--- base_model: aubmindlab/bert-base-arabertv02 tags: - generated_from_trainer model-index: - name: arabert_cross_organization_task4_fold1 results: [] --- # arabert_cross_organization_task4_fold1 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: 0.7438 - Qwk: 0.4143 - Mse: 0.7438 ## 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 | 3.2005 | 0.0155 | 3.2005 | | No log | 0.2353 | 4 | 1.7053 | -0.0034 | 1.7053 | | No log | 0.3529 | 6 | 1.1027 | 0.1636 | 1.1027 | | No log | 0.4706 | 8 | 1.0959 | 0.1953 | 1.0959 | | No log | 0.5882 | 10 | 1.1008 | 0.2356 | 1.1008 | | No log | 0.7059 | 12 | 0.7123 | 0.3763 | 0.7123 | | No log | 0.8235 | 14 | 0.7466 | 0.4020 | 0.7466 | | No log | 0.9412 | 16 | 0.9174 | 0.3522 | 0.9174 | | No log | 1.0588 | 18 | 0.9416 | 0.3154 | 0.9416 | | No log | 1.1765 | 20 | 0.7002 | 0.3766 | 0.7002 | | No log | 1.2941 | 22 | 0.6826 | 0.3709 | 0.6826 | | No log | 1.4118 | 24 | 0.6996 | 0.3822 | 0.6996 | | No log | 1.5294 | 26 | 0.6165 | 0.4733 | 0.6165 | | No log | 1.6471 | 28 | 0.5696 | 0.5228 | 0.5696 | | No log | 1.7647 | 30 | 0.7076 | 0.4534 | 0.7076 | | No log | 1.8824 | 32 | 0.7251 | 0.4533 | 0.7251 | | No log | 2.0 | 34 | 0.5922 | 0.4921 | 0.5922 | | No log | 2.1176 | 36 | 0.5428 | 0.5484 | 0.5428 | | No log | 2.2353 | 38 | 0.5662 | 0.5159 | 0.5662 | | No log | 2.3529 | 40 | 0.6256 | 0.4714 | 0.6256 | | No log | 2.4706 | 42 | 0.6008 | 0.4805 | 0.6008 | | No log | 2.5882 | 44 | 0.5493 | 0.5367 | 0.5493 | | No log | 2.7059 | 46 | 0.5855 | 0.5042 | 0.5855 | | No log | 2.8235 | 48 | 0.6888 | 0.4526 | 0.6888 | | No log | 2.9412 | 50 | 0.6594 | 0.4838 | 0.6594 | | No log | 3.0588 | 52 | 0.5913 | 0.5174 | 0.5913 | | No log | 3.1765 | 54 | 0.5440 | 0.5485 | 0.5440 | | No log | 3.2941 | 56 | 0.5766 | 0.5008 | 0.5766 | | No log | 3.4118 | 58 | 0.7541 | 0.4405 | 0.7541 | | No log | 3.5294 | 60 | 0.6666 | 0.4686 | 0.6666 | | No log | 3.6471 | 62 | 0.5454 | 0.5289 | 0.5454 | | No log | 3.7647 | 64 | 0.5373 | 0.5502 | 0.5373 | | No log | 3.8824 | 66 | 0.5811 | 0.4814 | 0.5811 | | No log | 4.0 | 68 | 0.8522 | 0.3958 | 0.8522 | | No log | 4.1176 | 70 | 0.9611 | 0.3420 | 0.9611 | | No log | 4.2353 | 72 | 0.7150 | 0.4366 | 0.7150 | | No log | 4.3529 | 74 | 0.5129 | 0.5461 | 0.5129 | | No log | 4.4706 | 76 | 0.5130 | 0.5731 | 0.5130 | | No log | 4.5882 | 78 | 0.5549 | 0.5002 | 0.5549 | | No log | 4.7059 | 80 | 0.6423 | 0.4682 | 0.6423 | | No log | 4.8235 | 82 | 0.6433 | 0.4655 | 0.6433 | | No log | 4.9412 | 84 | 0.6814 | 0.4383 | 0.6814 | | No log | 5.0588 | 86 | 0.6506 | 0.4536 | 0.6506 | | No log | 5.1765 | 88 | 0.6845 | 0.4340 | 0.6845 | | No log | 5.2941 | 90 | 0.6105 | 0.4691 | 0.6105 | | No log | 5.4118 | 92 | 0.5818 | 0.5096 | 0.5818 | | No log | 5.5294 | 94 | 0.6505 | 0.4675 | 0.6505 | | No log | 5.6471 | 96 | 0.8762 | 0.4031 | 0.8762 | | No log | 5.7647 | 98 | 0.9354 | 0.3979 | 0.9354 | | No log | 5.8824 | 100 | 0.7273 | 0.4370 | 0.7273 | | No log | 6.0 | 102 | 0.5439 | 0.5370 | 0.5439 | | No log | 6.1176 | 104 | 0.5275 | 0.5934 | 0.5275 | | No log | 6.2353 | 106 | 0.5389 | 0.5407 | 0.5389 | | No log | 6.3529 | 108 | 0.6632 | 0.4439 | 0.6632 | | No log | 6.4706 | 110 | 0.7438 | 0.4262 | 0.7438 | | No log | 6.5882 | 112 | 0.7194 | 0.4386 | 0.7194 | | No log | 6.7059 | 114 | 0.6649 | 0.4611 | 0.6649 | | No log | 6.8235 | 116 | 0.6469 | 0.4620 | 0.6469 | | No log | 6.9412 | 118 | 0.6869 | 0.4426 | 0.6869 | | No log | 7.0588 | 120 | 0.6784 | 0.4431 | 0.6784 | | No log | 7.1765 | 122 | 0.6099 | 0.4604 | 0.6099 | | No log | 7.2941 | 124 | 0.6103 | 0.4469 | 0.6103 | | No log | 7.4118 | 126 | 0.6514 | 0.4384 | 0.6514 | | No log | 7.5294 | 128 | 0.7174 | 0.4218 | 0.7174 | | No log | 7.6471 | 130 | 0.7205 | 0.4218 | 0.7205 | | No log | 7.7647 | 132 | 0.6510 | 0.4378 | 0.6510 | | No log | 7.8824 | 134 | 0.5851 | 0.4688 | 0.5851 | | No log | 8.0 | 136 | 0.5810 | 0.4909 | 0.5810 | | No log | 8.1176 | 138 | 0.6226 | 0.4464 | 0.6226 | | No log | 8.2353 | 140 | 0.7068 | 0.4404 | 0.7068 | | No log | 8.3529 | 142 | 0.8169 | 0.4120 | 0.8169 | | No log | 8.4706 | 144 | 0.8268 | 0.4106 | 0.8268 | | No log | 8.5882 | 146 | 0.7821 | 0.4211 | 0.7821 | | No log | 8.7059 | 148 | 0.7186 | 0.4252 | 0.7186 | | No log | 8.8235 | 150 | 0.6896 | 0.4345 | 0.6896 | | No log | 8.9412 | 152 | 0.6602 | 0.4409 | 0.6602 | | No log | 9.0588 | 154 | 0.6525 | 0.4435 | 0.6525 | | No log | 9.1765 | 156 | 0.6577 | 0.4435 | 0.6577 | | No log | 9.2941 | 158 | 0.6779 | 0.4325 | 0.6779 | | No log | 9.4118 | 160 | 0.7177 | 0.4166 | 0.7177 | | No log | 9.5294 | 162 | 0.7502 | 0.4106 | 0.7502 | | No log | 9.6471 | 164 | 0.7522 | 0.4057 | 0.7522 | | No log | 9.7647 | 166 | 0.7503 | 0.4106 | 0.7503 | | No log | 9.8824 | 168 | 0.7475 | 0.4150 | 0.7475 | | No log | 10.0 | 170 | 0.7438 | 0.4143 | 0.7438 | ### Framework versions - Transformers 4.44.0 - Pytorch 2.4.0 - Datasets 2.21.0 - Tokenizers 0.19.1