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Arabic_FineTuningAraBERT_AugV0_k2_task1_organization_fold1

This model is a fine-tuned version of aubmindlab/bert-base-arabertv02 on the None dataset. It achieves the following results on the evaluation set:

  • Loss: 0.3885
  • Qwk: 0.7368
  • Mse: 0.3885
  • Rmse: 0.6233

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:

  • learning_rate: 2e-05
  • train_batch_size: 8
  • eval_batch_size: 8
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • num_epochs: 10

Training results

Training Loss Epoch Step Validation Loss Qwk Mse Rmse
No log 0.0952 2 3.1653 0.0315 3.1653 1.7791
No log 0.1905 4 1.4373 0.1064 1.4373 1.1989
No log 0.2857 6 1.1174 0.1860 1.1174 1.0571
No log 0.3810 8 0.8149 0.3497 0.8149 0.9027
No log 0.4762 10 0.8664 0.2613 0.8664 0.9308
No log 0.5714 12 0.6495 0.4556 0.6495 0.8059
No log 0.6667 14 0.5543 0.5625 0.5543 0.7445
No log 0.7619 16 0.7081 0.4706 0.7081 0.8415
No log 0.8571 18 1.1308 0.4207 1.1308 1.0634
No log 0.9524 20 1.0216 0.4577 1.0216 1.0107
No log 1.0476 22 0.5026 0.6379 0.5026 0.7089
No log 1.1429 24 0.4569 0.5484 0.4569 0.6760
No log 1.2381 26 0.4653 0.5484 0.4653 0.6821
No log 1.3333 28 0.4723 0.5670 0.4723 0.6872
No log 1.4286 30 0.4227 0.6529 0.4227 0.6501
No log 1.5238 32 0.4257 0.6566 0.4257 0.6525
No log 1.6190 34 0.4279 0.6889 0.4279 0.6541
No log 1.7143 36 0.4475 0.72 0.4475 0.6690
No log 1.8095 38 0.4602 0.7336 0.4602 0.6784
No log 1.9048 40 0.4622 0.7658 0.4622 0.6799
No log 2.0 42 0.4505 0.7222 0.4505 0.6712
No log 2.0952 44 0.4200 0.7222 0.4200 0.6480
No log 2.1905 46 0.4052 0.6912 0.4052 0.6366
No log 2.2857 48 0.4296 0.72 0.4296 0.6555
No log 2.3810 50 0.4814 0.7123 0.4814 0.6939
No log 2.4762 52 0.5409 0.7308 0.5409 0.7354
No log 2.5714 54 0.5819 0.7111 0.5819 0.7628
No log 2.6667 56 0.6055 0.7251 0.6055 0.7781
No log 2.7619 58 0.6857 0.6198 0.6857 0.8281
No log 2.8571 60 0.6122 0.6364 0.6122 0.7824
No log 2.9524 62 0.4872 0.7183 0.4872 0.6980
No log 3.0476 64 0.4478 0.6851 0.4478 0.6692
No log 3.1429 66 0.4654 0.7368 0.4654 0.6822
No log 3.2381 68 0.4401 0.7529 0.4401 0.6634
No log 3.3333 70 0.4140 0.6288 0.4140 0.6434
No log 3.4286 72 0.4358 0.7287 0.4358 0.6601
No log 3.5238 74 0.4958 0.7222 0.4958 0.7041
No log 3.6190 76 0.4827 0.7072 0.4827 0.6948
No log 3.7143 78 0.4708 0.7072 0.4708 0.6861
No log 3.8095 80 0.4604 0.7072 0.4604 0.6785
No log 3.9048 82 0.4697 0.7072 0.4697 0.6853
No log 4.0 84 0.4836 0.6423 0.4836 0.6954
No log 4.0952 86 0.4701 0.5882 0.4701 0.6856
No log 4.1905 88 0.4728 0.6711 0.4728 0.6876
No log 4.2857 90 0.4808 0.6164 0.4808 0.6934
No log 4.3810 92 0.5289 0.6500 0.5289 0.7272
No log 4.4762 94 0.5980 0.7159 0.5980 0.7733
No log 4.5714 96 0.5975 0.7159 0.5975 0.7730
No log 4.6667 98 0.5620 0.7159 0.5620 0.7497
No log 4.7619 100 0.5196 0.6617 0.5196 0.7208
No log 4.8571 102 0.4729 0.6617 0.4729 0.6877
No log 4.9524 104 0.4864 0.7605 0.4864 0.6974
No log 5.0476 106 0.4598 0.7154 0.4598 0.6781
No log 5.1429 108 0.4237 0.7605 0.4237 0.6510
No log 5.2381 110 0.4292 0.7605 0.4292 0.6551
No log 5.3333 112 0.4053 0.7605 0.4053 0.6367
No log 5.4286 114 0.3604 0.7712 0.3604 0.6003
No log 5.5238 116 0.3602 0.7907 0.3602 0.6002
No log 5.6190 118 0.3629 0.7508 0.3629 0.6024
No log 5.7143 120 0.3661 0.7921 0.3661 0.6051
No log 5.8095 122 0.3626 0.7921 0.3626 0.6021
No log 5.9048 124 0.3573 0.7336 0.3573 0.5978
No log 6.0 126 0.3672 0.6912 0.3672 0.6059
No log 6.0952 128 0.4076 0.7138 0.4076 0.6384
No log 6.1905 130 0.4730 0.7159 0.4730 0.6878
No log 6.2857 132 0.4904 0.7159 0.4904 0.7003
No log 6.3810 134 0.4566 0.75 0.4566 0.6757
No log 6.4762 136 0.4396 0.75 0.4396 0.6630
No log 6.5714 138 0.4310 0.7375 0.4310 0.6565
No log 6.6667 140 0.4317 0.7375 0.4317 0.6571
No log 6.7619 142 0.4392 0.7667 0.4392 0.6627
No log 6.8571 144 0.4407 0.75 0.4407 0.6638
No log 6.9524 146 0.4818 0.7154 0.4818 0.6941
No log 7.0476 148 0.5411 0.6038 0.5411 0.7356
No log 7.1429 150 0.5509 0.6038 0.5509 0.7423
No log 7.2381 152 0.5148 0.7004 0.5148 0.7175
No log 7.3333 154 0.4629 0.7154 0.4629 0.6804
No log 7.4286 156 0.4404 0.7063 0.4404 0.6636
No log 7.5238 158 0.4411 0.7508 0.4411 0.6642
No log 7.6190 160 0.4374 0.6859 0.4374 0.6613
No log 7.7143 162 0.4352 0.7111 0.4352 0.6597
No log 7.8095 164 0.4350 0.7508 0.4350 0.6595
No log 7.9048 166 0.4505 0.7255 0.4505 0.6712
No log 8.0 168 0.4721 0.7368 0.4721 0.6871
No log 8.0952 170 0.4975 0.7368 0.4975 0.7053
No log 8.1905 172 0.4939 0.7605 0.4939 0.7028
No log 8.2857 174 0.4707 0.7368 0.4707 0.6861
No log 8.3810 176 0.4316 0.7138 0.4316 0.6570
No log 8.4762 178 0.3949 0.7063 0.3949 0.6284
No log 8.5714 180 0.3833 0.7063 0.3833 0.6191
No log 8.6667 182 0.3833 0.7063 0.3833 0.6191
No log 8.7619 184 0.3872 0.7138 0.3872 0.6223
No log 8.8571 186 0.3980 0.7368 0.3980 0.6308
No log 8.9524 188 0.4056 0.6908 0.4056 0.6369
No log 9.0476 190 0.4250 0.7154 0.4250 0.6519
No log 9.1429 192 0.4358 0.7154 0.4358 0.6601
No log 9.2381 194 0.4337 0.7154 0.4337 0.6586
No log 9.3333 196 0.4213 0.7154 0.4213 0.6490
No log 9.4286 198 0.4062 0.7154 0.4062 0.6373
No log 9.5238 200 0.3933 0.6908 0.3933 0.6271
No log 9.6190 202 0.3903 0.6908 0.3903 0.6247
No log 9.7143 204 0.3905 0.6908 0.3905 0.6249
No log 9.8095 206 0.3890 0.7368 0.3890 0.6237
No log 9.9048 208 0.3883 0.7368 0.3883 0.6232
No log 10.0 210 0.3885 0.7368 0.3885 0.6233

Framework versions

  • Transformers 4.44.2
  • Pytorch 2.4.0+cu118
  • Datasets 2.21.0
  • Tokenizers 0.19.1
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