Training Loss	Epoch	Step	Validation Loss
15.8251	0.19	500	8.3567
7.8217	0.39	1000	7.2693
7.2486	0.58	1500	7.0533
7.0209	0.77	2000	6.9330
6.9572	0.97	2500	6.9266
6.897	1.16	3000	6.7520
6.8273	1.36	3500	6.6686
6.7692	1.55	4000	6.5613
6.7269	1.74	4500	6.5789
6.6783	1.94	5000	6.5036
6.6792	2.13	5500	6.5714
6.6638	2.32	6000	6.4733
6.5913	2.52	6500	6.4967
6.634	2.71	7000	6.4530
6.5831	2.9	7500	6.4763
6.556	3.1	8000	6.4616
6.5676	3.29	8500	6.4449
6.5447	3.49	9000	6.4795
6.5531	3.68	9500	6.4253
6.5356	3.87	10000	6.4508
6.5677	4.07	10500	6.4002
6.5035	4.26	11000	6.3985
6.5112	4.45	11500	6.4798
6.5038	4.65	12000	6.4138
6.504	4.84	12500	6.4381
6.4993	5.03	13000	6.4241
6.4853	5.23	13500	6.4275
6.4881	5.42	14000	6.3979
6.4882	5.62	14500	6.4468
6.4728	5.81	15000	6.4319
6.4853	6.0	15500	6.4124
6.4764	6.2	16000	6.4013
6.4638	6.39	16500	6.3955
6.4727	6.58	17000	6.4140
6.4595	6.78	17500	6.4229
6.4273	6.97	18000	6.3758
6.4594	7.16	18500	6.3889
6.4457	7.36	19000	6.3175
6.4538	7.55	19500	6.3748
6.453	7.75	20000	6.3782
6.4662	7.94	20500	6.3953
6.437	8.13	21000	6.4125
6.4342	8.33	21500	6.3641
6.4424	8.52	22000	6.3911
6.4035	8.71	22500	6.4061
6.44	8.91	23000	6.3751
6.4206	9.1	23500	6.4066
6.4297	9.3	24000	6.3342
6.408	9.49	24500	6.3508
6.4026	9.68	25000	6.3609
6.4262	9.88	25500	6.4123
6.4311	10.07	26000	6.3867
6.3788	10.26	26500	6.3724
6.3943	10.46	27000	6.4175
6.3867	10.65	27500	6.3834
6.3868	10.84	28000	6.4073
6.4119	11.04	28500	6.3267
6.387	11.23	29000	6.4038
6.4092	11.43	29500	6.3426
6.3994	11.62	30000	6.3827
6.3868	11.81	30500	6.3681
6.3779	12.01	31000	6.3419
6.3427	12.2	31500	6.2775
6.3763	12.39	32000	6.4021
6.349	12.59	32500	6.3800
6.3538	12.78	33000	6.3369
6.3734	12.97	33500	6.3495
6.3857	13.17	34000	6.3715
6.3678	13.36	34500	6.3540
6.3345	13.56	35000	6.3504
6.3399	13.75	35500	6.3641
6.3383	13.94	36000	6.3556
6.3637	14.14	36500	6.2868
6.3478	14.33	37000	6.3285
6.3416	14.52	37500	6.3001
6.3513	14.72	38000	6.3484
6.3381	14.91	38500	6.3030
6.3244	15.1	39000	6.3705
6.3285	15.3	39500	6.3402
6.3448	15.49	40000	6.2991
6.3199	15.69	40500	6.3100
6.3195	15.88	41000	6.3203
6.3191	16.07	41500	6.3647
6.3228	16.27	42000	6.3181
6.3055	16.46	42500	6.2765
6.2817	16.65	43000	6.3116
6.3412	16.85	43500	6.3228
6.3002	17.04	44000	6.3100
6.289	17.23	44500	6.3308
6.3087	17.43	45000	6.3143
6.2945	17.62	45500	6.3081
6.3012	17.82	46000	6.3268
6.3008	18.01	46500	6.2792
6.2712	18.2	47000	6.2773
6.2488	18.4	47500	6.3212
6.2927	18.59	48000	6.3141
6.2865	18.78	48500	6.2275
6.2472	18.98	49000	6.2689
6.2617	19.17	49500	6.2390
6.2627	19.36	50000	6.2496
6.2916	19.56	50500	6.2473
6.2514	19.75	51000	6.2867
6.2501	19.95	51500	6.2353
6.2242	20.14	52000	6.2676
6.2465	20.33	52500	6.2274
6.2677	20.53	53000	6.2365
6.2534	20.72	53500	6.2161
6.2185	20.91	54000	6.2284
6.2171	21.11	54500	6.2475
6.2322	21.3	55000	6.2339
6.2359	21.49	55500	6.2272
6.1961	21.69	56000	6.2844
6.2254	21.88	56500	6.1721
6.242	22.08	57000	6.2173
6.2136	22.27	57500	6.2512
6.2053	22.46	58000	6.1929
6.2052	22.66	58500	6.2275
6.2022	22.85	59000	6.1908
6.2127	23.04	59500	6.1896
6.2163	23.24	60000	6.1702
6.187	23.43	60500	6.2002
6.2149	23.63	61000	6.2151
6.1867	23.82	61500	6.1795
6.1901	24.01	62000	6.1942
6.1901	24.21	62500	6.1266
6.1959	24.4	63000	6.1754
6.2138	24.59	63500	6.1405
6.1917	24.79	64000	6.1818
6.204	24.98	64500	6.2095
6.1947	25.17	65000	6.1928
6.1793	25.37	65500	6.1076
6.1571	25.56	66000	6.1632
6.1595	25.76	66500	6.1803
6.1632	25.95	67000	6.1188
6.1756	26.14	67500	6.1569
6.1947	26.34	68000	6.1281
6.1451	26.53	68500	6.1972
6.1418	26.72	69000	6.1418
6.1657	26.92	69500	6.1514
6.1751	27.11	70000	6.1513
6.1529	27.3	70500	6.1319
6.155	27.5	71000	6.1515
6.1537	27.69	71500	6.1271
6.1372	27.89	72000	6.0880
6.1475	28.08	72500	6.1339
6.1254	28.27	73000	6.1503
6.1125	28.47	73500	6.1292
6.1359	28.66	74000	6.1513
6.1346	28.85	74500	6.1083
6.1411	29.05	75000	6.1005
6.147	29.24	75500	6.1451
6.1263	29.43	76000	6.1206
6.1211	29.63	76500	6.1363
6.0854	29.82	77000	6.1753
6.132	30.02	77500	6.1525
6.127	30.21	78000	6.1390
6.151	30.4	78500	6.1003
6.1272	30.6	79000	6.1126
6.0856	30.79	79500	6.1034
6.0806	30.98	80000	6.0432
6.1221	31.18	80500	6.1355
6.0959	31.37	81000	6.1013
6.1166	31.56	81500	6.1264
6.1337	31.76	82000	6.1278
6.0929	31.95	82500	6.0627
6.099	32.15	83000	6.0922
6.0934	32.34	83500	6.0839
6.1255	32.53	84000	6.1186
6.084	32.73	84500	6.0857
6.1205	32.92	85000	6.0830
6.0857	33.11	85500	6.1101
6.1016	33.31	86000	6.0707
6.1124	33.5	86500	6.1193
6.0906	33.69	87000	6.0991
6.1113	33.89	87500	6.1047
6.0811	34.08	88000	6.0694
6.0966	34.28	88500	6.0595
6.0931	34.47	89000	6.1141
6.1163	34.66	89500	6.0787
6.0874	34.86	90000	6.1036
6.0872	35.05	90500	6.1053
6.0804	35.24	91000	6.0759
6.0955	35.44	91500	6.0604
6.1155	35.63	92000	6.1016
6.0789	35.82	92500	6.0678
6.0605	36.02	93000	6.0467
6.0832	36.21	93500	6.0742
6.0742	36.41	94000	6.0379
6.0804	36.6	94500	6.0870
6.0629	36.79	95000	6.0339
6.0676	36.99	95500	6.0635
6.0838	37.18	96000	6.1115
6.0696	37.37	96500	6.1257
6.0761	37.57	97000	6.0905
6.073	37.76	97500	6.0920
6.059	37.96	98000	6.0508
6.0628	38.15	98500	6.0818
6.0383	38.34	99000	6.1400
6.0753	38.54	99500	6.0810
6.0641	38.73	100000	6.1257
6.0648	38.92	100500	6.0536
6.0545	39.12	101000	6.1098
6.0643	39.31	101500	6.0630
6.0521	39.5	102000	6.0639
6.0633	39.7	102500	6.0430
6.0464	39.89	103000	6.0606

Framework versions

Transformers 4.24.0
Pytorch 1.12.1+cu113
Datasets 2.7.0
Tokenizers 0.13.2

gngpostalsrvc/BERiT_2000_custom_architecture_40_epochs_ls_.1

BERiT_2000_custom_architecture_40_epochs_ls_.1

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

gngpostalsrvc/
BERiT_2000_custom_architecture_40_epochs_ls_.1