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metadata
base_model: aubmindlab/bert-base-arabertv02
tags:
  - generated_from_trainer
model-index:
  - name: arabert_baseline_development_task2_fold1
    results: []

arabert_baseline_development_task2_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: 0.5447
  • Qwk: 0.4714
  • Mse: 0.5518

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: 10

Training results

Training Loss Epoch Step Validation Loss Qwk Mse
No log 0.3333 2 3.5280 -0.0028 3.6293
No log 0.6667 4 1.1366 0.0251 1.1880
No log 1.0 6 0.4107 0.0 0.4259
No log 1.3333 8 0.4662 0.1765 0.4494
No log 1.6667 10 0.3079 0.2075 0.3114
No log 2.0 12 0.2932 0.4101 0.2962
No log 2.3333 14 0.3202 0.1525 0.3155
No log 2.6667 16 0.4604 0.2410 0.4445
No log 3.0 18 0.3972 0.1765 0.3866
No log 3.3333 20 0.2887 0.1356 0.2966
No log 3.6667 22 0.3203 0.1907 0.3343
No log 4.0 24 0.3286 0.3453 0.3381
No log 4.3333 26 0.4544 0.1674 0.4506
No log 4.6667 28 0.5203 0.1674 0.5108
No log 5.0 30 0.4560 0.4453 0.4535
No log 5.3333 32 0.4023 0.4457 0.4083
No log 5.6667 34 0.3835 0.3453 0.3956
No log 6.0 36 0.4165 0.4231 0.4249
No log 6.3333 38 0.5295 0.4152 0.5286
No log 6.6667 40 0.6129 0.2759 0.6063
No log 7.0 42 0.6169 0.2759 0.6109
No log 7.3333 44 0.5387 0.3971 0.5393
No log 7.6667 46 0.4830 0.3913 0.4895
No log 8.0 48 0.4890 0.3695 0.4962
No log 8.3333 50 0.5286 0.4714 0.5334
No log 8.6667 52 0.5656 0.3971 0.5690
No log 9.0 54 0.5673 0.4714 0.5716
No log 9.3333 56 0.5533 0.4714 0.5593
No log 9.6667 58 0.5477 0.4714 0.5545
No log 10.0 60 0.5447 0.4714 0.5518

Framework versions

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