--- base_model: aubmindlab/bert-base-arabertv02 tags: - generated_from_trainer model-index: - name: arabert_cross_organization_task4_fold2 results: [] --- # arabert_cross_organization_task4_fold2 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: 1.7693 - Qwk: -0.0357 - Mse: 1.7659 ## 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 | 4.1886 | 0.0011 | 4.1850 | | No log | 0.2222 | 4 | 2.2640 | 0.0 | 2.2614 | | No log | 0.3333 | 6 | 1.3362 | 0.0183 | 1.3332 | | No log | 0.4444 | 8 | 0.9155 | 0.0 | 0.9122 | | No log | 0.5556 | 10 | 0.9188 | -0.0048 | 0.9152 | | No log | 0.6667 | 12 | 0.8902 | -0.0315 | 0.8872 | | No log | 0.7778 | 14 | 0.8950 | 0.0741 | 0.8923 | | No log | 0.8889 | 16 | 0.8770 | 0.0875 | 0.8744 | | No log | 1.0 | 18 | 0.8807 | 0.1030 | 0.8782 | | No log | 1.1111 | 20 | 0.8915 | -0.0099 | 0.8887 | | No log | 1.2222 | 22 | 0.9457 | 0.0424 | 0.9431 | | No log | 1.3333 | 24 | 1.0318 | 0.0182 | 1.0292 | | No log | 1.4444 | 26 | 1.1586 | 0.0 | 1.1562 | | No log | 1.5556 | 28 | 1.2936 | 0.0 | 1.2909 | | No log | 1.6667 | 30 | 1.4219 | 0.0 | 1.4193 | | No log | 1.7778 | 32 | 1.4842 | 0.0 | 1.4816 | | No log | 1.8889 | 34 | 1.4765 | 0.0 | 1.4737 | | No log | 2.0 | 36 | 1.4797 | 0.0 | 1.4769 | | No log | 2.1111 | 38 | 1.4454 | 0.0 | 1.4425 | | No log | 2.2222 | 40 | 1.5693 | 0.0 | 1.5665 | | No log | 2.3333 | 42 | 1.6763 | 0.0 | 1.6737 | | No log | 2.4444 | 44 | 1.6705 | 0.0 | 1.6677 | | No log | 2.5556 | 46 | 1.4309 | 0.0182 | 1.4278 | | No log | 2.6667 | 48 | 1.3201 | 0.0536 | 1.3169 | | No log | 2.7778 | 50 | 1.5069 | 0.0182 | 1.5037 | | No log | 2.8889 | 52 | 1.6390 | 0.0 | 1.6360 | | No log | 3.0 | 54 | 1.5712 | 0.0 | 1.5682 | | No log | 3.1111 | 56 | 1.4444 | 0.0182 | 1.4413 | | No log | 3.2222 | 58 | 1.3827 | 0.0182 | 1.3796 | | No log | 3.3333 | 60 | 1.5276 | 0.0 | 1.5245 | | No log | 3.4444 | 62 | 1.5680 | 0.0025 | 1.5650 | | No log | 3.5556 | 64 | 1.5963 | 0.0328 | 1.5931 | | No log | 3.6667 | 66 | 1.5027 | -0.0118 | 1.4994 | | No log | 3.7778 | 68 | 1.5906 | 0.0485 | 1.5873 | | No log | 3.8889 | 70 | 1.7316 | 0.0225 | 1.7284 | | No log | 4.0 | 72 | 1.6752 | 0.0160 | 1.6719 | | No log | 4.1111 | 74 | 1.4644 | 0.0136 | 1.4611 | | No log | 4.2222 | 76 | 1.2946 | 0.0498 | 1.2914 | | No log | 4.3333 | 78 | 1.3892 | 0.0571 | 1.3860 | | No log | 4.4444 | 80 | 1.6935 | 0.0183 | 1.6903 | | No log | 4.5556 | 82 | 1.8327 | 0.0026 | 1.8295 | | No log | 4.6667 | 84 | 1.7518 | 0.0272 | 1.7486 | | No log | 4.7778 | 86 | 1.5355 | 0.0437 | 1.5322 | | No log | 4.8889 | 88 | 1.4546 | 0.0750 | 1.4513 | | No log | 5.0 | 90 | 1.6165 | 0.0875 | 1.6132 | | No log | 5.1111 | 92 | 1.8925 | 0.0054 | 1.8891 | | No log | 5.2222 | 94 | 1.9400 | -0.0784 | 1.9366 | | No log | 5.3333 | 96 | 1.7667 | 0.0162 | 1.7634 | | No log | 5.4444 | 98 | 1.5714 | 0.0700 | 1.5681 | | No log | 5.5556 | 100 | 1.6393 | 0.0586 | 1.6360 | | No log | 5.6667 | 102 | 1.7094 | 0.0144 | 1.7060 | | No log | 5.7778 | 104 | 1.8384 | 0.0162 | 1.8349 | | No log | 5.8889 | 106 | 1.7972 | 0.0130 | 1.7937 | | No log | 6.0 | 108 | 1.7520 | 0.0113 | 1.7486 | | No log | 6.1111 | 110 | 1.6669 | 0.0264 | 1.6636 | | No log | 6.2222 | 112 | 1.6880 | 0.0301 | 1.6846 | | No log | 6.3333 | 114 | 1.7562 | 0.0096 | 1.7528 | | No log | 6.4444 | 116 | 1.7980 | 0.0228 | 1.7947 | | No log | 6.5556 | 118 | 1.8220 | -0.0199 | 1.8187 | | No log | 6.6667 | 120 | 1.7461 | 0.0746 | 1.7429 | | No log | 6.7778 | 122 | 1.5563 | 0.0272 | 1.5532 | | No log | 6.8889 | 124 | 1.4185 | 0.0655 | 1.4154 | | No log | 7.0 | 126 | 1.4690 | 0.0697 | 1.4658 | | No log | 7.1111 | 128 | 1.6412 | 0.0722 | 1.6381 | | No log | 7.2222 | 130 | 1.8101 | -0.0325 | 1.8069 | | No log | 7.3333 | 132 | 1.8161 | -0.0588 | 1.8129 | | No log | 7.4444 | 134 | 1.6991 | 0.0758 | 1.6959 | | No log | 7.5556 | 136 | 1.5693 | 0.0476 | 1.5661 | | No log | 7.6667 | 138 | 1.5215 | 0.0437 | 1.5183 | | No log | 7.7778 | 140 | 1.5990 | 0.0699 | 1.5957 | | No log | 7.8889 | 142 | 1.7382 | 0.0363 | 1.7348 | | No log | 8.0 | 144 | 1.8943 | -0.0587 | 1.8909 | | No log | 8.1111 | 146 | 1.9468 | -0.0675 | 1.9434 | | No log | 8.2222 | 148 | 1.9074 | -0.0587 | 1.9040 | | No log | 8.3333 | 150 | 1.8074 | -0.0208 | 1.8040 | | No log | 8.4444 | 152 | 1.7220 | 0.0241 | 1.7186 | | No log | 8.5556 | 154 | 1.6877 | 0.0352 | 1.6843 | | No log | 8.6667 | 156 | 1.7113 | 0.0222 | 1.7079 | | No log | 8.7778 | 158 | 1.7927 | -0.0208 | 1.7893 | | No log | 8.8889 | 160 | 1.8848 | -0.0477 | 1.8813 | | No log | 9.0 | 162 | 1.9430 | -0.0613 | 1.9396 | | No log | 9.1111 | 164 | 1.9388 | -0.0613 | 1.9354 | | No log | 9.2222 | 166 | 1.8894 | -0.0477 | 1.8860 | | No log | 9.3333 | 168 | 1.8314 | -0.0424 | 1.8279 | | No log | 9.4444 | 170 | 1.7625 | -0.0357 | 1.7591 | | No log | 9.5556 | 172 | 1.7335 | 0.0264 | 1.7302 | | No log | 9.6667 | 174 | 1.7407 | 0.0264 | 1.7373 | | No log | 9.7778 | 176 | 1.7576 | -0.0102 | 1.7543 | | No log | 9.8889 | 178 | 1.7644 | -0.0357 | 1.7610 | | No log | 10.0 | 180 | 1.7693 | -0.0357 | 1.7659 | ### Framework versions - Transformers 4.44.0 - Pytorch 2.4.0 - Datasets 2.21.0 - Tokenizers 0.19.1