learning_rate: 5e-05
train_batch_size: 8
eval_batch_size: 16
seed: 42
distributed_type: multi-GPU
num_devices: 2
total_train_batch_size: 16
total_eval_batch_size: 32
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: linear
lr_scheduler_warmup_steps: 500
num_epochs: 10
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Accuracy	F1	Validation Loss	Precision	Recall
1.757	0.0356	1000	0.1934	0.1444	1.8080	0.3630	0.1934
1.7207	0.0711	2000	0.1858	0.1167	1.8231	0.4510	0.1858
1.7191	0.1067	3000	0.2319	0.1956	1.7802	0.3031	0.2319
1.6801	0.1422	4000	0.2710	0.2483	1.7571	0.3163	0.2710
1.6729	0.1778	5000	0.316	0.3071	1.7127	0.3274	0.316
1.6273	0.2133	6000	0.2663	0.2278	1.7038	0.3393	0.2663
1.638	0.2489	7000	0.3340	0.2975	1.6556	0.3365	0.3340
1.6088	0.2844	8000	0.3467	0.3030	1.6232	0.3529	0.3467
1.6045	0.32	9000	0.3678	0.3467	1.6154	0.3719	0.3678
1.5529	0.3889	10000	0.3898	0.3557	1.5715	0.3820	0.3898
1.5729	0.4278	11000	0.3882	0.3649	1.5619	0.4034	0.3882
1.5647	0.4667	12000	0.4043	0.3773	1.5250	0.4066	0.4043
1.5344	0.5056	13000	0.4231	0.3957	1.5101	0.4251	0.4231
1.558	0.5445	14000	0.4288	0.4052	1.4953	0.4249	0.4288
1.5119	0.5834	15000	0.4318	0.4108	1.4901	0.4326	0.4318
1.53	0.6223	16000	0.4374	0.4203	1.4725	0.4316	0.4374
1.5029	0.6611	17000	0.4375	0.4130	1.4610	0.4317	0.4375
1.5406	0.7000	18000	0.4470	0.4341	1.4421	0.4589	0.4470
1.4774	0.7389	19000	0.4537	0.4282	1.4335	0.4697	0.4537
1.5911	0.7778	20000	0.4617	0.4440	1.4154	0.4506	0.4617
1.5075	0.8167	21000	0.4367	0.4043	1.4382	0.4717	0.4367
1.4361	0.8556	22000	0.4542	0.4433	1.4165	0.4565	0.4542
1.5074	0.8945	23000	0.4397	0.4216	1.4402	0.4570	0.4397
1.5422	0.9334	24000	0.4324	0.4164	1.4387	0.4636	0.4324
1.504	0.9723	25000	0.4691	0.4573	1.3951	0.4829	0.4691
1.589	1.0112	26000	0.4568	0.4396	1.4080	0.4792	0.4568
1.5463	1.0501	27000	0.4763	0.4612	1.3758	0.4912	0.4763
1.5442	1.0889	28000	0.4810	0.4603	1.3749	0.5010	0.4810
1.5678	1.1278	29000	0.4821	0.4679	1.3573	0.4898	0.4821
1.4957	1.1667	30000	0.4773	0.4531	1.3754	0.4864	0.4773
1.4619	1.2056	31000	0.4583	0.4333	1.4045	0.4852	0.4583
1.5267	1.2445	32000	0.4830	0.4659	1.3626	0.4797	0.4830
1.4861	1.2834	33000	0.4753	0.4560	1.3709	0.4818	0.4753
1.532	1.3223	34000	0.4689	0.4318	1.3816	0.4647	0.4689
1.5705	1.3612	35000	0.4840	0.4597	1.3663	0.4826	0.4840
1.4912	1.4001	36000	0.4854	0.4635	1.3536	0.4973	0.4854
1.4966	1.4390	37000	0.4909	0.4702	1.3497	0.4884	0.4909
1.4327	1.4779	38000	0.4800	0.4685	1.3592	0.4885	0.4800
1.5454	1.5167	39000	0.5042	0.4773	1.3186	0.5126	0.5042
1.4842	1.5556	40000	0.5018	0.4860	1.3254	0.5038	0.5018
1.4606	1.5945	41000	0.4928	0.4627	1.3411	0.5006	0.4928
1.4117	1.6334	42000	0.5009	0.4915	1.3106	0.5220	0.5009
1.4794	1.6723	43000	0.5002	0.4821	1.3182	0.5228	0.5002
1.5223	1.7112	44000	0.5027	0.4897	1.3102	0.5135	0.5027
1.5187	1.7501	45000	0.5134	0.4991	1.2922	0.5090	0.5134
1.6064	1.7890	46000	0.5105	0.4938	1.2987	0.5039	0.5105
1.5322	1.8279	47000	0.5081	0.4831	1.3015	0.4997	0.5081
1.4831	1.8668	48000	0.4918	0.4704	1.3280	0.5077	0.4918
1.4726	1.9057	49000	0.5011	0.4822	1.3042	0.5145	0.5011
1.5298	1.9445	50000	0.5162	0.5028	1.2816	0.5206	0.5162
1.559	1.9834	51000	0.5133	0.4969	1.2905	0.5131	0.5133
1.5835	2.0223	52000	0.5198	0.5097	1.2741	0.5248	0.5198
1.5087	2.0612	53000	0.5125	0.5040	1.2828	0.5206	0.5125
1.4915	2.1001	54000	0.5115	0.4952	1.2897	0.5185	0.5115
1.482	2.1390	55000	0.5138	0.5024	1.2792	0.5219	0.5138
1.5485	2.1779	56000	0.5181	0.5036	1.2789	0.5282	0.5181
1.5636	2.2168	57000	0.5151	0.5005	1.2838	0.5257	0.5151
1.4106	2.2557	58000	0.5132	0.4920	1.2850	0.5161	0.5132
1.4449	2.2946	59000	0.503	0.4772	1.3000	0.5147	0.503
1.4786	2.3335	60000	0.5203	0.5043	1.2671	0.5432	0.5203
1.4684	2.3723	61000	0.5206	0.5091	1.2671	0.5356	0.5206
1.4268	2.4112	62000	0.5223	0.5089	1.2658	0.5269	0.5223
1.4774	2.4501	63000	0.5296	0.5181	1.2524	0.5371	0.5296
1.4325	2.4890	64000	0.5202	0.5059	1.2673	0.5250	0.5202
1.5087	2.5279	65000	0.4971	0.4755	1.3084	0.5250	0.4971
1.4453	2.5668	66000	0.5123	0.5017	1.2858	0.5276	0.5123
1.476	2.6057	67000	0.5233	0.5089	1.2626	0.5223	0.5233
1.4795	2.6446	68000	0.5159	0.4972	1.2777	0.5278	0.5159
1.4468	2.6835	69000	0.5299	0.5126	1.2504	0.5283	0.5299
1.4137	2.7224	70000	0.5290	0.5176	1.2511	0.5377	0.5289
1.5105	2.7612	71000	0.5383	0.5298	1.2342	0.5430	0.5383
1.4906	2.8001	72000	0.5271	0.5137	1.2550	0.5295	0.5271
1.4464	2.8390	73000	0.5273	0.5118	1.2512	0.5384	0.5273
1.6306	2.8779	74000	0.5300	0.5160	1.2466	0.5320	0.5300
1.4965	2.9168	75000	0.5222	0.5078	1.2595	0.5358	0.5222
1.4079	2.9557	76000	0.5227	0.5092	1.2536	0.5231	0.5227
1.448	2.9946	77000	0.5230	0.4991	1.2700	0.5295	0.5230
1.6561	3.0335	78000	0.5348	0.5200	1.2381	0.5237	0.5348
1.5103	3.0724	79000	0.5334	0.5216	1.2393	0.5451	0.5334
1.5148	3.1113	80000	0.5307	0.5091	1.2489	0.5474	0.5307
1.4129	3.1502	81000	0.5379	0.5238	1.2319	0.5292	0.5379
1.6654	3.1890	82000	0.5335	0.5165	1.2415	0.5372	0.5335
1.4226	3.2279	83000	0.5336	0.5210	1.2343	0.5478	0.5336
1.3913	3.2668	84000	0.5381	0.5251	1.2317	0.5344	0.5381
1.4628	3.3057	85000	0.5240	0.5142	1.2496	0.5327	0.5240
1.3775	3.3446	86000	0.5305	0.5159	1.2400	0.5383	0.5305
1.4292	3.3835	87000	0.5140	0.4945	1.2727	0.5329	0.5140
1.5157	3.4224	88000	0.5243	0.5146	1.2419	0.5502	0.5243
1.4581	3.4613	89000	0.5318	0.5245	1.2296	0.5524	0.5318
1.3873	3.5002	90000	0.5314	0.5211	1.2380	0.5436	0.5314
1.425	3.5391	91000	0.5371	0.5242	1.2300	0.5420	0.5371
1.4202	3.5780	92000	0.5430	0.5282	1.2211	0.5475	0.5430
1.4748	3.6168	93000	0.5407	0.5273	1.2256	0.5422	0.5407
1.4289	3.6557	94000	0.5351	0.5230	1.2293	0.5426	0.5351
1.4312	3.6946	95000	0.5405	0.5314	1.2180	0.5483	0.5405
1.4342	3.7335	96000	0.5256	0.5085	1.2435	0.5420	0.5256
1.8241	3.7724	97000	0.5335	0.5138	1.2389	0.5384	0.5335
1.4589	3.8113	98000	0.5222	0.5070	1.2484	0.5458	0.5222
1.4884	3.8502	99000	0.5231	0.4996	1.2610	0.5311	0.5231
1.5725	3.8891	100000	0.5468	0.5383	1.2074	0.5456	0.5468
1.4603	3.9280	101000	0.5409	0.5261	1.2154	0.5471	0.5409
1.4581	3.9669	102000	0.5365	0.5221	1.2234	0.5352	0.5365
1.5738	4.0058	103000	0.5339	0.5205	1.2247	0.5445	0.5339
1.593	4.0446	104000	0.5210	0.5067	1.2527	0.5370	0.5210
1.4523	4.0835	105000	0.5456	0.5261	1.2102	0.5411	0.5456
1.5537	4.1224	106000	0.5341	0.5155	1.2337	0.5334	0.5341
1.4931	4.1613	107000	0.5437	0.5336	1.2114	0.5461	0.5437
1.4286	4.2002	108000	0.5153	0.4956	1.2611	0.5458	0.5153
1.3667	4.2391	109000	0.5439	0.5301	1.2108	0.5463	0.5439
1.4723	4.2780	110000	0.5312	0.5213	1.2269	0.5497	0.5312
1.3852	4.3169	111000	0.5452	0.5290	1.2128	0.5488	0.5452
1.489	4.3558	112000	0.5419	0.5310	1.2094	0.5471	0.5419
1.4598	4.3947	113000	0.5356	0.5246	1.2183	0.5418	0.5356
1.5491	4.4336	114000	0.5438	0.5372	1.2062	0.5535	0.5438
1.3628	4.4724	115000	0.5381	0.5281	1.2204	0.5430	0.5381
1.5225	4.5113	116000	0.5362	0.5289	1.2174	0.5573	0.5362
1.4036	4.5502	117000	0.5440	0.5316	1.2084	0.5547	0.5440
1.4956	4.5891	118000	0.5319	0.5173	1.2280	0.5358	0.5318
1.3991	4.6280	119000	0.5453	0.5338	1.2046	0.5494	0.5453
1.5407	4.6669	120000	0.5428	0.5289	1.2093	0.5503	0.5428
1.5033	4.7058	121000	0.5403	0.5237	1.2122	0.5630	0.5403
1.5966	4.7447	122000	0.5536	0.5462	1.1886	0.5590	0.5536
1.637	2.3918	123000	0.5389	0.5157	1.2282	0.5438	0.5389
1.5217	2.4113	124000	0.5488	0.5427	1.2010	0.5442	0.5488
1.6031	2.4307	125000	0.5400	0.5277	1.2237	0.5371	0.5400
1.4542	2.4502	126000	0.5434	0.5308	1.2101	0.5586	0.5434
1.5071	2.4696	127000	0.5429	0.5279	1.2116	0.5501	0.5429
1.5437	2.4891	128000	0.5383	0.5256	1.2150	0.5487	0.5383
1.4489	2.5085	129000	0.5129	0.5063	1.2566	0.5528	0.5129
1.5495	2.5280	130000	0.5532	0.5427	1.1922	0.5581	0.5532
1.4348	2.5474	131000	0.5432	0.5375	1.2032	0.5510	0.5432
1.4554	2.5668	132000	0.5383	0.5252	1.2144	0.5638	0.5383
1.4183	2.5863	133000	0.5335	0.5256	1.2202	0.5482	0.5335
1.4754	2.6057	134000	0.5470	0.5437	1.1988	0.5537	0.5470
1.5864	2.6252	135000	0.5426	0.5348	1.2015	0.5414	0.5426
1.3715	2.6446	136000	0.5287	0.5136	1.2306	0.5495	0.5287
1.3886	2.6641	137000	0.5445	0.5323	1.2043	0.5478	0.5445
1.4509	2.6835	138000	0.5438	0.5276	1.2045	0.5602	0.5438
1.4868	2.7030	139000	0.5233	0.5097	1.2468	0.5385	0.5233
1.4345	2.7224	140000	0.5456	0.5312	1.2123	0.5404	0.5456
1.3935	2.7419	141000	0.5441	0.5321	1.2061	0.5428	0.5441
1.5243	2.7613	142000	0.5530	0.5402	1.1959	0.5437	0.5530
1.5884	2.8196	145000	0.5519	0.5430	1.1936	0.5581	0.5519
1.4449	2.9169	150000	0.5288	0.5164	1.2338	0.5527	0.5288
1.4557	3.0141	155000	0.5561	0.5428	1.1910	0.5568	0.5561
1.6852	3.1113	160000	0.5564	0.5497	1.1852	0.5597	0.5564
1.4623	3.2086	165000	0.5598	0.5557	1.1777	0.5623	0.5598
1.4993	3.3058	170000	1.2342	0.5299	0.5362	0.5299	0.5146
1.466	3.4030	175000	1.1878	0.5499	0.5656	0.5499	0.5471
1.409	3.5002	180000	1.2197	0.5333	0.5568	0.5333	0.5278
1.4949	3.5975	185000	1.1775	0.5617	0.5611	0.5618	0.5408
1.4764	3.6947	190000	1.2087	0.5430	0.5455	0.5430	0.5266
1.4751	3.7919	195000	1.2000	0.5462	0.5690	0.5462	0.5353
1.5135	3.8892	200000	1.2028	0.5468	0.5500	0.5468	0.5348
1.3999	3.9864	205000	1.1762	0.5622	0.5630	0.5622	0.5517
1.4685	4.0836	210000	1.1819	0.5550	0.5632	0.5550	0.5405
1.4338	4.1808	215000	1.1992	0.5498	0.5569	0.5498	0.5354
1.6445	4.2781	220000	1.2039	0.5424	0.5603	0.5424	0.5297
1.4788	4.3753	225000	1.1930	0.5549	0.5525	0.5549	0.5458
1.3937	4.4725	230000	1.1762	0.5571	0.5552	0.5571	0.5509
1.3932	4.5698	235000	1.2016	0.5471	0.5523	0.5471	0.5338
1.5177	4.6670	240000	1.1786	0.5577	0.5666	0.5577	0.5449
1.5508	4.7642	245000	1.1772	0.5540	0.5826	0.5540	0.5521
1.4184	4.8614	250000	1.1773	0.5581	0.5682	0.5581	0.5455
1.5349	4.9587	255000	1.1828	0.5581	0.5663	0.5581	0.5440
1.4414	5.0559	260000	1.1804	0.5536	0.5699	0.5536	0.5437
1.4374	5.1531	265000	1.1910	0.5525	0.5576	0.5525	0.5356
1.4101	5.2504	270000	1.1854	0.5548	0.5648	0.5548	0.5427
1.6934	5.3476	275000	1.2125	0.5399	0.5599	0.5399	0.5184
1.4133	5.4448	280000	1.1745	0.5591	0.5694	0.5591	0.5487
1.5981	5.5421	285000	1.2078	0.5391	0.5644	0.5391	0.5317
1.4194	5.6393	290000	1.1834	0.5507	0.5654	0.5507	0.5414
1.5619	5.7365	295000	1.1951	0.5485	0.5685	0.5485	0.5356
1.4517	5.8337	300000	1.1835	0.5570	0.5696	0.5570	0.5360
1.5457	5.9310	305000	1.1635	0.5617	0.5738	0.5618	0.5530
1.4769	6.0282	310000	1.1636	0.5633	0.5670	0.5633	0.5565
1.3975	6.1254	315000	1.1785	0.5596	0.5684	0.5596	0.5443
1.6069	6.2227	320000	1.1685	0.5634	0.5632	0.5634	0.5541
1.3608	6.3199	325000	1.1589	0.5673	0.5600	0.5673	0.5581
1.5021	6.4171	330000	1.1799	0.5576	0.5561	0.5576	0.5435
1.6022	6.5143	335000	1.1722	0.5617	0.5579	0.5617	0.5504
1.5354	6.6116	340000	1.1631	0.5644	0.5668	0.5644	0.5541
1.4264	6.7088	345000	1.1693	0.5626	0.5640	0.5626	0.5484
1.5207	6.8060	350000	1.1781	0.5583	0.5668	0.5583	0.5401
1.441	6.9033	355000	1.1746	0.5581	0.5666	0.5581	0.5496
1.33	7.0005	360000	1.1605	0.5677	0.5721	0.5677	0.5574
1.5886	7.0977	365000	1.1649	0.5657	0.5711	0.5657	0.5523
1.5005	7.1949	370000	1.1872	0.5523	0.5644	0.5523	0.5384
1.4685	7.2922	375000	1.1735	0.5607	0.5671	0.5607	0.5451
1.373	7.3894	380000	1.1597	0.5652	0.5726	0.5652	0.5557
1.5504	7.4866	385000	1.1803	0.5518	0.5732	0.5518	0.5413
1.4173	7.5839	390000	1.1709	0.5601	0.5660	0.5601	0.5455
1.4251	7.6811	395000	1.1607	0.5674	0.5710	0.5674	0.5574
1.6129	7.7783	400000	1.1831	0.5530	0.5610	0.5530	0.5418
1.4331	7.8755	405000	1.1715	0.5626	0.5645	0.5626	0.5488
1.5966	7.9728	410000	1.1825	0.5592	0.5623	0.5592	0.5411
1.3413	8.0700	415000	1.1705	0.5585	0.5687	0.5585	0.5486
1.3785	8.1672	420000	1.1576	0.5692	0.5656	0.5692	0.5568
1.5491	8.2645	425000	1.1627	0.5665	0.5671	0.5665	0.5515
1.3878	8.3617	430000	1.1688	0.5607	0.5712	0.5607	0.5497
1.415	8.4589	435000	1.1801	0.5546	0.5650	0.5546	0.5423
1.3973	8.5561	440000	1.1650	0.5612	0.5712	0.5612	0.5538
1.3801	8.6534	445000	1.1671	0.5655	0.5665	0.5655	0.5525
1.4631	8.7506	450000	1.1839	0.5552	0.5631	0.5552	0.5414
1.4076	8.8478	455000	1.1725	0.5604	0.5668	0.5604	0.5452
1.6888	8.9451	460000	1.1622	0.5642	0.5732	0.5642	0.5533
1.4282	9.0423	465000	1.1566	0.5682	0.5726	0.5682	0.5579
1.4833	9.1395	470000	1.1658	0.5635	0.5725	0.5635	0.5526
1.5365	9.2368	475000	1.1589	0.5684	0.5687	0.5684	0.5567
1.3789	9.3340	480000	1.1688	0.5616	0.5678	0.5616	0.5489
1.3586	9.4312	485000	1.1796	0.5547	0.5646	0.5547	0.5427
1.4582	9.5284	490000	1.1725	0.5606	0.5635	0.5606	0.5485
1.439	9.6257	495000	1.1643	0.5649	0.5700	0.5650	0.5534
1.4671	9.7229	500000	1.1688	0.5617	0.5667	0.5617	0.5495
1.4149	9.8201	505000	1.1640	0.5652	0.5662	0.5652	0.5535
1.5227	9.9174	510000	1.1634	0.5646	0.5686	0.5646	0.5531

Framework versions

Transformers 4.48.0
Pytorch 2.2.1+cu121
Datasets 2.18.0
Tokenizers 0.21.0

HERIUN/wav2vec-bert-korean-dialect-recognition

wav2vec-bert-korean-dialect-recognition

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

HERIUN/
wav2vec-bert-korean-dialect-recognition