, 'weighted avg': {'precision': 0.6831810165143499, 'recall': 0.6851851851851852, 'f1-score': 0.6839462002252701, 'support': 54.0}}
No log	190.0	380	1.8384	{'0': {'precision': 0.6190476190476191, 'recall': 0.5909090909090909, 'f1-score': 0.6046511627906976, 'support': 22.0}, '1': {'precision': 0.7272727272727273, 'recall': 0.75, 'f1-score': 0.7384615384615385, 'support': 32.0}, 'accuracy': 0.6851851851851852, 'macro avg': {'precision': 0.6731601731601732, 'recall': 0.6704545454545454, 'f1-score': 0.6715563506261181, 'support': 54.0}, 'weighted avg': {'precision': 0.6831810165143499, 'recall': 0.6851851851851852, 'f1-score': 0.6839462002252701, 'support': 54.0}}
No log	191.0	382	1.8523	{'0': {'precision': 0.5909090909090909, 'recall': 0.5909090909090909, 'f1-score': 0.5909090909090909, 'support': 22.0}, '1': {'precision': 0.71875, 'recall': 0.71875, 'f1-score': 0.71875, 'support': 32.0}, 'accuracy': 0.6666666666666666, 'macro avg': {'precision': 0.6548295454545454, 'recall': 0.6548295454545454, 'f1-score': 0.6548295454545454, 'support': 54.0}, 'weighted avg': {'precision': 0.6666666666666666, 'recall': 0.6666666666666666, 'f1-score': 0.6666666666666666, 'support': 54.0}}
No log	192.0	384	1.8471	{'0': {'precision': 0.6190476190476191, 'recall': 0.5909090909090909, 'f1-score': 0.6046511627906976, 'support': 22.0}, '1': {'precision': 0.7272727272727273, 'recall': 0.75, 'f1-score': 0.7384615384615385, 'support': 32.0}, 'accuracy': 0.6851851851851852, 'macro avg': {'precision': 0.6731601731601732, 'recall': 0.6704545454545454, 'f1-score': 0.6715563506261181, 'support': 54.0}, 'weighted avg': {'precision': 0.6831810165143499, 'recall': 0.6851851851851852, 'f1-score': 0.6839462002252701, 'support': 54.0}}
No log	193.0	386	1.8426	{'0': {'precision': 0.6190476190476191, 'recall': 0.5909090909090909, 'f1-score': 0.6046511627906976, 'support': 22.0}, '1': {'precision': 0.7272727272727273, 'recall': 0.75, 'f1-score': 0.7384615384615385, 'support': 32.0}, 'accuracy': 0.6851851851851852, 'macro avg': {'precision': 0.6731601731601732, 'recall': 0.6704545454545454, 'f1-score': 0.6715563506261181, 'support': 54.0}, 'weighted avg': {'precision': 0.6831810165143499, 'recall': 0.6851851851851852, 'f1-score': 0.6839462002252701, 'support': 54.0}}
No log	194.0	388	1.8424	{'0': {'precision': 0.5909090909090909, 'recall': 0.5909090909090909, 'f1-score': 0.5909090909090909, 'support': 22.0}, '1': {'precision': 0.71875, 'recall': 0.71875, 'f1-score': 0.71875, 'support': 32.0}, 'accuracy': 0.6666666666666666, 'macro avg': {'precision': 0.6548295454545454, 'recall': 0.6548295454545454, 'f1-score': 0.6548295454545454, 'support': 54.0}, 'weighted avg': {'precision': 0.6666666666666666, 'recall': 0.6666666666666666, 'f1-score': 0.6666666666666666, 'support': 54.0}}
No log	195.0	390	1.8432	{'0': {'precision': 0.5909090909090909, 'recall': 0.5909090909090909, 'f1-score': 0.5909090909090909, 'support': 22.0}, '1': {'precision': 0.71875, 'recall': 0.71875, 'f1-score': 0.71875, 'support': 32.0}, 'accuracy': 0.6666666666666666, 'macro avg': {'precision': 0.6548295454545454, 'recall': 0.6548295454545454, 'f1-score': 0.6548295454545454, 'support': 54.0}, 'weighted avg': {'precision': 0.6666666666666666, 'recall': 0.6666666666666666, 'f1-score': 0.6666666666666666, 'support': 54.0}}
No log	196.0	392	1.8511	{'0': {'precision': 0.6190476190476191, 'recall': 0.5909090909090909, 'f1-score': 0.6046511627906976, 'support': 22.0}, '1': {'precision': 0.7272727272727273, 'recall': 0.75, 'f1-score': 0.7384615384615385, 'support': 32.0}, 'accuracy': 0.6851851851851852, 'macro avg': {'precision': 0.6731601731601732, 'recall': 0.6704545454545454, 'f1-score': 0.6715563506261181, 'support': 54.0}, 'weighted avg': {'precision': 0.6831810165143499, 'recall': 0.6851851851851852, 'f1-score': 0.6839462002252701, 'support': 54.0}}
No log	197.0	394	1.8382	{'0': {'precision': 0.6190476190476191, 'recall': 0.5909090909090909, 'f1-score': 0.6046511627906976, 'support': 22.0}, '1': {'precision': 0.7272727272727273, 'recall': 0.75, 'f1-score': 0.7384615384615385, 'support': 32.0}, 'accuracy': 0.6851851851851852, 'macro avg': {'precision': 0.6731601731601732, 'recall': 0.6704545454545454, 'f1-score': 0.6715563506261181, 'support': 54.0}, 'weighted avg': {'precision': 0.6831810165143499, 'recall': 0.6851851851851852, 'f1-score': 0.6839462002252701, 'support': 54.0}}
No log	198.0	396	1.8549	{'0': {'precision': 0.5909090909090909, 'recall': 0.5909090909090909, 'f1-score': 0.5909090909090909, 'support': 22.0}, '1': {'precision': 0.71875, 'recall': 0.71875, 'f1-score': 0.71875, 'support': 32.0}, 'accuracy': 0.6666666666666666, 'macro avg': {'precision': 0.6548295454545454, 'recall': 0.6548295454545454, 'f1-score': 0.6548295454545454, 'support': 54.0}, 'weighted avg': {'precision': 0.6666666666666666, 'recall': 0.6666666666666666, 'f1-score': 0.6666666666666666, 'support': 54.0}}
No log	199.0	398	1.8486	{'0': {'precision': 0.6190476190476191, 'recall': 0.5909090909090909, 'f1-score': 0.6046511627906976, 'support': 22.0}, '1': {'precision': 0.7272727272727273, 'recall': 0.75, 'f1-score': 0.7384615384615385, 'support': 32.0}, 'accuracy': 0.6851851851851852, 'macro avg': {'precision': 0.6731601731601732, 'recall': 0.6704545454545454, 'f1-score': 0.6715563506261181, 'support': 54.0}, 'weighted avg': {'precision': 0.6831810165143499, 'recall': 0.6851851851851852, 'f1-score': 0.6839462002252701, 'support': 54.0}}
No log	200.0	400	1.8529	{'0': {'precision': 0.6190476190476191, 'recall': 0.5909090909090909, 'f1-score': 0.6046511627906976, 'support': 22.0}, '1': {'precision': 0.7272727272727273, 'recall': 0.75, 'f1-score': 0.7384615384615385, 'support': 32.0}, 'accuracy': 0.6851851851851852, 'macro avg': {'precision': 0.6731601731601732, 'recall': 0.6704545454545454, 'f1-score': 0.6715563506261181, 'support': 54.0}, 'weighted avg': {'precision': 0.6831810165143499, 'recall': 0.6851851851851852, 'f1-score': 0.6839462002252701, 'support': 54.0}}

Framework versions

• Transformers 4.53.1
• Pytorch 2.6.0+cu124
• Datasets 3.5.0
• Tokenizers 0.21.1