generated_from_trainer

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BERiT_2000_custom_architecture_40_epochs_ls_.1

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
15.8251 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