Training Loss	Epoch	Step	Validation Loss	Cer
68.3088	0.03	100	125.2977	1.0
42.6071	0.07	200	75.7330	1.0
25.0826	0.1	300	35.9474	1.0
10.1653	0.14	400	7.3490	1.0
4.9322	0.17	500	5.3703	1.0
4.7973	0.21	600	5.2150	1.0
4.7712	0.24	700	5.1904	1.0
4.7076	0.28	800	5.0821	1.0
4.6923	0.31	900	4.9610	1.0
4.6677	0.34	1000	4.9497	1.0
4.7168	0.38	1100	5.0000	1.0
4.632	0.41	1200	4.9656	1.0
4.6183	0.45	1300	4.8375	1.0
4.5395	0.48	1400	4.6795	1.0
4.5385	0.52	1500	4.7110	1.0
4.5216	0.55	1600	4.6153	1.0
4.4442	0.59	1700	4.5397	1.0
4.3119	0.62	1800	4.2205	1.0
4.0832	0.66	1900	3.8678	0.9388
3.7389	0.69	2000	3.3075	0.6898
3.2707	0.72	2100	2.9589	0.5771
3.0099	0.76	2200	2.6719	0.5310
2.8581	0.79	2300	2.5290	0.5090
2.677	0.83	2400	2.3971	0.4737
2.5064	0.86	2500	2.2446	0.4643
2.4691	0.9	2600	2.1015	0.4373
2.2864	0.93	2700	2.0987	0.4290
2.2526	0.97	2800	2.0082	0.4223
2.156	1.0	2900	1.9078	0.4063
2.1556	1.03	3000	1.8029	0.3970
1.9783	1.07	3100	1.7724	0.3876
1.9678	1.1	3200	1.7115	0.3705
1.9242	1.14	3300	1.6834	0.3634
1.8216	1.17	3400	1.6559	0.3532
1.7855	1.21	3500	1.6106	0.3556
1.8123	1.24	3600	1.6309	0.3465
1.7609	1.28	3700	1.5353	0.3403
1.7131	1.31	3800	1.5067	0.3320
1.6954	1.35	3900	1.4273	0.3228
1.6219	1.38	4000	1.3992	0.3198
1.5606	1.41	4100	1.4247	0.3164
1.6549	1.45	4200	1.3775	0.3135
1.5869	1.48	4300	1.3162	0.3043
1.531	1.52	4400	1.2849	0.2980
1.4833	1.55	4500	1.3072	0.2989
1.4852	1.59	4600	1.2669	0.2984
1.4379	1.62	4700	1.2259	0.2897
1.4085	1.66	4800	1.2219	0.2828
1.4005	1.69	4900	1.1980	0.2791
1.3868	1.72	5000	1.2399	0.2874
1.3646	1.76	5100	1.2098	0.2829
1.3728	1.79	5200	1.2053	0.2779
1.2867	1.83	5300	1.1602	0.2737
1.3263	1.86	5400	1.1363	0.2650
1.2914	1.9	5500	1.1249	0.2611
1.2629	1.93	5600	1.0774	0.2559
1.2031	1.97	5700	1.1048	0.2570
1.2491	2.0	5800	1.0966	0.2655
1.159	2.04	5900	1.0593	0.2566
1.134	2.07	6000	1.0350	0.2475
1.1207	2.1	6100	1.0544	0.2455
1.116	2.14	6200	1.0340	0.2470
1.0947	2.17	6300	1.0177	0.2431
1.0844	2.21	6400	1.0166	0.2394
1.0679	2.24	6500	1.0666	0.2457
1.1139	2.28	6600	0.9607	0.2333
1.1074	2.31	6700	0.9982	0.2315
1.0263	2.35	6800	0.9937	0.2326
1.0264	2.38	6900	0.9521	0.2294
0.999	2.41	7000	0.9542	0.2306
1.0688	2.45	7100	0.9294	0.2251
1.0357	2.48	7200	0.9602	0.2231
1.0218	2.52	7300	0.9285	0.2264
0.9932	2.55	7400	0.9392	0.2224
1.0133	2.59	7500	0.9092	0.2225
1.0369	2.62	7600	0.9226	0.2185
0.9927	2.66	7700	0.9695	0.2236
1.0042	2.69	7800	0.9115	0.2200
0.9954	2.73	7900	0.8979	0.2151
0.9775	2.76	8000	0.9016	0.2161
0.9078	2.79	8100	0.9009	0.2177
0.9196	2.83	8200	0.9006	0.2149
0.9177	2.86	8300	0.8777	0.2125
0.8992	2.9	8400	0.8889	0.2097
0.911	2.93	8500	0.8693	0.2087
0.888	2.97	8600	0.8735	0.2134
0.9566	3.0	8700	0.8586	0.2078
0.8704	3.04	8800	0.8686	0.2079
0.8203	3.07	8900	0.8537	0.2064
0.8425	3.1	9000	0.8827	0.2065
0.8615	3.14	9100	0.8392	0.2030
0.8364	3.17	9200	0.8474	0.2041
0.8011	3.21	9300	0.8441	0.2031
0.8624	3.24	9400	0.8491	0.2046
0.8525	3.28	9500	0.8359	0.1995
0.8235	3.31	9600	0.8370	0.2008
0.8155	3.35	9700	0.8495	0.2015
0.7683	3.38	9800	0.8514	0.2014
0.8514	3.41	9900	0.8179	0.1981
0.8574	3.45	10000	0.8201	0.1992
0.819	3.48	10100	0.8437	0.1998
0.8078	3.52	10200	0.8292	0.1984
0.7888	3.55	10300	0.8253	0.1971
0.8105	3.59	10400	0.8180	0.1961
0.8006	3.62	10500	0.8111	0.1951
0.7583	3.66	10600	0.8265	0.1960
0.8425	3.69	10700	0.8248	0.1943
0.8564	3.73	10800	0.8250	0.1953
0.772	3.76	10900	0.8287	0.1970
0.8058	3.79	11000	0.8243	0.1961
0.7974	3.83	11100	0.8162	0.1944
0.7292	3.86	11200	0.8227	0.1950
0.7915	3.9	11300	0.8160	0.1941
0.7891	3.93	11400	0.8193	0.1945
0.7766	3.97	11500	0.8178	0.1942

Framework versions

Transformers 4.35.2
Pytorch 2.1.1+cu121
Datasets 2.13.0
Tokenizers 0.15.0

yoon1000/ft_0117_korean

ft_0117_korean

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

yoon1000/
ft_0117_korean