MaxLinggg/distilhubert-gtzan-loraAL-dropout0.25-split3

distilhubert-finetuned-gtzan-lora-dropout0.25-split3-full

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

• Loss: 1.1294
• Accuracy: 0.8433

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: 0.001
• train_batch_size: 8
• eval_batch_size: 8
• seed: 42
• optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
• lr_scheduler_type: cosine
• lr_scheduler_warmup_ratio: 0.1
• num_epochs: 10

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
1.5075	1.0	338	1.2247	0.5067
1.0213	2.0	676	1.0717	0.6333
0.833	3.0	1014	1.0113	0.6667
0.712	4.0	1352	0.8911	0.77
0.4565	5.0	1690	1.1474	0.73
0.3869	6.0	2028	0.8967	0.77
0.2404	7.0	2366	0.9313	0.8233
0.0816	8.0	2704	1.1234	0.83
0.0499	9.0	3042	1.1998	0.8267
0.0529	10.0	3380	1.1294	0.8433

Framework versions

• PEFT 0.15.2
• Transformers 4.51.3
• Pytorch 2.6.0
• Datasets 3.3.2
• Tokenizers 0.21.0