overall_binary / README.md

harun27

End of training

1284738 verified about 2 months ago

preview code

raw

history blame contribute delete

37.3 kB

metadata

library_name: transformers
license: apache-2.0
base_model: answerdotai/ModernBERT-large
tags:
  - generated_from_trainer
model-index:
  - name: overall_binary
    results: []

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: 0.5527
Classification Report: {'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}}

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: 1e-06
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
num_epochs: 60

Training results

Training Loss	Epoch	Step	Validation Loss	Classification Report
No log	1.0	2	0.6891	{'0': {'precision': 0.4375, 'recall': 0.6363636363636364, 'f1-score': 0.5185185185185185, 'support': 22.0}, '1': {'precision': 0.6363636363636364, 'recall': 0.4375, 'f1-score': 0.5185185185185185, 'support': 32.0}, 'accuracy': 0.5185185185185185, 'macro avg': {'precision': 0.5369318181818181, 'recall': 0.5369318181818181, 'f1-score': 0.5185185185185185, 'support': 54.0}, 'weighted avg': {'precision': 0.5553451178451179, 'recall': 0.5185185185185185, 'f1-score': 0.5185185185185185, 'support': 54.0}}
No log	2.0	4	0.6670	{'0': {'precision': 0.5555555555555556, 'recall': 0.22727272727272727, 'f1-score': 0.3225806451612903, 'support': 22.0}, '1': {'precision': 0.6222222222222222, 'recall': 0.875, 'f1-score': 0.7272727272727273, 'support': 32.0}, 'accuracy': 0.6111111111111112, 'macro avg': {'precision': 0.5888888888888889, 'recall': 0.5511363636363636, 'f1-score': 0.5249266862170088, 'support': 54.0}, 'weighted avg': {'precision': 0.5950617283950618, 'recall': 0.6111111111111112, 'f1-score': 0.5623981753014011, 'support': 54.0}}
No log	3.0	6	0.6707	{'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	4.0	8	0.6573	{'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	0.6460	{'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	0.6352	{'0': {'precision': 0.5, 'recall': 0.13636363636363635, 'f1-score': 0.21428571428571427, 'support': 22.0}, '1': {'precision': 0.6041666666666666, 'recall': 0.90625, 'f1-score': 0.725, 'support': 32.0}, 'accuracy': 0.5925925925925926, 'macro avg': {'precision': 0.5520833333333333, 'recall': 0.5213068181818181, 'f1-score': 0.46964285714285714, 'support': 54.0}, 'weighted avg': {'precision': 0.5617283950617283, 'recall': 0.5925925925925926, 'f1-score': 0.5169312169312169, 'support': 54.0}}
No log	7.0	14	0.6288	{'0': {'precision': 0.625, 'recall': 0.22727272727272727, 'f1-score': 0.3333333333333333, 'support': 22.0}, '1': {'precision': 0.6304347826086957, 'recall': 0.90625, 'f1-score': 0.7435897435897436, 'support': 32.0}, 'accuracy': 0.6296296296296297, 'macro avg': {'precision': 0.6277173913043479, 'recall': 0.5667613636363636, 'f1-score': 0.5384615384615384, 'support': 54.0}, 'weighted avg': {'precision': 0.6282206119162642, 'recall': 0.6296296296296297, 'f1-score': 0.5764482431149097, 'support': 54.0}}
No log	8.0	16	0.6234	{'0': {'precision': 0.6666666666666666, 'recall': 0.45454545454545453, 'f1-score': 0.5405405405405406, 'support': 22.0}, '1': {'precision': 0.6923076923076923, 'recall': 0.84375, 'f1-score': 0.7605633802816901, 'support': 32.0}, 'accuracy': 0.6851851851851852, 'macro avg': {'precision': 0.6794871794871795, 'recall': 0.6491477272727273, 'f1-score': 0.6505519604111154, 'support': 54.0}, 'weighted avg': {'precision': 0.6818613485280152, 'recall': 0.6851851851851852, 'f1-score': 0.6709244455723329, 'support': 54.0}}
No log	9.0	18	0.6166	{'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	10.0	20	0.6029	{'0': {'precision': 0.6666666666666666, 'recall': 0.45454545454545453, 'f1-score': 0.5405405405405406, 'support': 22.0}, '1': {'precision': 0.6923076923076923, 'recall': 0.84375, 'f1-score': 0.7605633802816901, 'support': 32.0}, 'accuracy': 0.6851851851851852, 'macro avg': {'precision': 0.6794871794871795, 'recall': 0.6491477272727273, 'f1-score': 0.6505519604111154, 'support': 54.0}, 'weighted avg': {'precision': 0.6818613485280152, 'recall': 0.6851851851851852, 'f1-score': 0.6709244455723329, 'support': 54.0}}
No log	11.0	22	0.5977	{'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	12.0	24	0.5902	{'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	13.0	26	0.5839	{'0': {'precision': 0.6, 'recall': 0.6818181818181818, 'f1-score': 0.6382978723404256, 'support': 22.0}, '1': {'precision': 0.7586206896551724, 'recall': 0.6875, 'f1-score': 0.7213114754098361, 'support': 32.0}, 'accuracy': 0.6851851851851852, 'macro avg': {'precision': 0.6793103448275861, 'recall': 0.6846590909090908, 'f1-score': 0.6798046738751309, 'support': 54.0}, 'weighted avg': {'precision': 0.6939974457215835, 'recall': 0.6851851851851852, 'f1-score': 0.68749111860378, 'support': 54.0}}
No log	14.0	28	0.5809	{'0': {'precision': 0.5925925925925926, 'recall': 0.7272727272727273, 'f1-score': 0.6530612244897959, 'support': 22.0}, '1': {'precision': 0.7777777777777778, 'recall': 0.65625, 'f1-score': 0.711864406779661, 'support': 32.0}, 'accuracy': 0.6851851851851852, 'macro avg': {'precision': 0.6851851851851851, 'recall': 0.6917613636363636, 'f1-score': 0.6824628156347284, 'support': 54.0}, 'weighted avg': {'precision': 0.7023319615912208, 'recall': 0.6851851851851852, 'f1-score': 0.6879075547356419, 'support': 54.0}}
No log	15.0	30	0.5742	{'0': {'precision': 0.5925925925925926, 'recall': 0.7272727272727273, 'f1-score': 0.6530612244897959, 'support': 22.0}, '1': {'precision': 0.7777777777777778, 'recall': 0.65625, 'f1-score': 0.711864406779661, 'support': 32.0}, 'accuracy': 0.6851851851851852, 'macro avg': {'precision': 0.6851851851851851, 'recall': 0.6917613636363636, 'f1-score': 0.6824628156347284, 'support': 54.0}, 'weighted avg': {'precision': 0.7023319615912208, 'recall': 0.6851851851851852, 'f1-score': 0.6879075547356419, 'support': 54.0}}
No log	16.0	32	0.5630	{'0': {'precision': 0.6, 'recall': 0.6818181818181818, 'f1-score': 0.6382978723404256, 'support': 22.0}, '1': {'precision': 0.7586206896551724, 'recall': 0.6875, 'f1-score': 0.7213114754098361, 'support': 32.0}, 'accuracy': 0.6851851851851852, 'macro avg': {'precision': 0.6793103448275861, 'recall': 0.6846590909090908, 'f1-score': 0.6798046738751309, 'support': 54.0}, 'weighted avg': {'precision': 0.6939974457215835, 'recall': 0.6851851851851852, 'f1-score': 0.68749111860378, 'support': 54.0}}
No log	17.0	34	0.5591	{'0': {'precision': 0.7, 'recall': 0.6363636363636364, 'f1-score': 0.6666666666666666, 'support': 22.0}, '1': {'precision': 0.7647058823529411, 'recall': 0.8125, 'f1-score': 0.7878787878787878, 'support': 32.0}, 'accuracy': 0.7407407407407407, 'macro avg': {'precision': 0.7323529411764705, 'recall': 0.7244318181818181, 'f1-score': 0.7272727272727273, 'support': 54.0}, 'weighted avg': {'precision': 0.7383442265795206, 'recall': 0.7407407407407407, 'f1-score': 0.7384960718294051, 'support': 54.0}}
No log	18.0	36	0.5496	{'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	19.0	38	0.5427	{'0': {'precision': 0.5833333333333334, 'recall': 0.6363636363636364, 'f1-score': 0.6086956521739131, 'support': 22.0}, '1': {'precision': 0.7333333333333333, 'recall': 0.6875, 'f1-score': 0.7096774193548387, 'support': 32.0}, 'accuracy': 0.6666666666666666, 'macro avg': {'precision': 0.6583333333333333, 'recall': 0.6619318181818181, 'f1-score': 0.6591865357643759, 'support': 54.0}, 'weighted avg': {'precision': 0.6722222222222222, 'recall': 0.6666666666666666, 'f1-score': 0.6685366993922394, 'support': 54.0}}
No log	20.0	40	0.5372	{'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	21.0	42	0.5395	{'0': {'precision': 0.5925925925925926, 'recall': 0.7272727272727273, 'f1-score': 0.6530612244897959, 'support': 22.0}, '1': {'precision': 0.7777777777777778, 'recall': 0.65625, 'f1-score': 0.711864406779661, 'support': 32.0}, 'accuracy': 0.6851851851851852, 'macro avg': {'precision': 0.6851851851851851, 'recall': 0.6917613636363636, 'f1-score': 0.6824628156347284, 'support': 54.0}, 'weighted avg': {'precision': 0.7023319615912208, 'recall': 0.6851851851851852, 'f1-score': 0.6879075547356419, 'support': 54.0}}
No log	22.0	44	0.5482	{'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	23.0	46	0.5480	{'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	24.0	48	0.5387	{'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	25.0	50	0.5280	{'0': {'precision': 0.5769230769230769, 'recall': 0.6818181818181818, 'f1-score': 0.625, 'support': 22.0}, '1': {'precision': 0.75, 'recall': 0.65625, 'f1-score': 0.7, 'support': 32.0}, 'accuracy': 0.6666666666666666, 'macro avg': {'precision': 0.6634615384615384, 'recall': 0.6690340909090908, 'f1-score': 0.6625, 'support': 54.0}, 'weighted avg': {'precision': 0.6794871794871795, 'recall': 0.6666666666666666, 'f1-score': 0.6694444444444444, 'support': 54.0}}
No log	26.0	52	0.5293	{'0': {'precision': 0.5769230769230769, 'recall': 0.6818181818181818, 'f1-score': 0.625, 'support': 22.0}, '1': {'precision': 0.75, 'recall': 0.65625, 'f1-score': 0.7, 'support': 32.0}, 'accuracy': 0.6666666666666666, 'macro avg': {'precision': 0.6634615384615384, 'recall': 0.6690340909090908, 'f1-score': 0.6625, 'support': 54.0}, 'weighted avg': {'precision': 0.6794871794871795, 'recall': 0.6666666666666666, 'f1-score': 0.6694444444444444, 'support': 54.0}}
No log	27.0	54	0.5337	{'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	28.0	56	0.5526	{'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	0.5693	{'0': {'precision': 0.6060606060606061, 'recall': 0.9090909090909091, 'f1-score': 0.7272727272727273, 'support': 22.0}, '1': {'precision': 0.9047619047619048, 'recall': 0.59375, 'f1-score': 0.7169811320754716, 'support': 32.0}, 'accuracy': 0.7222222222222222, 'macro avg': {'precision': 0.7554112554112554, 'recall': 0.7514204545454546, 'f1-score': 0.7221269296740995, 'support': 54.0}, 'weighted avg': {'precision': 0.783068783068783, 'recall': 0.7222222222222222, 'f1-score': 0.721174004192872, 'support': 54.0}}
No log	30.0	60	0.5618	{'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	31.0	62	0.5456	{'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	32.0	64	0.5323	{'0': {'precision': 0.5925925925925926, 'recall': 0.7272727272727273, 'f1-score': 0.6530612244897959, 'support': 22.0}, '1': {'precision': 0.7777777777777778, 'recall': 0.65625, 'f1-score': 0.711864406779661, 'support': 32.0}, 'accuracy': 0.6851851851851852, 'macro avg': {'precision': 0.6851851851851851, 'recall': 0.6917613636363636, 'f1-score': 0.6824628156347284, 'support': 54.0}, 'weighted avg': {'precision': 0.7023319615912208, 'recall': 0.6851851851851852, 'f1-score': 0.6879075547356419, 'support': 54.0}}
No log	33.0	66	0.5386	{'0': {'precision': 0.5925925925925926, 'recall': 0.7272727272727273, 'f1-score': 0.6530612244897959, 'support': 22.0}, '1': {'precision': 0.7777777777777778, 'recall': 0.65625, 'f1-score': 0.711864406779661, 'support': 32.0}, 'accuracy': 0.6851851851851852, 'macro avg': {'precision': 0.6851851851851851, 'recall': 0.6917613636363636, 'f1-score': 0.6824628156347284, 'support': 54.0}, 'weighted avg': {'precision': 0.7023319615912208, 'recall': 0.6851851851851852, 'f1-score': 0.6879075547356419, 'support': 54.0}}
No log	34.0	68	0.5511	{'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	35.0	70	0.5582	{'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	36.0	72	0.5456	{'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	37.0	74	0.5422	{'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	38.0	76	0.5422	{'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	39.0	78	0.5409	{'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	40.0	80	0.5442	{'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	41.0	82	0.5519	{'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	42.0	84	0.5634	{'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	43.0	86	0.5594	{'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	44.0	88	0.5539	{'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	45.0	90	0.5495	{'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	46.0	92	0.5479	{'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	47.0	94	0.5505	{'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	48.0	96	0.5609	{'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	49.0	98	0.5618	{'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	50.0	100	0.5642	{'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	51.0	102	0.5520	{'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	52.0	104	0.5554	{'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	53.0	106	0.5512	{'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	54.0	108	0.5583	{'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	55.0	110	0.5543	{'0': {'precision': 0.6206896551724138, 'recall': 0.8181818181818182, 'f1-score': 0.7058823529411765, 'support': 22.0}, '1': {'precision': 0.84, 'recall': 0.65625, 'f1-score': 0.7368421052631579, 'support': 32.0}, 'accuracy': 0.7222222222222222, 'macro avg': {'precision': 0.7303448275862069, 'recall': 0.7372159090909092, 'f1-score': 0.7213622291021672, 'support': 54.0}, 'weighted avg': {'precision': 0.7506513409961686, 'recall': 0.7222222222222222, 'f1-score': 0.724228872835684, 'support': 54.0}}
No log	56.0	112	0.5532	{'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	57.0	114	0.5456	{'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	58.0	116	0.5491	{'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	59.0	118	0.5537	{'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	60.0	120	0.5527	{'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}}

Framework versions

Transformers 4.53.1
Pytorch 2.6.0+cu124
Datasets 3.5.0
Tokenizers 0.21.1