This model is a fine-tuned version of ntu-spml/distilhubert on the FMA-music-dataset dataset. It achieves the following results on the evaluation set:

Loss: 1.8929
Accuracy: 0.4575

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: 8
eval_batch_size: 8
seed: 42
optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: linear
lr_scheduler_warmup_steps: 100
num_epochs: 10

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
2.0384	1.0	238	2.2583	0.2358
1.9204	2.0	476	2.1387	0.2972
1.6768	3.0	714	1.9356	0.3774
1.7940	4.0	952	1.8584	0.3868
1.5281	5.0	1190	1.7814	0.4434
1.1608	6.0	1428	1.8745	0.4151
1.2103	7.0	1666	1.8066	0.4434
0.8522	8.0	1904	1.8688	0.4434
0.7455	9.0	2142	1.8818	0.4528
0.5444	10.0	2380	1.8929	0.4575

Framework versions

Transformers 5.0.0
Pytorch 2.10.0+cu128
Datasets 4.0.0
Tokenizers 0.22.2

Danieljava/distilhubert-finetuned-gtzan

distilhubert-finetuned-FMA-music

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

Danieljava/
distilhubert-finetuned-gtzan