This model is a fine-tuned version of distilbert/distilbert-base-uncased on an unknown dataset. It achieves the following results on the evaluation set:

Loss: 1.0725
F1: [0.9473684210526316, 0.6666666666666666, 0.8181818181818182, 0.6666666666666665, 0.631578947368421, 0.7368421052631577, 0.4444444444444445, 0.7272727272727273, 0.2, 0.7368421052631577, 0.8695652173913044, 0.7272727272727272, 0.47058823529411764, 0.2105263157894737, 0.7826086956521738, 0.5714285714285713, 0.7200000000000001, 0.6666666666666666, 0.5333333333333333, 0.7777777777777777]

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: 16
eval_batch_size: 16
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	95	2.4874	[0.4347826086956522, 0.5142857142857143, 0.0, 0.5, 0.23529411764705882, 0.33333333333333337, 0.0, 0.0, 0.0, 0.0, 0.3636363636363636, 0.380952380952381, 0.0, 0.0, 0.43243243243243246, 0.0, 0.5, 0.0, 0.18181818181818182, 0.35294117647058826]
No log	2.0	190	2.0485	[0.588235294117647, 0.48, 0.625, 0.7499999999999999, 0.3870967741935483, 0.16666666666666669, 0.0, 0.0, 0.0, 0.5625, 0.6428571428571429, 0.45454545454545453, 0.18181818181818182, 0.35714285714285715, 0.25, 0.6956521739130435, 0.4347826086956522, 0.0, 0.48, 0.7]
No log	3.0	285	1.7309	[0.9, 0.6666666666666667, 0.7200000000000001, 0.7499999999999999, 0.6428571428571429, 0.588235294117647, 0.18181818181818182, 0.7000000000000001, 0.0, 0.5882352941176471, 0.6896551724137931, 0.5217391304347826, 0.0, 0.2727272727272727, 0.8000000000000002, 0.8000000000000002, 0.6956521739130435, 0.0, 0.5, 0.8235294117647058]
No log	4.0	380	1.4777	[0.8571428571428572, 0.7200000000000001, 0.8181818181818182, 0.8235294117647058, 0.5263157894736842, 0.625, 0.30769230769230765, 0.6956521739130436, 0.0, 0.5, 0.8695652173913044, 0.5925925925925927, 0.5, 0.2105263157894737, 0.8000000000000002, 0.6666666666666666, 0.6956521739130435, 0.4, 0.33333333333333326, 0.7777777777777777]
No log	5.0	475	1.3535	[0.9, 0.75, 0.8181818181818182, 0.8235294117647058, 0.64, 0.625, 0.37499999999999994, 0.7272727272727273, 0.0, 0.7000000000000001, 0.8695652173913044, 0.5454545454545454, 0.15384615384615383, 0.3157894736842105, 0.6956521739130435, 0.7272727272727272, 0.7826086956521738, 0.4, 0.631578947368421, 0.7777777777777777]
1.8726	6.0	570	1.2614	[0.9, 0.7272727272727272, 0.8333333333333333, 0.7499999999999999, 0.6363636363636365, 0.7368421052631577, 0.4285714285714285, 0.761904761904762, 0.0, 0.5, 0.7407407407407407, 0.608695652173913, 0.4285714285714285, 0.3, 0.7272727272727272, 0.7272727272727272, 0.75, 0.6666666666666666, 0.4210526315789474, 0.8235294117647058]
1.8726	7.0	665	1.1649	[0.9473684210526316, 0.7272727272727272, 0.8333333333333333, 0.6666666666666665, 0.631578947368421, 0.7368421052631577, 0.47058823529411764, 0.761904761904762, 0.0, 0.7000000000000001, 0.8695652173913044, 0.7272727272727272, 0.6666666666666666, 0.20000000000000004, 0.6956521739130435, 0.6956521739130435, 0.7826086956521738, 0.5714285714285715, 0.5333333333333333, 0.7777777777777777]
1.8726	8.0	760	1.1142	[0.9473684210526316, 0.7272727272727272, 0.8181818181818182, 0.6666666666666665, 0.761904761904762, 0.7368421052631577, 0.4444444444444445, 0.761904761904762, 0.22222222222222224, 0.7058823529411765, 0.8333333333333333, 0.7272727272727272, 0.47058823529411764, 0.2105263157894737, 0.8571428571428572, 0.7272727272727272, 0.7826086956521738, 0.6666666666666666, 0.4444444444444445, 0.7777777777777777]
1.8726	9.0	855	1.0813	[0.9473684210526316, 0.7272727272727272, 0.8695652173913044, 0.6666666666666665, 0.631578947368421, 0.7368421052631577, 0.4444444444444445, 0.7272727272727273, 0.22222222222222224, 0.7368421052631577, 0.9090909090909091, 0.7272727272727272, 0.47058823529411764, 0.22222222222222224, 0.7826086956521738, 0.608695652173913, 0.75, 0.6666666666666666, 0.5333333333333333, 0.7777777777777777]
1.8726	10.0	950	1.0725	[0.9473684210526316, 0.6666666666666666, 0.8181818181818182, 0.6666666666666665, 0.631578947368421, 0.7368421052631577, 0.4444444444444445, 0.7272727272727273, 0.2, 0.7368421052631577, 0.8695652173913044, 0.7272727272727272, 0.47058823529411764, 0.2105263157894737, 0.7826086956521738, 0.5714285714285713, 0.7200000000000001, 0.6666666666666666, 0.5333333333333333, 0.7777777777777777]

Framework versions

Transformers 4.38.2
Pytorch 2.2.1+cu121
Datasets 2.18.0
Tokenizers 0.15.2

tjl223/song-artist-classifier-v2

song-artist-classifier-v2

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

tjl223/
song-artist-classifier-v2