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arabert_cross_organization_task7_fold5

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bert
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metadata
base_model: aubmindlab/bert-base-arabertv02
tags:
  - generated_from_trainer
model-index:
  - name: arabert_cross_organization_task7_fold5
    results: []

arabert_cross_organization_task7_fold5

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: 0.5800
  • Qwk: 0.7690
  • Mse: 0.5800

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: 64
  • eval_batch_size: 64
  • 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
No log 0.125 2 3.1878 0.0032 3.1878
No log 0.25 4 1.6075 0.1098 1.6075
No log 0.375 6 1.1035 0.2670 1.1035
No log 0.5 8 1.4112 0.3086 1.4112
No log 0.625 10 1.3436 0.3880 1.3436
No log 0.75 12 1.1181 0.3654 1.1181
No log 0.875 14 0.8279 0.4164 0.8279
No log 1.0 16 0.6795 0.4918 0.6795
No log 1.125 18 0.6997 0.5370 0.6997
No log 1.25 20 0.8208 0.6120 0.8208
No log 1.375 22 0.7785 0.7348 0.7785
No log 1.5 24 0.6388 0.7210 0.6388
No log 1.625 26 0.6688 0.7365 0.6688
No log 1.75 28 0.6507 0.7403 0.6507
No log 1.875 30 0.5320 0.7356 0.5320
No log 2.0 32 0.5254 0.7454 0.5254
No log 2.125 34 0.5348 0.7325 0.5348
No log 2.25 36 0.6139 0.7376 0.6139
No log 2.375 38 0.6648 0.7474 0.6648
No log 2.5 40 0.5894 0.7707 0.5894
No log 2.625 42 0.5580 0.7530 0.5580
No log 2.75 44 0.5824 0.7698 0.5824
No log 2.875 46 0.6444 0.7641 0.6444
No log 3.0 48 0.5327 0.7206 0.5327
No log 3.125 50 0.5871 0.7517 0.5871
No log 3.25 52 0.5331 0.7366 0.5331
No log 3.375 54 0.6130 0.7665 0.6130
No log 3.5 56 0.5889 0.7650 0.5889
No log 3.625 58 0.5848 0.7758 0.5848
No log 3.75 60 0.7089 0.7737 0.7089
No log 3.875 62 0.7846 0.7865 0.7846
No log 4.0 64 0.6552 0.7793 0.6552
No log 4.125 66 0.5020 0.7284 0.5020
No log 4.25 68 0.5170 0.7322 0.5170
No log 4.375 70 0.5877 0.7481 0.5877
No log 4.5 72 0.5700 0.7494 0.5700
No log 4.625 74 0.5147 0.7380 0.5147
No log 4.75 76 0.5942 0.7664 0.5942
No log 4.875 78 0.6564 0.7710 0.6564
No log 5.0 80 0.6565 0.7710 0.6565
No log 5.125 82 0.6572 0.7802 0.6572
No log 5.25 84 0.6860 0.7836 0.6860
No log 5.375 86 0.6265 0.7687 0.6265
No log 5.5 88 0.5116 0.7530 0.5116
No log 5.625 90 0.5026 0.7603 0.5026
No log 5.75 92 0.5588 0.7542 0.5588
No log 5.875 94 0.6752 0.7902 0.6752
No log 6.0 96 0.7891 0.7984 0.7891
No log 6.125 98 0.7038 0.7947 0.7038
No log 6.25 100 0.5797 0.7519 0.5797
No log 6.375 102 0.5895 0.7634 0.5895
No log 6.5 104 0.6498 0.7782 0.6498
No log 6.625 106 0.5864 0.7623 0.5864
No log 6.75 108 0.5259 0.7227 0.5259
No log 6.875 110 0.5133 0.7040 0.5133
No log 7.0 112 0.5219 0.7120 0.5219
No log 7.125 114 0.5822 0.7464 0.5822
No log 7.25 116 0.6526 0.7676 0.6526
No log 7.375 118 0.6628 0.7818 0.6628
No log 7.5 120 0.6080 0.7726 0.6080
No log 7.625 122 0.5645 0.7416 0.5645
No log 7.75 124 0.5592 0.7409 0.5592
No log 7.875 126 0.5637 0.7527 0.5637
No log 8.0 128 0.5640 0.7522 0.5640
No log 8.125 130 0.5743 0.7522 0.5743
No log 8.25 132 0.6128 0.7551 0.6128
No log 8.375 134 0.6083 0.7551 0.6083
No log 8.5 136 0.5761 0.7661 0.5761
No log 8.625 138 0.5522 0.7596 0.5522
No log 8.75 140 0.5418 0.7580 0.5418
No log 8.875 142 0.5541 0.7626 0.5541
No log 9.0 144 0.5890 0.7591 0.5890
No log 9.125 146 0.6347 0.7704 0.6347
No log 9.25 148 0.6524 0.7643 0.6524
No log 9.375 150 0.6441 0.7643 0.6441
No log 9.5 152 0.6196 0.7597 0.6196
No log 9.625 154 0.5988 0.7575 0.5988
No log 9.75 156 0.5835 0.7591 0.5835
No log 9.875 158 0.5803 0.7672 0.5803
No log 10.0 160 0.5800 0.7690 0.5800

Framework versions

  • Transformers 4.44.0
  • Pytorch 2.4.0
  • Datasets 2.21.0
  • Tokenizers 0.19.1