Loss: 1.0933
F1: [0.7777777777777777, 0.75, 0.7, 0.9473684210526316, 1.0, 0.30769230769230765, 0.9090909090909091, 0.7368421052631577, 0.5217391304347826, 0.8000000000000002, 0.25, 0.761904761904762, 0.6923076923076923, 0.7499999999999999, 0.5, 0.5263157894736842, 0.8421052631578948, 0.8421052631578948, 0.5333333333333333, 0.4347826086956522]

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: 8
eval_batch_size: 8
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 10

Training results

Training Loss	Epoch	Step	Validation Loss	F1
No log	1.0	190	2.1347	[0.3478260869565218, 0.25, 0.26666666666666666, 0.47619047619047616, 0.0, 0.0, 0.18181818181818182, 0.5384615384615384, 0.0, 0.625, 0.0, 0.2222222222222222, 0.33333333333333326, 0.4444444444444445, 0.22222222222222224, 0.5833333333333334, 0.3478260869565218, 0.6923076923076923, 0.4210526315789474, 0.14285714285714288]
No log	2.0	380	1.5541	[0.7368421052631577, 0.14285714285714285, 0.5, 0.4347826086956522, 0.6666666666666666, 0.5, 0.6666666666666666, 0.37499999999999994, 0.3333333333333333, 0.6666666666666666, 0.0, 0.5882352941176471, 0.6666666666666666, 0.6666666666666666, 0.5714285714285713, 0.64, 0.6363636363636365, 0.888888888888889, 0.38461538461538464, 0.45454545454545453]
1.9658	3.0	570	1.2774	[0.7777777777777777, 0.47058823529411764, 0.608695652173913, 0.6, 0.8571428571428571, 0.28571428571428575, 0.7692307692307693, 0.7272727272727272, 0.2727272727272727, 0.7, 0.0, 0.5555555555555556, 0.75, 0.6666666666666666, 0.6, 0.6666666666666666, 0.5185185185185185, 0.8000000000000002, 0.5333333333333333, 0.4000000000000001]
1.9658	4.0	760	1.1606	[0.761904761904762, 0.64, 0.6666666666666666, 0.5, 0.8571428571428571, 0.16666666666666669, 0.8181818181818182, 0.7368421052631577, 0.5, 0.761904761904762, 0.18181818181818182, 0.7777777777777777, 0.8421052631578948, 0.7499999999999999, 0.43243243243243246, 0.6666666666666666, 0.7777777777777777, 0.888888888888889, 0.5714285714285715, 0.37499999999999994]
1.9658	5.0	950	1.2332	[0.608695652173913, 0.6666666666666667, 0.7368421052631577, 0.8235294117647058, 0.888888888888889, 0.5, 0.9, 0.7368421052631577, 0.4615384615384615, 0.7499999999999999, 0.125, 0.7272727272727273, 0.8000000000000002, 0.7499999999999999, 0.37037037037037035, 0.5714285714285715, 0.8421052631578948, 0.888888888888889, 0.5714285714285715, 0.3333333333333333]
0.7658	6.0	1140	1.0831	[0.7, 0.75, 0.7368421052631577, 0.9, 1.0, 0.47058823529411764, 0.8695652173913044, 0.7368421052631577, 0.5, 0.8000000000000002, 0.13333333333333333, 0.761904761904762, 0.7826086956521738, 0.6666666666666666, 0.47619047619047616, 0.7, 0.9, 0.8421052631578948, 0.5714285714285715, 0.4615384615384615]
0.7658	7.0	1330	1.0915	[0.7777777777777777, 0.75, 0.6666666666666666, 0.888888888888889, 1.0, 0.4285714285714285, 0.8181818181818182, 0.7368421052631577, 0.47619047619047616, 0.7777777777777777, 0.25, 0.761904761904762, 0.7200000000000001, 0.7499999999999999, 0.56, 0.7368421052631577, 0.9090909090909091, 0.8421052631578948, 0.5333333333333333, 0.4347826086956522]
0.3114	8.0	1520	1.0847	[0.761904761904762, 0.7058823529411765, 0.7, 0.9473684210526316, 1.0, 0.4285714285714285, 0.9, 0.7368421052631577, 0.48, 0.8000000000000002, 0.26666666666666666, 0.8, 0.8571428571428572, 0.7777777777777777, 0.5185185185185185, 0.6666666666666665, 0.9, 0.888888888888889, 0.5714285714285715, 0.45454545454545453]
0.3114	9.0	1710	1.0742	[0.7777777777777777, 0.7058823529411765, 0.7, 0.9473684210526316, 1.0, 0.4285714285714285, 0.9090909090909091, 0.7368421052631577, 0.5454545454545454, 0.8000000000000002, 0.2857142857142857, 0.7272727272727273, 0.6923076923076923, 0.7499999999999999, 0.5833333333333334, 0.6, 0.8421052631578948, 0.8421052631578948, 0.5714285714285715, 0.4347826086956522]
0.3114	10.0	1900	1.0933	[0.7777777777777777, 0.75, 0.7, 0.9473684210526316, 1.0, 0.30769230769230765, 0.9090909090909091, 0.7368421052631577, 0.5217391304347826, 0.8000000000000002, 0.25, 0.761904761904762, 0.6923076923076923, 0.7499999999999999, 0.5, 0.5263157894736842, 0.8421052631578948, 0.8421052631578948, 0.5333333333333333, 0.4347826086956522]

Framework versions

Transformers 4.38.2
Pytorch 2.2.1+cu121
Datasets 2.18.0
Tokenizers 0.15.2

tjl223/song-artist-classifier-v4-roberta-batch-8

song-artist-classifier-v4-roberta-batch-8

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

tjl223/
song-artist-classifier-v4-roberta-batch-8