--- base_model: aubmindlab/bert-base-arabertv02 tags: - generated_from_trainer model-index: - name: arabert_cross_organization_task1_fold6 results: [] --- # arabert_cross_organization_task1_fold6 This model is a fine-tuned version of [aubmindlab/bert-base-arabertv02](https://huggingface.co/aubmindlab/bert-base-arabertv02) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.5605 - Qwk: 0.5728 - Mse: 0.5597 ## 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 | 0.1333 | 2 | 2.6067 | 0.0127 | 2.6095 | | No log | 0.2667 | 4 | 1.3449 | 0.1252 | 1.3444 | | No log | 0.4 | 6 | 0.8578 | 0.3924 | 0.8566 | | No log | 0.5333 | 8 | 0.9106 | 0.4859 | 0.9097 | | No log | 0.6667 | 10 | 0.8748 | 0.2420 | 0.8739 | | No log | 0.8 | 12 | 0.7294 | 0.3596 | 0.7288 | | No log | 0.9333 | 14 | 0.5690 | 0.6415 | 0.5689 | | No log | 1.0667 | 16 | 0.5434 | 0.5530 | 0.5434 | | No log | 1.2 | 18 | 0.5471 | 0.5275 | 0.5466 | | No log | 1.3333 | 20 | 0.5550 | 0.5931 | 0.5542 | | No log | 1.4667 | 22 | 0.6050 | 0.5930 | 0.6038 | | No log | 1.6 | 24 | 0.5418 | 0.6626 | 0.5409 | | No log | 1.7333 | 26 | 0.5460 | 0.5657 | 0.5451 | | No log | 1.8667 | 28 | 0.5935 | 0.4982 | 0.5926 | | No log | 2.0 | 30 | 0.5427 | 0.5075 | 0.5420 | | No log | 2.1333 | 32 | 0.4531 | 0.6187 | 0.4527 | | No log | 2.2667 | 34 | 0.4419 | 0.6495 | 0.4414 | | No log | 2.4 | 36 | 0.4419 | 0.6978 | 0.4415 | | No log | 2.5333 | 38 | 0.4760 | 0.6296 | 0.4752 | | No log | 2.6667 | 40 | 0.6868 | 0.4906 | 0.6857 | | No log | 2.8 | 42 | 0.7124 | 0.4820 | 0.7111 | | No log | 2.9333 | 44 | 0.5222 | 0.5761 | 0.5212 | | No log | 3.0667 | 46 | 0.4482 | 0.7104 | 0.4476 | | No log | 3.2 | 48 | 0.4458 | 0.6877 | 0.4451 | | No log | 3.3333 | 50 | 0.4617 | 0.6149 | 0.4608 | | No log | 3.4667 | 52 | 0.4966 | 0.5426 | 0.4956 | | No log | 3.6 | 54 | 0.4450 | 0.6238 | 0.4441 | | No log | 3.7333 | 56 | 0.4262 | 0.6657 | 0.4254 | | No log | 3.8667 | 58 | 0.4293 | 0.6880 | 0.4287 | | No log | 4.0 | 60 | 0.4457 | 0.6871 | 0.4450 | | No log | 4.1333 | 62 | 0.4834 | 0.6153 | 0.4825 | | No log | 4.2667 | 64 | 0.4860 | 0.6114 | 0.4851 | | No log | 4.4 | 66 | 0.4688 | 0.6743 | 0.4681 | | No log | 4.5333 | 68 | 0.4821 | 0.6484 | 0.4814 | | No log | 4.6667 | 70 | 0.5849 | 0.5461 | 0.5839 | | No log | 4.8 | 72 | 0.6875 | 0.4677 | 0.6865 | | No log | 4.9333 | 74 | 0.6204 | 0.5060 | 0.6195 | | No log | 5.0667 | 76 | 0.4953 | 0.6000 | 0.4944 | | No log | 5.2 | 78 | 0.4472 | 0.6794 | 0.4465 | | No log | 5.3333 | 80 | 0.4447 | 0.6714 | 0.4438 | | No log | 5.4667 | 82 | 0.5001 | 0.5886 | 0.4990 | | No log | 5.6 | 84 | 0.5921 | 0.5277 | 0.5908 | | No log | 5.7333 | 86 | 0.6040 | 0.5105 | 0.6027 | | No log | 5.8667 | 88 | 0.5520 | 0.5654 | 0.5509 | | No log | 6.0 | 90 | 0.4986 | 0.6296 | 0.4977 | | No log | 6.1333 | 92 | 0.4842 | 0.6769 | 0.4835 | | No log | 6.2667 | 94 | 0.4892 | 0.6687 | 0.4885 | | No log | 6.4 | 96 | 0.5316 | 0.5863 | 0.5305 | | No log | 6.5333 | 98 | 0.6215 | 0.5289 | 0.6203 | | No log | 6.6667 | 100 | 0.6345 | 0.5159 | 0.6334 | | No log | 6.8 | 102 | 0.6012 | 0.5377 | 0.6002 | | No log | 6.9333 | 104 | 0.5566 | 0.5601 | 0.5558 | | No log | 7.0667 | 106 | 0.5366 | 0.5820 | 0.5359 | | No log | 7.2 | 108 | 0.5404 | 0.5733 | 0.5396 | | No log | 7.3333 | 110 | 0.5546 | 0.5683 | 0.5539 | | No log | 7.4667 | 112 | 0.5313 | 0.5733 | 0.5306 | | No log | 7.6 | 114 | 0.5360 | 0.5716 | 0.5353 | | No log | 7.7333 | 116 | 0.5598 | 0.5575 | 0.5589 | | No log | 7.8667 | 118 | 0.5884 | 0.5500 | 0.5875 | | No log | 8.0 | 120 | 0.6065 | 0.5413 | 0.6055 | | No log | 8.1333 | 122 | 0.6118 | 0.5428 | 0.6108 | | No log | 8.2667 | 124 | 0.6220 | 0.5539 | 0.6210 | | No log | 8.4 | 126 | 0.6154 | 0.5499 | 0.6145 | | No log | 8.5333 | 128 | 0.6035 | 0.5508 | 0.6025 | | No log | 8.6667 | 130 | 0.6093 | 0.5515 | 0.6083 | | No log | 8.8 | 132 | 0.6055 | 0.5501 | 0.6046 | | No log | 8.9333 | 134 | 0.5879 | 0.5569 | 0.5870 | | No log | 9.0667 | 136 | 0.5763 | 0.5636 | 0.5754 | | No log | 9.2 | 138 | 0.5630 | 0.5646 | 0.5622 | | No log | 9.3333 | 140 | 0.5585 | 0.5852 | 0.5577 | | No log | 9.4667 | 142 | 0.5527 | 0.5852 | 0.5519 | | No log | 9.6 | 144 | 0.5526 | 0.5852 | 0.5518 | | No log | 9.7333 | 146 | 0.5553 | 0.5745 | 0.5545 | | No log | 9.8667 | 148 | 0.5592 | 0.5728 | 0.5584 | | No log | 10.0 | 150 | 0.5605 | 0.5728 | 0.5597 | ### Framework versions - Transformers 4.44.0 - Pytorch 2.4.0 - Datasets 2.21.0 - Tokenizers 0.19.1