dvshah13/dhbert-finetuned-gtzan

ast-finetuned-audioset-10-10-0.4593-finetuned-gtzan

This model is a fine-tuned version of MIT/ast-finetuned-audioset-10-10-0.4593 on the GTZAN dataset. It achieves the following results on the evaluation set:

• Loss: 0.4717
• Accuracy: 0.9

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: 5e-05
• train_batch_size: 4
• eval_batch_size: 4
• seed: 42
• gradient_accumulation_steps: 4
• total_train_batch_size: 16
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• lr_scheduler_warmup_ratio: 0.1
• num_epochs: 20

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
0.7581	1.0	56	0.7029	0.78
0.3942	1.99	112	0.4646	0.86
0.3298	2.99	168	0.3861	0.88
0.1227	4.0	225	0.4702	0.86
0.0774	5.0	281	0.4492	0.9
0.0039	5.99	337	0.4607	0.9
0.0014	6.99	393	0.5022	0.9
0.0022	8.0	450	0.4711	0.9
0.0193	9.0	506	0.5226	0.86
0.0004	9.99	562	0.6055	0.82
0.0003	10.99	618	0.4793	0.89
0.0002	12.0	675	0.5052	0.9
0.0002	13.0	731	0.4652	0.89
0.0001	13.99	787	0.4617	0.9
0.0001	14.99	843	0.4653	0.9
0.0001	16.0	900	0.4635	0.91
0.0001	17.0	956	0.4693	0.9
0.0001	17.99	1012	0.4697	0.9
0.0001	18.99	1068	0.4715	0.9
0.0025	19.91	1120	0.4717	0.9

Framework versions

• Transformers 4.31.0.dev0
• Pytorch 2.0.1+cu118
• Datasets 2.13.1
• Tokenizers 0.13.3