Training Loss	Epoch	Step	Validation Loss
15.998	0.19	500	8.5537
7.8818	0.39	1000	7.3646
7.2781	0.58	1500	7.1307
7.1073	0.77	2000	7.0462
7.0749	0.97	2500	7.0667
7.0373	1.16	3000	6.9511
6.9767	1.36	3500	6.8339
6.9483	1.55	4000	6.7795
6.9071	1.74	4500	6.7828
6.8591	1.94	5000	6.7164
6.8595	2.13	5500	6.7705
6.8406	2.32	6000	6.6906
6.7861	2.52	6500	6.6878
6.8103	2.71	7000	6.6486
6.7724	2.9	7500	6.6703
6.7563	3.1	8000	6.6626
6.7567	3.29	8500	6.6603
6.7315	3.49	9000	6.6392
6.7443	3.68	9500	6.6306
6.7244	3.87	10000	6.6456
6.7464	4.07	10500	6.6224
6.7008	4.26	11000	6.6138
6.7076	4.45	11500	6.6783
6.6944	4.65	12000	6.6147
6.6993	4.84	12500	6.6466
6.6893	5.03	13000	6.6369
6.6905	5.23	13500	6.6293
6.6899	5.42	14000	6.6271
6.6835	5.62	14500	6.6566
6.6746	5.81	15000	6.6385
6.68	6.0	15500	6.6309
6.6776	6.2	16000	6.6069
6.6714	6.39	16500	6.5991
6.6766	6.58	17000	6.6180
6.6591	6.78	17500	6.6212
6.6396	6.97	18000	6.5804
6.6575	7.16	18500	6.6096
6.6506	7.36	19000	6.5579
6.6618	7.55	19500	6.5911
6.6581	7.75	20000	6.5870
6.6703	7.94	20500	6.6062
6.6392	8.13	21000	6.5962
6.6343	8.33	21500	6.5903
6.6426	8.52	22000	6.6010
6.6227	8.71	22500	6.6060
6.6392	8.91	23000	6.5935
6.6198	9.1	23500	6.6293
6.6372	9.3	24000	6.5594
6.6146	9.49	24500	6.5917
6.6119	9.68	25000	6.5694
6.6292	9.88	25500	6.6230
6.634	10.07	26000	6.5857
6.5863	10.26	26500	6.5938
6.5957	10.46	27000	6.6256
6.5928	10.65	27500	6.6111
6.5948	10.84	28000	6.6031
6.6131	11.04	28500	6.5582
6.5946	11.23	29000	6.6093
6.6155	11.43	29500	6.5670
6.6051	11.62	30000	6.6016
6.5917	11.81	30500	6.6045
6.5918	12.01	31000	6.5802
6.558	12.2	31500	6.5195
6.5896	12.39	32000	6.6315
6.5662	12.59	32500	6.6112
6.5702	12.78	33000	6.5779
6.5798	12.97	33500	6.5662
6.5963	13.17	34000	6.5776
6.5733	13.36	34500	6.5870
6.5499	13.56	35000	6.5850
6.5492	13.75	35500	6.5957
6.5466	13.94	36000	6.5812
6.5741	14.14	36500	6.5287
6.5612	14.33	37000	6.5611
6.5648	14.52	37500	6.5381
6.5661	14.72	38000	6.5742
6.5564	14.91	38500	6.5424
6.5423	15.1	39000	6.5987
6.5471	15.3	39500	6.5662
6.5559	15.49	40000	6.5290
6.5332	15.69	40500	6.5412
6.5362	15.88	41000	6.5486
6.5351	16.07	41500	6.5959
6.5337	16.27	42000	6.5405
6.5246	16.46	42500	6.5217
6.4999	16.65	43000	6.5443
6.5459	16.85	43500	6.5424
6.5077	17.04	44000	6.5499
6.5069	17.23	44500	6.5509
6.5189	17.43	45000	6.5310
6.5086	17.62	45500	6.5361
6.5182	17.82	46000	6.5320
6.51	18.01	46500	6.4850
6.4868	18.2	47000	6.5155
6.4665	18.4	47500	6.5305
6.5123	18.59	48000	6.5301
6.4981	18.78	48500	6.4617
6.4606	18.98	49000	6.4895
6.4716	19.17	49500	6.4790
6.4733	19.36	50000	6.4818
6.4935	19.56	50500	6.4518
6.4761	19.75	51000	6.4852
6.4651	19.95	51500	6.4836
6.4462	20.14	52000	6.4792
6.4605	20.33	52500	6.4661
6.4718	20.53	53000	6.4639
6.459	20.72	53500	6.4683
6.4407	20.91	54000	6.4663
6.4388	21.11	54500	6.4832
6.4479	21.3	55000	6.4606
6.4583	21.49	55500	6.4723
6.4169	21.69	56000	6.4897
6.4437	21.88	56500	6.4368
6.4566	22.08	57000	6.4491
6.4248	22.27	57500	6.4630
6.431	22.46	58000	6.4246
6.4274	22.66	58500	6.4618
6.4262	22.85	59000	6.4177
6.4328	23.04	59500	6.4243
6.4305	23.24	60000	6.4178
6.4078	23.43	60500	6.4310
6.4431	23.63	61000	6.4338
6.4066	23.82	61500	6.4080
6.417	24.01	62000	6.4236
6.4008	24.21	62500	6.3703
6.4222	24.4	63000	6.4188
6.4304	24.59	63500	6.3924
6.4063	24.79	64000	6.4140
6.4176	24.98	64500	6.4419
6.4203	25.17	65000	6.4250
6.3983	25.37	65500	6.3602
6.3911	25.56	66000	6.4129
6.3821	25.76	66500	6.4225
6.3864	25.95	67000	6.3801
6.4109	26.14	67500	6.4032
6.4136	26.34	68000	6.3870
6.3714	26.53	68500	6.4385
6.3711	26.72	69000	6.4081
6.391	26.92	69500	6.3901
6.3931	27.11	70000	6.4047
6.3842	27.3	70500	6.3830
6.3798	27.5	71000	6.3935
6.3903	27.69	71500	6.3756
6.3771	27.89	72000	6.3554
6.3763	28.08	72500	6.3911
6.3576	28.27	73000	6.4059
6.3581	28.47	73500	6.3976
6.3739	28.66	74000	6.3921
6.363	28.85	74500	6.3590
6.3687	29.05	75000	6.3683
6.3788	29.24	75500	6.3915
6.3505	29.43	76000	6.3826
6.3618	29.63	76500	6.3833
6.3287	29.82	77000	6.4055
6.3589	30.02	77500	6.3994
6.3614	30.21	78000	6.3848
6.3729	30.4	78500	6.3550
6.3687	30.6	79000	6.3683
6.3377	30.79	79500	6.3743
6.3188	30.98	80000	6.3113
6.3613	31.18	80500	6.3852
6.3428	31.37	81000	6.3610
6.3541	31.56	81500	6.3848
6.3821	31.76	82000	6.3706
6.3357	31.95	82500	6.3191
6.3408	32.15	83000	6.3357
6.3301	32.34	83500	6.3374
6.3681	32.53	84000	6.3583
6.324	32.73	84500	6.3472
6.3615	32.92	85000	6.3359
6.3382	33.11	85500	6.3664
6.34	33.31	86000	6.3281
6.3504	33.5	86500	6.3688
6.3393	33.69	87000	6.3553
6.3453	33.89	87500	6.3493
6.3293	34.08	88000	6.3315
6.3346	34.28	88500	6.3134
6.3325	34.47	89000	6.3631
6.3497	34.66	89500	6.3380
6.332	34.86	90000	6.3484
6.3224	35.05	90500	6.3602
6.3242	35.24	91000	6.3414
6.3346	35.44	91500	6.3151
6.3547	35.63	92000	6.3499
6.3243	35.82	92500	6.3173
6.3148	36.02	93000	6.3141
6.3202	36.21	93500	6.3358
6.3251	36.41	94000	6.2946
6.3313	36.6	94500	6.3413
6.3077	36.79	95000	6.2959
6.3173	36.99	95500	6.3220
6.3207	37.18	96000	6.3630
6.311	37.37	96500	6.3802
6.3259	37.57	97000	6.3425
6.3269	37.76	97500	6.3407
6.3136	37.96	98000	6.3140
6.3007	38.15	98500	6.3392
6.2911	38.34	99000	6.3874
6.3241	38.54	99500	6.3363
6.3056	38.73	100000	6.3766
6.3138	38.92	100500	6.3147
6.3065	39.12	101000	6.3622
6.3118	39.31	101500	6.3200
6.3009	39.5	102000	6.3316
6.3107	39.7	102500	6.3112
6.2977	39.89	103000	6.3120

Framework versions

Transformers 4.24.0
Pytorch 1.12.1+cu113
Datasets 2.6.1
Tokenizers 0.13.2

gngpostalsrvc/BERiT_2000_custom_architecture_40_epochs_ls_.2

BERiT_2000_custom_architecture_40_epochs_ls_.2

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_.2