--- base_model: aubmindlab/bert-base-arabertv02 tags: - generated_from_trainer model-index: - name: arabert_cross_organization_task6_fold3 results: [] --- # arabert_cross_organization_task6_fold3 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: 1.3563 - Qwk: -0.0016 - Mse: 1.3563 ## 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 | 0.1111 | 2 | 5.0067 | -0.0021 | 5.0067 | | No log | 0.2222 | 4 | 2.3688 | 0.0294 | 2.3688 | | No log | 0.3333 | 6 | 1.3423 | -0.0120 | 1.3423 | | No log | 0.4444 | 8 | 1.5207 | -0.0996 | 1.5207 | | No log | 0.5556 | 10 | 1.1292 | -0.0620 | 1.1292 | | No log | 0.6667 | 12 | 1.1093 | -0.0624 | 1.1093 | | No log | 0.7778 | 14 | 1.1280 | -0.1037 | 1.1280 | | No log | 0.8889 | 16 | 1.1271 | -0.0458 | 1.1271 | | No log | 1.0 | 18 | 1.1353 | -0.0286 | 1.1353 | | No log | 1.1111 | 20 | 1.2273 | 0.0 | 1.2273 | | No log | 1.2222 | 22 | 1.1922 | 0.0071 | 1.1922 | | No log | 1.3333 | 24 | 1.1874 | 0.0071 | 1.1874 | | No log | 1.4444 | 26 | 1.1668 | -0.0325 | 1.1668 | | No log | 1.5556 | 28 | 1.1666 | -0.0039 | 1.1666 | | No log | 1.6667 | 30 | 1.1595 | -0.0073 | 1.1595 | | No log | 1.7778 | 32 | 1.1718 | 0.0290 | 1.1718 | | No log | 1.8889 | 34 | 1.2238 | 0.0 | 1.2238 | | No log | 2.0 | 36 | 1.2276 | 0.0 | 1.2276 | | No log | 2.1111 | 38 | 1.1870 | -0.0366 | 1.1870 | | No log | 2.2222 | 40 | 1.1871 | -0.0785 | 1.1871 | | No log | 2.3333 | 42 | 1.2823 | -0.0775 | 1.2823 | | No log | 2.4444 | 44 | 1.2667 | -0.0775 | 1.2667 | | No log | 2.5556 | 46 | 1.1897 | -0.1036 | 1.1897 | | No log | 2.6667 | 48 | 1.2024 | -0.0005 | 1.2024 | | No log | 2.7778 | 50 | 1.2359 | -0.0107 | 1.2359 | | No log | 2.8889 | 52 | 1.2436 | -0.0073 | 1.2436 | | No log | 3.0 | 54 | 1.2335 | -0.0335 | 1.2335 | | No log | 3.1111 | 56 | 1.2366 | -0.0073 | 1.2366 | | No log | 3.2222 | 58 | 1.2111 | -0.0305 | 1.2111 | | No log | 3.3333 | 60 | 1.1935 | 0.0372 | 1.1935 | | No log | 3.4444 | 62 | 1.2135 | -0.0513 | 1.2135 | | No log | 3.5556 | 64 | 1.2077 | -0.0335 | 1.2077 | | No log | 3.6667 | 66 | 1.2202 | 0.0152 | 1.2202 | | No log | 3.7778 | 68 | 1.2293 | 0.0152 | 1.2293 | | No log | 3.8889 | 70 | 1.2441 | 0.0021 | 1.2441 | | No log | 4.0 | 72 | 1.2616 | 0.0583 | 1.2616 | | No log | 4.1111 | 74 | 1.2524 | -0.0046 | 1.2524 | | No log | 4.2222 | 76 | 1.2555 | -0.0433 | 1.2555 | | No log | 4.3333 | 78 | 1.2534 | 0.0522 | 1.2534 | | No log | 4.4444 | 80 | 1.2663 | 0.0174 | 1.2663 | | No log | 4.5556 | 82 | 1.2903 | -0.0521 | 1.2903 | | No log | 4.6667 | 84 | 1.2865 | -0.0521 | 1.2865 | | No log | 4.7778 | 86 | 1.2537 | -0.0389 | 1.2537 | | No log | 4.8889 | 88 | 1.2394 | -0.0348 | 1.2394 | | No log | 5.0 | 90 | 1.2581 | -0.0668 | 1.2581 | | No log | 5.1111 | 92 | 1.2785 | -0.0442 | 1.2785 | | No log | 5.2222 | 94 | 1.2996 | 0.0353 | 1.2996 | | No log | 5.3333 | 96 | 1.3274 | 0.0118 | 1.3274 | | No log | 5.4444 | 98 | 1.3336 | -0.0386 | 1.3336 | | No log | 5.5556 | 100 | 1.2963 | -0.0161 | 1.2963 | | No log | 5.6667 | 102 | 1.2938 | 0.0144 | 1.2938 | | No log | 5.7778 | 104 | 1.3454 | -0.0160 | 1.3454 | | No log | 5.8889 | 106 | 1.3525 | -0.0233 | 1.3525 | | No log | 6.0 | 108 | 1.3385 | 0.0349 | 1.3385 | | No log | 6.1111 | 110 | 1.3741 | -0.0338 | 1.3741 | | No log | 6.2222 | 112 | 1.4104 | -0.0362 | 1.4104 | | No log | 6.3333 | 114 | 1.4214 | -0.0478 | 1.4214 | | No log | 6.4444 | 116 | 1.4394 | -0.0301 | 1.4394 | | No log | 6.5556 | 118 | 1.4439 | -0.0301 | 1.4439 | | No log | 6.6667 | 120 | 1.3931 | -0.0486 | 1.3931 | | No log | 6.7778 | 122 | 1.3303 | -0.0123 | 1.3303 | | No log | 6.8889 | 124 | 1.2675 | -0.0795 | 1.2675 | | No log | 7.0 | 126 | 1.2466 | -0.0580 | 1.2466 | | No log | 7.1111 | 128 | 1.2543 | -0.0097 | 1.2543 | | No log | 7.2222 | 130 | 1.2895 | -0.0149 | 1.2895 | | No log | 7.3333 | 132 | 1.3307 | 0.0443 | 1.3307 | | No log | 7.4444 | 134 | 1.3809 | -0.0384 | 1.3809 | | No log | 7.5556 | 136 | 1.4319 | -0.1211 | 1.4319 | | No log | 7.6667 | 138 | 1.4611 | -0.0542 | 1.4611 | | No log | 7.7778 | 140 | 1.4378 | -0.0509 | 1.4378 | | No log | 7.8889 | 142 | 1.3913 | -0.0200 | 1.3913 | | No log | 8.0 | 144 | 1.3657 | -0.0247 | 1.3657 | | No log | 8.1111 | 146 | 1.3785 | -0.0093 | 1.3785 | | No log | 8.2222 | 148 | 1.3631 | 0.0143 | 1.3631 | | No log | 8.3333 | 150 | 1.3158 | -0.0532 | 1.3158 | | No log | 8.4444 | 152 | 1.2825 | 0.0371 | 1.2825 | | No log | 8.5556 | 154 | 1.2704 | 0.0035 | 1.2704 | | No log | 8.6667 | 156 | 1.2773 | 0.0377 | 1.2773 | | No log | 8.7778 | 158 | 1.2959 | 0.0377 | 1.2959 | | No log | 8.8889 | 160 | 1.3175 | 0.0625 | 1.3175 | | No log | 9.0 | 162 | 1.3404 | -0.0274 | 1.3404 | | No log | 9.1111 | 164 | 1.3572 | -0.0400 | 1.3572 | | No log | 9.2222 | 166 | 1.3639 | -0.0274 | 1.3639 | | No log | 9.3333 | 168 | 1.3672 | -0.0274 | 1.3672 | | No log | 9.4444 | 170 | 1.3652 | -0.0274 | 1.3652 | | No log | 9.5556 | 172 | 1.3654 | -0.0146 | 1.3654 | | No log | 9.6667 | 174 | 1.3609 | -0.0016 | 1.3609 | | No log | 9.7778 | 176 | 1.3588 | -0.0016 | 1.3588 | | No log | 9.8889 | 178 | 1.3572 | -0.0016 | 1.3572 | | No log | 10.0 | 180 | 1.3563 | -0.0016 | 1.3563 | ### Framework versions - Transformers 4.44.0 - Pytorch 2.4.0 - Datasets 2.21.0 - Tokenizers 0.19.1