generated_from_trainer

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BERiT_2000_custom_architecture_40_epochs_ls_.2

This model is a fine-tuned version of roberta-base on an unknown dataset. It achieves the following results on the evaluation set:

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

Training results

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