Training Loss	Epoch	Step	Validation Loss	Accuracy
0.0381	0.13	500	0.0777	0.9745
0.0669	0.26	1000	0.0545	0.9790
0.0595	0.39	1500	0.0545	0.9803
0.054	0.52	2000	0.0555	0.9796
0.0502	0.65	2500	0.0451	0.9828
0.0474	0.78	3000	0.0486	0.9818
0.0458	0.92	3500	0.0417	0.9836
0.0415	1.05	4000	0.0440	0.9827
0.0372	1.18	4500	0.0432	0.9839
0.0391	1.31	5000	0.0442	0.9839
0.0368	1.44	5500	0.0377	0.9856
0.0388	1.57	6000	0.0417	0.9846
0.0351	1.7	6500	0.0363	0.9857
0.0357	1.83	7000	0.0383	0.9858
0.0336	1.96	7500	0.0371	0.9860
0.0309	2.09	8000	0.0373	0.9859
0.0288	2.22	8500	0.0355	0.9870
0.0288	2.35	9000	0.0359	0.9867
0.0285	2.49	9500	0.0369	0.9872
0.0307	2.62	10000	0.0322	0.9880
0.028	2.75	10500	0.0307	0.9886
0.0246	2.88	11000	0.0326	0.9881
0.0267	3.01	11500	0.0346	0.9882
0.022	3.14	12000	0.0316	0.9889
0.0218	3.27	12500	0.0357	0.9883
0.0217	3.4	13000	0.0363	0.9883
0.0208	3.53	13500	0.0340	0.9894
0.0223	3.66	14000	0.0304	0.9892
0.0232	3.79	14500	0.0319	0.9894
0.0229	3.92	15000	0.0307	0.9901
0.0192	4.06	15500	0.0310	0.9905
0.0178	4.19	16000	0.0345	0.9897
0.0178	4.32	16500	0.0309	0.9902
0.0173	4.45	17000	0.0328	0.9904
0.0176	4.58	17500	0.0316	0.9908
0.017	4.71	18000	0.0307	0.9912
0.0163	4.84	18500	0.0329	0.9909
0.018	4.97	19000	0.0295	0.9910
0.0143	5.1	19500	0.0367	0.9903
0.0144	5.23	20000	0.0317	0.9915
0.0158	5.36	20500	0.0290	0.9917
0.0143	5.49	21000	0.0315	0.9917
0.0137	5.63	21500	0.0310	0.9913
0.0135	5.76	22000	0.0310	0.9913
0.0128	5.89	22500	0.0290	0.9917
0.0132	6.02	23000	0.0314	0.9921
0.0124	6.15	23500	0.0274	0.9921
0.0114	6.28	24000	0.0300	0.9921
0.0111	6.41	24500	0.0291	0.9922
0.0109	6.54	25000	0.0307	0.9923
0.0117	6.67	25500	0.0328	0.9921
0.0112	6.8	26000	0.0293	0.9924
0.012	6.93	26500	0.0300	0.9924
0.0102	7.06	27000	0.0330	0.9921
0.0094	7.2	27500	0.0323	0.9922
0.0091	7.33	28000	0.0309	0.9924
0.0087	7.46	28500	0.0331	0.9920
0.0091	7.59	29000	0.0332	0.9923
0.0095	7.72	29500	0.0298	0.9925
0.0083	7.85	30000	0.0303	0.9929
0.0097	7.98	30500	0.0298	0.9928
0.0069	8.11	31000	0.0319	0.9926
0.0086	8.24	31500	0.0314	0.9929
0.0079	8.37	32000	0.0306	0.9929
0.0065	8.5	32500	0.0317	0.9926
0.0072	8.63	33000	0.0307	0.9927
0.0082	8.77	33500	0.0306	0.9929
0.0086	8.9	34000	0.0312	0.9931
0.0079	9.03	34500	0.0329	0.9929
0.0061	9.16	35000	0.0326	0.9928
0.0074	9.29	35500	0.0315	0.9928
0.0068	9.42	36000	0.0310	0.9931
0.0059	9.55	36500	0.0318	0.9930
0.0064	9.68	37000	0.0307	0.9933
0.0063	9.81	37500	0.0308	0.9930
0.0062	9.94	38000	0.0311	0.9931
0.0058	10.07	38500	0.0314	0.9932
0.0051	10.2	39000	0.0316	0.9933
0.0065	10.33	39500	0.0315	0.9933
0.0059	10.47	40000	0.0314	0.9933

Framework versions

Transformers 4.35.2
Pytorch 2.1.0+cu121
Datasets 2.15.0
Tokenizers 0.15.0

zachwatanabe/test

test

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

zachwatanabe/
test