đ distilhubert-finetuned-gtzan
This model is a fine - tuned version of Sandiago21/distilhubert-finetuned-gtzan on the GTZAN dataset. It offers a valuable solution for audio classification tasks, achieving high accuracy on the evaluation set.
đ Documentation
Model Information
Property |
Details |
Model Type |
Fine - tuned version of Sandiago21/distilhubert-finetuned-gtzan |
Training Data |
marsyas/gtzan |
Metrics |
Accuracy |
This model achieves the following results on the evaluation set:
- Loss: 0.9951
- Accuracy: 0.88
Model description
More information needed
Intended uses & limitations
More information needed
Training and evaluation data
More information needed
đ§ Technical Details
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- 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
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 40
Training results
Training Loss |
Epoch |
Step |
Validation Loss |
Accuracy |
0.0951 |
1.0 |
57 |
0.5566 |
0.87 |
0.0629 |
2.0 |
114 |
0.6819 |
0.83 |
0.0231 |
3.0 |
171 |
0.6118 |
0.86 |
0.0159 |
4.0 |
228 |
0.9208 |
0.83 |
0.0374 |
5.0 |
285 |
0.8746 |
0.85 |
0.1714 |
6.0 |
342 |
0.6671 |
0.87 |
0.2148 |
7.0 |
399 |
1.1850 |
0.79 |
0.0147 |
8.0 |
456 |
1.0551 |
0.79 |
0.0788 |
9.0 |
513 |
1.5179 |
0.79 |
0.0015 |
10.0 |
570 |
1.3290 |
0.8 |
0.0049 |
11.0 |
627 |
1.0943 |
0.85 |
0.0012 |
12.0 |
684 |
1.0667 |
0.85 |
0.0043 |
13.0 |
741 |
1.1816 |
0.82 |
0.0015 |
14.0 |
798 |
0.9108 |
0.88 |
0.0011 |
15.0 |
855 |
1.0289 |
0.87 |
0.001 |
16.0 |
912 |
0.7696 |
0.87 |
0.0006 |
17.0 |
969 |
0.8539 |
0.87 |
0.1001 |
18.0 |
1026 |
1.1917 |
0.78 |
0.0017 |
19.0 |
1083 |
1.0016 |
0.83 |
0.0525 |
20.0 |
1140 |
0.9513 |
0.88 |
0.0004 |
21.0 |
1197 |
0.9268 |
0.86 |
0.0003 |
22.0 |
1254 |
1.1209 |
0.82 |
0.0003 |
23.0 |
1311 |
0.9270 |
0.87 |
0.0003 |
24.0 |
1368 |
1.1148 |
0.84 |
0.0003 |
25.0 |
1425 |
1.0507 |
0.85 |
0.0002 |
26.0 |
1482 |
1.0156 |
0.86 |
0.0002 |
27.0 |
1539 |
1.0062 |
0.87 |
0.0002 |
28.0 |
1596 |
1.0124 |
0.87 |
0.0002 |
29.0 |
1653 |
1.0154 |
0.87 |
0.0002 |
30.0 |
1710 |
1.0092 |
0.88 |
0.0002 |
31.0 |
1767 |
1.0123 |
0.88 |
0.0175 |
32.0 |
1824 |
0.9928 |
0.88 |
0.0002 |
33.0 |
1881 |
1.0014 |
0.88 |
0.0115 |
34.0 |
1938 |
0.9989 |
0.88 |
0.0001 |
35.0 |
1995 |
0.9871 |
0.88 |
0.0001 |
36.0 |
2052 |
0.9920 |
0.88 |
0.0002 |
37.0 |
2109 |
0.9974 |
0.88 |
0.0002 |
38.0 |
2166 |
0.9950 |
0.88 |
0.0001 |
39.0 |
2223 |
0.9997 |
0.88 |
0.0001 |
40.0 |
2280 |
0.9951 |
0.88 |
Framework versions
- Transformers 4.31.0
- Pytorch 2.0.1+cu117
- Datasets 2.13.1
- Tokenizers 0.13.3
đ License
This model is licensed under the Apache - 2.0 license.