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arabert_cross_relevance_task7_fold3

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metadata
base_model: aubmindlab/bert-base-arabertv02
tags:
  - generated_from_trainer
model-index:
  - name: arabert_cross_relevance_task7_fold3
    results: []

arabert_cross_relevance_task7_fold3

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: 0.4851
  • Qwk: 0.0152
  • Mse: 0.4851

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 0.1176 2 0.4468 0.0241 0.4468
No log 0.2353 4 0.5293 0.1111 0.5293
No log 0.3529 6 0.4293 0.0657 0.4293
No log 0.4706 8 0.2782 0.0 0.2782
No log 0.5882 10 0.2775 0.0 0.2775
No log 0.7059 12 0.3091 0.0172 0.3091
No log 0.8235 14 0.3313 -0.0714 0.3313
No log 0.9412 16 0.3259 0.0101 0.3259
No log 1.0588 18 0.3382 0.0 0.3382
No log 1.1765 20 0.3574 -0.0135 0.3574
No log 1.2941 22 0.3580 -0.0274 0.3580
No log 1.4118 24 0.4301 0.0 0.4301
No log 1.5294 26 0.4619 0.0 0.4619
No log 1.6471 28 0.3492 0.0 0.3492
No log 1.7647 30 0.3168 -0.0417 0.3168
No log 1.8824 32 0.3350 -0.0496 0.3350
No log 2.0 34 0.2965 0.0203 0.2965
No log 2.1176 36 0.2856 0.0 0.2856
No log 2.2353 38 0.2924 0.0 0.2924
No log 2.3529 40 0.2917 0.0 0.2917
No log 2.4706 42 0.2928 0.0 0.2928
No log 2.5882 44 0.2979 -0.0616 0.2979
No log 2.7059 46 0.3126 0.0 0.3126
No log 2.8235 48 0.3738 0.0 0.3738
No log 2.9412 50 0.3664 0.0 0.3664
No log 3.0588 52 0.3323 0.0 0.3323
No log 3.1765 54 0.3388 0.0 0.3388
No log 3.2941 56 0.3765 0.0 0.3765
No log 3.4118 58 0.3896 0.0 0.3896
No log 3.5294 60 0.3597 0.0 0.3597
No log 3.6471 62 0.3248 0.0 0.3248
No log 3.7647 64 0.3130 -0.0473 0.3130
No log 3.8824 66 0.3039 0.0 0.3039
No log 4.0 68 0.3036 -0.0235 0.3036
No log 4.1176 70 0.3005 0.0 0.3005
No log 4.2353 72 0.3729 0.0 0.3729
No log 4.3529 74 0.4274 0.0 0.4274
No log 4.4706 76 0.4149 0.0 0.4149
No log 4.5882 78 0.3636 0.0 0.3636
No log 4.7059 80 0.3435 0.0 0.3435
No log 4.8235 82 0.3605 0.0 0.3605
No log 4.9412 84 0.4118 0.0 0.4118
No log 5.0588 86 0.4373 0.0 0.4373
No log 5.1765 88 0.3877 0.0 0.3877
No log 5.2941 90 0.3152 0.0 0.3152
No log 5.4118 92 0.3033 0.0 0.3033
No log 5.5294 94 0.3085 0.0 0.3085
No log 5.6471 96 0.3451 0.0 0.3451
No log 5.7647 98 0.4554 0.0 0.4554
No log 5.8824 100 0.5290 0.0230 0.5290
No log 6.0 102 0.5208 0.0230 0.5208
No log 6.1176 104 0.4384 0.0 0.4384
No log 6.2353 106 0.3592 0.0 0.3592
No log 6.3529 108 0.3270 -0.0235 0.3270
No log 6.4706 110 0.3341 -0.0235 0.3341
No log 6.5882 112 0.3501 0.0 0.3501
No log 6.7059 114 0.4010 0.0224 0.4010
No log 6.8235 116 0.4289 0.0224 0.4289
No log 6.9412 118 0.4617 0.0432 0.4617
No log 7.0588 120 0.4638 0.0432 0.4638
No log 7.1765 122 0.4444 0.0432 0.4444
No log 7.2941 124 0.4625 0.0120 0.4625
No log 7.4118 126 0.5041 0.0139 0.5041
No log 7.5294 128 0.5111 0.0139 0.5111
No log 7.6471 130 0.5193 -0.0026 0.5193
No log 7.7647 132 0.4942 0.0139 0.4942
No log 7.8824 134 0.4487 0.0432 0.4487
No log 8.0 136 0.3941 0.0 0.3941
No log 8.1176 138 0.3656 0.0 0.3656
No log 8.2353 140 0.3670 0.0 0.3670
No log 8.3529 142 0.3813 0.0 0.3813
No log 8.4706 144 0.4173 0.0432 0.4173
No log 8.5882 146 0.4510 0.0230 0.4510
No log 8.7059 148 0.4637 0.0230 0.4637
No log 8.8235 150 0.4865 -0.0026 0.4865
No log 8.9412 152 0.5054 -0.0101 0.5054
No log 9.0588 154 0.5000 0.0074 0.5000
No log 9.1765 156 0.4847 -0.0026 0.4847
No log 9.2941 158 0.4728 -0.0026 0.4728
No log 9.4118 160 0.4652 0.0054 0.4652
No log 9.5294 162 0.4641 0.0054 0.4641
No log 9.6471 164 0.4713 -0.0026 0.4713
No log 9.7647 166 0.4801 0.0152 0.4801
No log 9.8824 168 0.4839 0.0152 0.4839
No log 10.0 170 0.4851 0.0152 0.4851

Framework versions

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