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arabert_cross_vocabulary_task2_fold2

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: 0.7896
  • Qwk: -0.0578
  • Mse: 0.7896

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: 16
  • eval_batch_size: 16
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • num_epochs: 1

Training results

Training Loss Epoch Step Validation Loss Qwk Mse
No log 0.0308 2 8.5786 0.0 8.5786
No log 0.0615 4 4.7361 0.0027 4.7361
No log 0.0923 6 2.4849 -0.0164 2.4849
No log 0.1231 8 1.0682 0.0 1.0682
No log 0.1538 10 1.0484 -0.2071 1.0484
No log 0.1846 12 1.1580 -0.1541 1.1580
No log 0.2154 14 0.7197 0.0071 0.7197
No log 0.2462 16 0.7275 0.1341 0.7275
No log 0.2769 18 0.8223 0.0676 0.8223
No log 0.3077 20 0.8068 0.0447 0.8068
No log 0.3385 22 0.7532 -0.0157 0.7532
No log 0.3692 24 0.7525 -0.0155 0.7525
No log 0.4 26 0.7808 -0.0217 0.7808
No log 0.4308 28 0.7874 -0.0658 0.7874
No log 0.4615 30 0.7780 0.0 0.7780
No log 0.4923 32 0.8462 0.0 0.8462
No log 0.5231 34 0.9212 0.0 0.9212
No log 0.5538 36 0.9624 0.0 0.9624
No log 0.5846 38 0.9977 0.0 0.9977
No log 0.6154 40 0.9965 0.0 0.9965
No log 0.6462 42 0.9605 0.0 0.9605
No log 0.6769 44 0.8767 0.0 0.8767
No log 0.7077 46 0.8153 0.0 0.8153
No log 0.7385 48 0.7919 0.0 0.7919
No log 0.7692 50 0.7850 -0.0371 0.7850
No log 0.8 52 0.7871 -0.0777 0.7871
No log 0.8308 54 0.7856 -0.0777 0.7856
No log 0.8615 56 0.7873 -0.0777 0.7873
No log 0.8923 58 0.7877 -0.0578 0.7877
No log 0.9231 60 0.7921 -0.0777 0.7921
No log 0.9538 62 0.7919 -0.0777 0.7919
No log 0.9846 64 0.7896 -0.0578 0.7896

Framework versions

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