Training Loss	Epoch	Step	Validation Loss	Macro F1	Accuracy	Weighted f1	Micro f1	Weighted recall	Micro recall	Macro recall	Weighted precision	Micro precision	Macro precision
1.3419	0.28	100	1.2665	0.4034	0.4276	0.4034	0.4276	0.4276	0.4276	0.4276	0.4520	0.4276	0.4520
1.2338	0.57	200	1.1790	0.4459	0.4638	0.4459	0.4638	0.4638	0.4638	0.4638	0.4618	0.4638	0.4618
1.1842	0.85	300	1.1540	0.4560	0.4972	0.4560	0.4972	0.4972	0.4972	0.4972	0.4966	0.4972	0.4966
1.1304	1.13	400	1.1268	0.5056	0.5249	0.5056	0.5249	0.5249	0.5249	0.5249	0.5269	0.5249	0.5269
1.0815	1.42	500	1.0745	0.5390	0.5447	0.5390	0.5447	0.5447	0.5447	0.5447	0.5396	0.5447	0.5396
1.0822	1.7	600	1.0715	0.5071	0.5270	0.5071	0.5270	0.5270	0.5270	0.5270	0.5260	0.5270	0.5260
1.0484	1.99	700	1.0213	0.5555	0.5632	0.5555	0.5632	0.5632	0.5632	0.5632	0.5573	0.5632	0.5573
0.9784	2.27	800	1.0601	0.5640	0.5739	0.5640	0.5739	0.5739	0.5739	0.5739	0.5715	0.5739	0.5715
0.9627	2.55	900	1.0287	0.5606	0.5746	0.5606	0.5746	0.5746	0.5746	0.5746	0.5714	0.5746	0.5714
0.9614	2.84	1000	0.9945	0.5705	0.5753	0.5705	0.5753	0.5753	0.5753	0.5753	0.5782	0.5753	0.5782
0.9379	3.12	1100	1.0166	0.5852	0.5881	0.5852	0.5881	0.5881	0.5881	0.5881	0.5899	0.5881	0.5899
0.8982	3.4	1200	1.0289	0.5685	0.5724	0.5685	0.5724	0.5724	0.5724	0.5724	0.5905	0.5724	0.5905
0.8651	3.69	1300	1.0100	0.5967	0.6001	0.5967	0.6001	0.6001	0.6001	0.6001	0.6005	0.6001	0.6005
0.9017	3.97	1400	1.0405	0.5702	0.5739	0.5702	0.5739	0.5739	0.5739	0.5739	0.5884	0.5739	0.5884
0.8152	4.26	1500	0.9874	0.6016	0.6030	0.6016	0.6030	0.6030	0.6030	0.6030	0.6090	0.6030	0.6090
0.8149	4.54	1600	0.9994	0.6001	0.6044	0.6001	0.6044	0.6044	0.6044	0.6044	0.6092	0.6044	0.6092
0.7978	4.82	1700	1.0319	0.5945	0.6080	0.5945	0.6080	0.6080	0.6080	0.6080	0.6093	0.6080	0.6093
0.7674	5.11	1800	1.0800	0.5884	0.5909	0.5884	0.5909	0.5909	0.5909	0.5909	0.6128	0.5909	0.6128
0.7126	5.39	1900	1.0071	0.6177	0.6200	0.6177	0.6200	0.6200	0.6200	0.6200	0.6229	0.6200	0.6229
0.7229	5.67	2000	1.0267	0.6141	0.6165	0.6141	0.6165	0.6165	0.6165	0.6165	0.6141	0.6165	0.6141
0.7272	5.96	2100	1.0179	0.6119	0.6143	0.6119	0.6143	0.6143	0.6143	0.6143	0.6147	0.6143	0.6147
0.6519	6.24	2200	1.0576	0.6246	0.6257	0.6246	0.6257	0.6257	0.6257	0.6257	0.6322	0.6257	0.6322
0.6287	6.52	2300	1.0537	0.6275	0.6307	0.6275	0.6307	0.6307	0.6307	0.6307	0.6382	0.6307	0.6382
0.6103	6.81	2400	1.0323	0.6305	0.6328	0.6305	0.6328	0.6328	0.6328	0.6328	0.6329	0.6328	0.6329
0.5639	7.09	2500	1.1021	0.6306	0.6335	0.6306	0.6335	0.6335	0.6335	0.6335	0.6336	0.6335	0.6336
0.5706	7.38	2600	1.1086	0.6328	0.6342	0.6328	0.6342	0.6342	0.6342	0.6342	0.6349	0.6342	0.6349
0.529	7.66	2700	1.1428	0.6194	0.6186	0.6194	0.6186	0.6186	0.6186	0.6186	0.6260	0.6186	0.6260
0.5336	7.94	2800	1.1523	0.6128	0.6136	0.6128	0.6136	0.6136	0.6136	0.6136	0.6131	0.6136	0.6131
0.4776	8.23	2900	1.3509	0.5922	0.5959	0.5922	0.5959	0.5959	0.5959	0.5959	0.6070	0.5959	0.6070
0.4603	8.51	3000	1.2143	0.6036	0.6023	0.6036	0.6023	0.6023	0.6023	0.6023	0.6058	0.6023	0.6058
0.4734	8.79	3100	1.2464	0.6056	0.6051	0.6056	0.6051	0.6051	0.6051	0.6051	0.6063	0.6051	0.6063
0.4358	9.08	3200	1.3027	0.6110	0.6108	0.6110	0.6108	0.6108	0.6108	0.6108	0.6178	0.6108	0.6178
0.3808	9.36	3300	1.3469	0.6265	0.6328	0.6265	0.6328	0.6328	0.6328	0.6328	0.6304	0.6328	0.6304
0.4184	9.65	3400	1.3317	0.6168	0.6165	0.6168	0.6165	0.6165	0.6165	0.6165	0.6220	0.6165	0.6220
0.3748	9.93	3500	1.3316	0.6232	0.625	0.6232	0.625	0.625	0.625	0.625	0.6344	0.625	0.6344
0.3785	10.21	3600	1.3792	0.6144	0.6158	0.6144	0.6158	0.6158	0.6158	0.6158	0.6172	0.6158	0.6172
0.3339	10.5	3700	1.4025	0.6263	0.6264	0.6263	0.6264	0.6264	0.6264	0.6264	0.6296	0.6264	0.6296
0.367	10.78	3800	1.3871	0.6108	0.6115	0.6108	0.6115	0.6115	0.6115	0.6115	0.6135	0.6115	0.6135
0.3307	11.06	3900	1.3996	0.6170	0.6179	0.6170	0.6179	0.6179	0.6179	0.6179	0.6181	0.6179	0.6181
0.3188	11.35	4000	1.4383	0.6251	0.6271	0.6251	0.6271	0.6271	0.6271	0.6271	0.6252	0.6271	0.6252
0.3129	11.63	4100	1.4338	0.6209	0.6214	0.6209	0.6214	0.6214	0.6214	0.6214	0.6217	0.6214	0.6217
0.3112	11.91	4200	1.4456	0.6275	0.6278	0.6275	0.6278	0.6278	0.6278	0.6278	0.6296	0.6278	0.6296

Framework versions

Transformers 4.30.2
Pytorch 2.0.0
Datasets 2.1.0
Tokenizers 0.13.3

byc3230/hubert-large-korean-finetuned-korspeech-ser2

hubert-large-korean-finetuned-korspeech-ser2

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

byc3230/
hubert-large-korean-finetuned-korspeech-ser2