Training Loss	Epoch	Step	Validation Loss
11.0601	0.02	200	1.8848
1.8408	0.05	400	1.7685
1.7219	0.07	600	1.6998
1.7133	0.09	800	1.6720
1.6776	0.12	1000	1.6420
1.6309	0.14	1200	1.7187
1.6157	0.16	1400	1.6025
1.5546	0.18	1600	1.5661
1.4834	0.21	1800	1.5589
1.5641	0.23	2000	1.5451
1.5133	0.25	2200	1.5195
1.5373	0.28	2400	1.5099
1.498	0.3	2600	1.5026
1.4382	0.32	2800	1.4915
1.4585	0.35	3000	1.4937
1.4493	0.37	3200	1.4737
1.403	0.39	3400	1.4713
1.4216	0.42	3600	1.4573
1.4204	0.44	3800	1.4684
1.5143	0.46	4000	1.4458
1.5003	0.48	4200	1.4115
1.4828	0.51	4400	1.4446
1.4098	0.53	4600	1.4133
1.4208	0.55	4800	1.4178
1.401	0.58	5000	1.3915
1.3639	0.6	5200	1.4326
1.3752	0.62	5400	1.3989
1.4016	0.65	5600	1.3873
1.4157	0.67	5800	1.3792
1.4421	0.69	6000	1.3809
1.4024	0.72	6200	1.3780
1.4031	0.74	6400	1.4014
1.4033	0.76	6600	1.4148
1.4009	0.78	6800	1.3824
1.4519	0.81	7000	1.3795
1.377	0.83	7200	1.3762
1.4153	0.85	7400	1.3608
1.4112	0.88	7600	1.3853
1.409	0.9	7800	1.3728
1.4125	0.92	8000	1.3661
1.3637	0.95	8200	1.3609
1.3902	0.97	8400	1.3591
1.4463	0.99	8600	1.3665
1.3782	1.02	8800	1.3634
1.3468	1.04	9000	1.3728
1.3339	1.06	9200	1.3712
1.3171	1.09	9400	1.3557
1.357	1.11	9600	1.3723
1.3791	1.13	9800	1.3617
1.3888	1.15	10000	1.3477
1.3923	1.18	10200	1.3512
1.342	1.2	10400	1.3538
1.3485	1.22	10600	1.3595
1.3523	1.25	10800	1.3623
1.3881	1.27	11000	1.3416
1.3741	1.29	11200	1.3523
1.3869	1.32	11400	1.3442
1.3545	1.34	11600	1.3490
1.3571	1.36	11800	1.3491
1.3396	1.39	12000	1.3510
1.3713	1.41	12200	1.3341
1.3165	1.43	12400	1.3376
1.3236	1.45	12600	1.3364
1.3028	1.48	12800	1.3322
1.3671	1.5	13000	1.3403
1.3295	1.52	13200	1.3377
1.3807	1.55	13400	1.3264
1.3714	1.57	13600	1.3271
1.3249	1.59	13800	1.3388
1.3656	1.62	14000	1.3319
1.2864	1.64	14200	1.3321
1.352	1.66	14400	1.3497
1.3599	1.69	14600	1.3268
1.3191	1.71	14800	1.3339
1.3136	1.73	15000	1.3336
1.3338	1.75	15200	1.3265
1.3528	1.78	15400	1.3363
1.3538	1.8	15600	1.3196
1.2879	1.82	15800	1.3335
1.3217	1.85	16000	1.3376
1.3657	1.87	16200	1.3257
1.3351	1.89	16400	1.3262
1.3469	1.92	16600	1.3299
1.3053	1.94	16800	1.3329
1.3332	1.96	17000	1.3212
1.3466	1.99	17200	1.3317
1.3743	2.01	17400	1.3302
1.3227	2.03	17600	1.3332
1.2728	2.05	17800	1.3450
1.3239	2.08	18000	1.3414
1.3661	2.1	18200	1.3243
1.298	2.12	18400	1.3315
1.2974	2.15	18600	1.3310
1.3174	2.17	18800	1.3224
1.3121	2.19	19000	1.3233
1.3527	2.22	19200	1.3211
1.3712	2.24	19400	1.3143
1.2873	2.26	19600	1.3302
1.306	2.29	19800	1.3211
1.3161	2.31	20000	1.3242
1.308	2.33	20200	1.3176
1.3403	2.35	20400	1.3143
1.3688	2.38	20600	1.3195
1.2743	2.4	20800	1.3230
1.2892	2.42	21000	1.3287
1.3782	2.45	21200	1.3137
1.3331	2.47	21400	1.3148
1.3182	2.49	21600	1.3220
1.2542	2.52	21800	1.3332
1.2879	2.54	22000	1.3229
1.316	2.56	22200	1.3181
1.2989	2.59	22400	1.3155
1.3095	2.61	22600	1.3218
1.2457	2.63	22800	1.3185
1.3053	2.65	23000	1.3168
1.3036	2.68	23200	1.3180
1.2861	2.7	23400	1.3117
1.3	2.72	23600	1.3208
1.3026	2.75	23800	1.3147
1.3006	2.77	24000	1.3211
1.3477	2.79	24200	1.3140
1.2851	2.82	24400	1.3208
1.2859	2.84	24600	1.3172
1.3286	2.86	24800	1.3151
1.3237	2.89	25000	1.3148
1.3503	2.91	25200	1.3133
1.27	2.93	25400	1.3138
1.2998	2.96	25600	1.3151
1.3461	2.98	25800	1.3143

Framework versions

Transformers 4.35.2
Pytorch 2.1.1+cu118
Datasets 2.15.0
Tokenizers 0.15.0

Taeyeun72/GPT2_V2

gpt2-cs00

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

Taeyeun72/
GPT2_V2