Training Loss	Epoch	Step	Validation Loss	Accuracy	F1
0.0485	0.12	500	0.3564	0.9360	0.9263
0.2109	0.24	1000	0.2069	0.9438	0.9315
0.1862	0.36	1500	0.1709	0.9437	0.9301
0.1759	0.48	2000	0.1809	0.9449	0.9372
0.176	0.6	2500	0.1710	0.9408	0.9336
0.1736	0.72	3000	0.1610	0.9447	0.9412
0.1668	0.84	3500	0.1739	0.9454	0.9359
0.169	0.96	4000	0.1725	0.9449	0.9358
0.1419	1.08	4500	0.2007	0.9471	0.9413
0.1283	1.2	5000	0.2220	0.9465	0.9381
0.1308	1.32	5500	0.1991	0.9471	0.9408
0.1417	1.44	6000	0.1752	0.9474	0.9420
0.121	1.57	6500	0.1917	0.9485	0.9423
0.1392	1.69	7000	0.1842	0.9486	0.9421
0.1256	1.81	7500	0.2031	0.9476	0.9382
0.1447	1.93	8000	0.1784	0.9459	0.9392
0.1138	2.05	8500	0.2106	0.9449	0.9425
0.088	2.17	9000	0.2321	0.9477	0.9405
0.0971	2.29	9500	0.2160	0.9449	0.9405
0.0923	2.41	10000	0.2561	0.9474	0.9413
0.0947	2.53	10500	0.2355	0.9472	0.9438
0.0832	2.65	11000	0.2683	0.9465	0.9409
0.0936	2.77	11500	0.2640	0.9439	0.9411
0.0968	2.89	12000	0.2642	0.9481	0.9378
0.0965	3.01	12500	0.2619	0.9443	0.9394
0.0446	3.13	13000	0.3138	0.9381	0.9390
0.0527	3.25	13500	0.3145	0.9415	0.9393
0.0528	3.37	14000	0.3099	0.9453	0.9413
0.0583	3.49	14500	0.3114	0.9464	0.9384
0.0593	3.61	15000	0.3153	0.9449	0.9415
0.0661	3.73	15500	0.3373	0.9404	0.9383
0.0572	3.85	16000	0.3282	0.9418	0.9404
0.063	3.97	16500	0.3098	0.9443	0.9388
0.0315	4.09	17000	0.3851	0.9393	0.9390
0.0275	4.21	17500	0.3971	0.9409	0.9391
0.035	4.33	18000	0.4076	0.9426	0.9388
0.0413	4.45	18500	0.3752	0.9409	0.9404
0.0345	4.58	19000	0.3747	0.9449	0.9419
0.0421	4.7	19500	0.3612	0.9443	0.9420
0.0383	4.82	20000	0.3918	0.9458	0.9415
0.0363	4.94	20500	0.3815	0.9404	0.9403
0.032	5.06	21000	0.3890	0.9455	0.9431
0.0165	5.18	21500	0.4314	0.9455	0.9417
0.0226	5.3	22000	0.4210	0.9463	0.9409
0.0211	5.42	22500	0.4091	0.9461	0.9418
0.0186	5.54	23000	0.4210	0.9436	0.9405
0.0217	5.66	23500	0.4197	0.9446	0.9410
0.0252	5.78	24000	0.4054	0.9423	0.9414
0.0203	5.9	24500	0.4233	0.9447	0.9410
0.0197	6.02	25000	0.4844	0.9418	0.9396
0.0085	6.14	25500	0.4883	0.9422	0.9411
0.0147	6.26	26000	0.5028	0.9381	0.9384
0.0169	6.38	26500	0.4833	0.9390	0.9384
0.0133	6.5	27000	0.4980	0.9450	0.9383
0.0147	6.62	27500	0.4710	0.9406	0.9389
0.0119	6.74	28000	0.4898	0.9392	0.9378
0.0191	6.86	28500	0.4502	0.9425	0.9393
0.0172	6.98	29000	0.4568	0.9444	0.9403
0.012	7.1	29500	0.4680	0.9452	0.9409
0.007	7.22	30000	0.4756	0.9445	0.9404
0.0059	7.34	30500	0.4892	0.9440	0.9403
0.0123	7.46	31000	0.4672	0.9449	0.9401
0.0061	7.58	31500	0.4901	0.9447	0.9415
0.0088	7.71	32000	0.5093	0.9435	0.9409
0.0108	7.83	32500	0.4955	0.9445	0.9415
0.0061	7.95	33000	0.5107	0.9458	0.9423
0.0033	8.07	33500	0.5209	0.9452	0.9428
0.0044	8.19	34000	0.5307	0.9455	0.9411
0.0048	8.31	34500	0.5211	0.9450	0.9410
0.0039	8.43	35000	0.5213	0.9449	0.9417
0.0023	8.55	35500	0.5323	0.9461	0.9403
0.0071	8.67	36000	0.5250	0.9453	0.9421
0.005	8.79	36500	0.5237	0.9464	0.9408
0.0033	8.91	37000	0.5247	0.9435	0.9404
0.0048	9.03	37500	0.5304	0.9462	0.9411
0.0022	9.15	38000	0.5319	0.9458	0.9417
0.0029	9.27	38500	0.5333	0.9438	0.9407
0.0026	9.39	39000	0.5336	0.9459	0.9416
0.0042	9.51	39500	0.5379	0.9451	0.9414
0.0023	9.63	40000	0.5400	0.9456	0.9410
0.0008	9.75	40500	0.5436	0.9453	0.9412
0.0009	9.87	41000	0.5452	0.9449	0.9410
0.0031	9.99	41500	0.5455	0.9445	0.9408

Framework versions

Transformers 4.32.1
Pytorch 2.2.0+cu121
Datasets 2.12.0
Tokenizers 0.13.2

eunyounglee/pogny-persona-emotion-bert-simple-finetuning-1

hana-persona-emotion-bert-finetuning-1

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

eunyounglee/
pogny-persona-emotion-bert-simple-finetuning-1