hubert-large-korean-finetuned-korspeech-ser2

This model is a fine-tuned version of team-lucid/hubert-large-korean on the None dataset. It achieves the following results on the evaluation set:

• Loss: 1.4456
• Macro F1: 0.6275
• Accuracy: 0.6278
• Weighted f1: 0.6275
• Micro f1: 0.6278
• Weighted recall: 0.6278
• Micro recall: 0.6278
• Macro recall: 0.6278
• Weighted precision: 0.6296
• Micro precision: 0.6278
• Macro precision: 0.6296

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: 0.0001
• train_batch_size: 8
• eval_batch_size: 8
• seed: 42
• gradient_accumulation_steps: 4
• total_train_batch_size: 32
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• lr_scheduler_warmup_ratio: 0.1
• lr_scheduler_warmup_steps: 100
• num_epochs: 12

Training results

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