distilhubert-finetuned-gtzan-4

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: 2.8452
• Accuracy: 0.82

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
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• lr_scheduler_warmup_ratio: 0.1
• num_epochs: 80

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
2.2676	1.0	225	2.2400	0.33
1.9086	2.0	450	1.8716	0.55
1.417	3.0	675	1.4094	0.69
1.0484	4.0	900	1.1065	0.77
1.0122	5.0	1125	0.9172	0.75
0.8937	6.0	1350	1.1343	0.66
0.2979	7.0	1575	0.6102	0.83
0.3413	8.0	1800	1.1212	0.71
1.459	9.0	2025	1.5433	0.75
0.5177	10.0	2250	1.3990	0.78
0.0494	11.0	2475	2.7712	0.78
0.792	12.0	2700	2.7047	0.81
0.0	13.0	2925	2.8097	0.82
0.0	14.0	3150	3.3873	0.79
0.0	15.0	3375	2.6185	0.81
0.0	16.0	3600	3.0773	0.81
0.0	17.0	3825	2.2380	0.84
0.0	18.0	4050	2.5949	0.79
0.0	19.0	4275	3.4890	0.75
0.0	20.0	4500	2.7776	0.82
0.0	21.0	4725	3.8952	0.77
0.0	22.0	4950	2.8020	0.8
0.0	23.0	5175	3.7968	0.72
0.0	24.0	5400	2.8630	0.81
0.0	25.0	5625	2.2026	0.84
0.0	26.0	5850	1.8612	0.87
0.0	27.0	6075	3.2650	0.78
0.0	28.0	6300	2.6739	0.82
0.0	29.0	6525	3.0167	0.8
0.0	30.0	6750	1.9316	0.84
0.0	31.0	6975	3.0458	0.8
0.0	32.0	7200	3.3569	0.77
0.0	33.0	7425	3.0012	0.79
0.0	34.0	7650	3.2477	0.79
0.0	35.0	7875	3.2145	0.79
0.0	36.0	8100	3.0645	0.79
0.0	37.0	8325	3.2974	0.77
0.0	38.0	8550	3.4422	0.77
0.0	39.0	8775	2.7268	0.8
0.0	40.0	9000	2.6908	0.8
0.0	41.0	9225	2.4034	0.82
0.0	42.0	9450	3.1446	0.79
0.0	43.0	9675	2.9127	0.8
0.0	44.0	9900	2.3812	0.81
0.0	45.0	10125	2.4215	0.81
0.0	46.0	10350	2.6125	0.82
0.7338	47.0	10575	2.5113	0.82
0.0	48.0	10800	2.9264	0.81
0.0	49.0	11025	2.7811	0.81
0.0	50.0	11250	2.6749	0.8
0.0	51.0	11475	3.2003	0.78
0.0	52.0	11700	3.2670	0.78
0.0	53.0	11925	3.4001	0.76
0.0	54.0	12150	2.8570	0.76
0.0	55.0	12375	2.1772	0.83
0.0	56.0	12600	2.7977	0.81
0.0	57.0	12825	2.9106	0.78
0.0	58.0	13050	2.8428	0.8
0.0	59.0	13275	2.7308	0.78
0.0	60.0	13500	2.8214	0.8
0.0	61.0	13725	2.8194	0.79
0.0	62.0	13950	1.7708	0.85
0.0	63.0	14175	2.6017	0.81
0.0	64.0	14400	2.7698	0.78
0.0	65.0	14625	2.8218	0.81
0.0	66.0	14850	2.8252	0.82
0.0	67.0	15075	2.9149	0.81
0.0	68.0	15300	2.8106	0.81
0.0	69.0	15525	2.8514	0.8
0.0	70.0	15750	2.6649	0.82
0.0	71.0	15975	2.5629	0.81
0.0	72.0	16200	2.8140	0.81
0.0	73.0	16425	2.8164	0.79
0.0	74.0	16650	2.7022	0.81
0.0	75.0	16875	2.7376	0.81
0.0	76.0	17100	2.6498	0.8
0.0	77.0	17325	2.7363	0.81
0.0	78.0	17550	2.8057	0.81
0.0	79.0	17775	2.8526	0.81
0.0	80.0	18000	2.8452	0.82

Framework versions

• Transformers 4.33.0.dev0
• Pytorch 2.0.1+cu117
• Datasets 2.14.4
• Tokenizers 0.13.3