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

Rolo/distilhubert-finetuned-gtzan-4

distilhubert-finetuned-gtzan-4

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

Rolo/
distilhubert-finetuned-gtzan-4