--- library_name: transformers base_model: aubmindlab/bert-base-arabertv02 tags: - generated_from_trainer model-index: - name: Arabic_FineTuningAraBERT_AugV4_k1_task1_organization_fold1 results: [] --- # Arabic_FineTuningAraBERT_AugV4_k1_task1_organization_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: 0.7620 - Qwk: 0.7042 - Mse: 0.7620 - Rmse: 0.8729 ## 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: 8 - eval_batch_size: 8 - 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 | Rmse | |:-------------:|:------:|:----:|:---------------:|:-------:|:------:|:------:| | No log | 0.0714 | 2 | 3.6124 | -0.0132 | 3.6124 | 1.9006 | | No log | 0.1429 | 4 | 1.8035 | 0.0072 | 1.8035 | 1.3429 | | No log | 0.2143 | 6 | 0.9159 | 0.1026 | 0.9159 | 0.9570 | | No log | 0.2857 | 8 | 0.7706 | 0.3348 | 0.7706 | 0.8778 | | No log | 0.3571 | 10 | 1.3346 | 0.2257 | 1.3346 | 1.1552 | | No log | 0.4286 | 12 | 1.2293 | 0.2257 | 1.2293 | 1.1088 | | No log | 0.5 | 14 | 0.8303 | 0.3951 | 0.8303 | 0.9112 | | No log | 0.5714 | 16 | 0.7716 | 0.4425 | 0.7716 | 0.8784 | | No log | 0.6429 | 18 | 0.6770 | 0.5333 | 0.6770 | 0.8228 | | No log | 0.7143 | 20 | 0.6677 | 0.6182 | 0.6677 | 0.8171 | | No log | 0.7857 | 22 | 0.5707 | 0.6231 | 0.5707 | 0.7554 | | No log | 0.8571 | 24 | 0.5062 | 0.6733 | 0.5062 | 0.7115 | | No log | 0.9286 | 26 | 0.5458 | 0.5508 | 0.5458 | 0.7388 | | No log | 1.0 | 28 | 0.5303 | 0.6828 | 0.5303 | 0.7282 | | No log | 1.0714 | 30 | 0.5012 | 0.6557 | 0.5012 | 0.7080 | | No log | 1.1429 | 32 | 0.4980 | 0.6828 | 0.4980 | 0.7057 | | No log | 1.2143 | 34 | 0.5893 | 0.6211 | 0.5893 | 0.7677 | | No log | 1.2857 | 36 | 0.6739 | 0.6786 | 0.6739 | 0.8209 | | No log | 1.3571 | 38 | 0.7754 | 0.6447 | 0.7754 | 0.8806 | | No log | 1.4286 | 40 | 0.8049 | 0.6613 | 0.8049 | 0.8972 | | No log | 1.5 | 42 | 0.8029 | 0.6120 | 0.8029 | 0.8961 | | No log | 1.5714 | 44 | 0.7854 | 0.5779 | 0.7854 | 0.8862 | | No log | 1.6429 | 46 | 0.7768 | 0.6396 | 0.7768 | 0.8813 | | No log | 1.7143 | 48 | 0.7709 | 0.6486 | 0.7709 | 0.8780 | | No log | 1.7857 | 50 | 0.7285 | 0.6160 | 0.7285 | 0.8535 | | No log | 1.8571 | 52 | 0.5849 | 0.5294 | 0.5849 | 0.7648 | | No log | 1.9286 | 54 | 0.5560 | 0.5164 | 0.5560 | 0.7456 | | No log | 2.0 | 56 | 0.6040 | 0.5576 | 0.6040 | 0.7772 | | No log | 2.0714 | 58 | 0.7966 | 0.5405 | 0.7966 | 0.8925 | | No log | 2.1429 | 60 | 0.8970 | 0.5078 | 0.8970 | 0.9471 | | No log | 2.2143 | 62 | 0.8134 | 0.5939 | 0.8134 | 0.9019 | | No log | 2.2857 | 64 | 0.7359 | 0.6232 | 0.7359 | 0.8579 | | No log | 2.3571 | 66 | 0.7887 | 0.3824 | 0.7887 | 0.8881 | | No log | 2.4286 | 68 | 0.7449 | 0.4588 | 0.7449 | 0.8631 | | No log | 2.5 | 70 | 0.7041 | 0.6453 | 0.7041 | 0.8391 | | No log | 2.5714 | 72 | 0.8706 | 0.6769 | 0.8706 | 0.9331 | | No log | 2.6429 | 74 | 1.0587 | 0.6957 | 1.0587 | 1.0289 | | No log | 2.7143 | 76 | 0.9892 | 0.6488 | 0.9892 | 0.9946 | | No log | 2.7857 | 78 | 0.7240 | 0.6744 | 0.7240 | 0.8509 | | No log | 2.8571 | 80 | 0.4759 | 0.6364 | 0.4759 | 0.6898 | | No log | 2.9286 | 82 | 0.5055 | 0.5157 | 0.5055 | 0.7110 | | No log | 3.0 | 84 | 0.5233 | 0.5157 | 0.5233 | 0.7234 | | No log | 3.0714 | 86 | 0.4751 | 0.5302 | 0.4751 | 0.6893 | | No log | 3.1429 | 88 | 0.5147 | 0.6387 | 0.5147 | 0.7174 | | No log | 3.2143 | 90 | 0.6567 | 0.7651 | 0.6567 | 0.8104 | | No log | 3.2857 | 92 | 0.7222 | 0.6729 | 0.7222 | 0.8498 | | No log | 3.3571 | 94 | 0.7476 | 0.6453 | 0.7476 | 0.8646 | | No log | 3.4286 | 96 | 0.8091 | 0.6376 | 0.8091 | 0.8995 | | No log | 3.5 | 98 | 0.8363 | 0.6453 | 0.8363 | 0.9145 | | No log | 3.5714 | 100 | 0.8654 | 0.7042 | 0.8654 | 0.9302 | | No log | 3.6429 | 102 | 0.8417 | 0.7042 | 0.8417 | 0.9174 | | No log | 3.7143 | 104 | 0.7806 | 0.6872 | 0.7806 | 0.8835 | | No log | 3.7857 | 106 | 0.7126 | 0.6453 | 0.7126 | 0.8442 | | No log | 3.8571 | 108 | 0.7201 | 0.6453 | 0.7201 | 0.8486 | | No log | 3.9286 | 110 | 0.7687 | 0.6872 | 0.7687 | 0.8768 | | No log | 4.0 | 112 | 0.8380 | 0.7042 | 0.8380 | 0.9154 | | No log | 4.0714 | 114 | 0.8143 | 0.6872 | 0.8143 | 0.9024 | | No log | 4.1429 | 116 | 0.8054 | 0.7042 | 0.8054 | 0.8974 | | No log | 4.2143 | 118 | 0.8669 | 0.6557 | 0.8669 | 0.9311 | | No log | 4.2857 | 120 | 0.8679 | 0.6557 | 0.8679 | 0.9316 | | No log | 4.3571 | 122 | 0.8411 | 0.7042 | 0.8411 | 0.9171 | | No log | 4.4286 | 124 | 0.8101 | 0.6453 | 0.8101 | 0.9001 | | No log | 4.5 | 126 | 0.8224 | 0.7614 | 0.8224 | 0.9068 | | No log | 4.5714 | 128 | 0.8112 | 0.8119 | 0.8112 | 0.9007 | | No log | 4.6429 | 130 | 0.7025 | 0.7614 | 0.7025 | 0.8381 | | No log | 4.7143 | 132 | 0.5948 | 0.6872 | 0.5948 | 0.7712 | | No log | 4.7857 | 134 | 0.5557 | 0.6571 | 0.5557 | 0.7454 | | No log | 4.8571 | 136 | 0.5748 | 0.7042 | 0.5748 | 0.7581 | | No log | 4.9286 | 138 | 0.6399 | 0.7799 | 0.6399 | 0.8000 | | No log | 5.0 | 140 | 0.6768 | 0.6686 | 0.6768 | 0.8227 | | No log | 5.0714 | 142 | 0.7147 | 0.7042 | 0.7147 | 0.8454 | | No log | 5.1429 | 144 | 0.6956 | 0.6453 | 0.6956 | 0.8340 | | No log | 5.2143 | 146 | 0.7307 | 0.6453 | 0.7307 | 0.8548 | | No log | 5.2857 | 148 | 0.7958 | 0.6613 | 0.7958 | 0.8921 | | No log | 5.3571 | 150 | 0.8007 | 0.6613 | 0.8007 | 0.8948 | | No log | 5.4286 | 152 | 0.7583 | 0.6613 | 0.7583 | 0.8708 | | No log | 5.5 | 154 | 0.6943 | 0.6453 | 0.6943 | 0.8333 | | No log | 5.5714 | 156 | 0.6583 | 0.6429 | 0.6583 | 0.8113 | | No log | 5.6429 | 158 | 0.6565 | 0.6613 | 0.6565 | 0.8102 | | No log | 5.7143 | 160 | 0.7078 | 0.7284 | 0.7078 | 0.8413 | | No log | 5.7857 | 162 | 0.7690 | 0.8121 | 0.7690 | 0.8769 | | No log | 5.8571 | 164 | 0.7825 | 0.8121 | 0.7825 | 0.8846 | | No log | 5.9286 | 166 | 0.7413 | 0.8121 | 0.7413 | 0.8610 | | No log | 6.0 | 168 | 0.7217 | 0.7907 | 0.7217 | 0.8495 | | No log | 6.0714 | 170 | 0.6900 | 0.7515 | 0.6900 | 0.8307 | | No log | 6.1429 | 172 | 0.6854 | 0.6613 | 0.6854 | 0.8279 | | No log | 6.2143 | 174 | 0.7411 | 0.6613 | 0.7411 | 0.8609 | | No log | 6.2857 | 176 | 0.7943 | 0.7614 | 0.7943 | 0.8912 | | No log | 6.3571 | 178 | 0.8542 | 0.7556 | 0.8542 | 0.9242 | | No log | 6.4286 | 180 | 0.8404 | 0.7892 | 0.8404 | 0.9167 | | No log | 6.5 | 182 | 0.8195 | 0.7393 | 0.8195 | 0.9052 | | No log | 6.5714 | 184 | 0.7813 | 0.7154 | 0.7813 | 0.8839 | | No log | 6.6429 | 186 | 0.7764 | 0.7154 | 0.7764 | 0.8812 | | No log | 6.7143 | 188 | 0.7663 | 0.6429 | 0.7663 | 0.8754 | | No log | 6.7857 | 190 | 0.7531 | 0.7154 | 0.7531 | 0.8678 | | No log | 6.8571 | 192 | 0.7491 | 0.7154 | 0.7491 | 0.8655 | | No log | 6.9286 | 194 | 0.7724 | 0.7393 | 0.7724 | 0.8788 | | No log | 7.0 | 196 | 0.7571 | 0.7048 | 0.7571 | 0.8701 | | No log | 7.0714 | 198 | 0.7078 | 0.7284 | 0.7078 | 0.8413 | | No log | 7.1429 | 200 | 0.6651 | 0.7331 | 0.6651 | 0.8155 | | No log | 7.2143 | 202 | 0.6309 | 0.7331 | 0.6309 | 0.7943 | | No log | 7.2857 | 204 | 0.6506 | 0.7331 | 0.6506 | 0.8066 | | No log | 7.3571 | 206 | 0.7157 | 0.7284 | 0.7157 | 0.8460 | | No log | 7.4286 | 208 | 0.7367 | 0.7284 | 0.7367 | 0.8583 | | No log | 7.5 | 210 | 0.7571 | 0.7614 | 0.7571 | 0.8701 | | No log | 7.5714 | 212 | 0.7919 | 0.7042 | 0.7919 | 0.8899 | | No log | 7.6429 | 214 | 0.8154 | 0.7042 | 0.8154 | 0.9030 | | No log | 7.7143 | 216 | 0.8134 | 0.7042 | 0.8134 | 0.9019 | | No log | 7.7857 | 218 | 0.7885 | 0.7042 | 0.7885 | 0.8880 | | No log | 7.8571 | 220 | 0.7690 | 0.6872 | 0.7690 | 0.8769 | | No log | 7.9286 | 222 | 0.7516 | 0.6872 | 0.7516 | 0.8670 | | No log | 8.0 | 224 | 0.7383 | 0.6872 | 0.7383 | 0.8593 | | No log | 8.0714 | 226 | 0.7214 | 0.6872 | 0.7214 | 0.8494 | | No log | 8.1429 | 228 | 0.7246 | 0.6872 | 0.7246 | 0.8512 | | No log | 8.2143 | 230 | 0.7395 | 0.6872 | 0.7395 | 0.8600 | | No log | 8.2857 | 232 | 0.7649 | 0.6686 | 0.7649 | 0.8746 | | No log | 8.3571 | 234 | 0.7811 | 0.6686 | 0.7811 | 0.8838 | | No log | 8.4286 | 236 | 0.7939 | 0.7284 | 0.7939 | 0.8910 | | No log | 8.5 | 238 | 0.7853 | 0.7799 | 0.7853 | 0.8862 | | No log | 8.5714 | 240 | 0.7595 | 0.6686 | 0.7595 | 0.8715 | | No log | 8.6429 | 242 | 0.7337 | 0.6686 | 0.7337 | 0.8566 | | No log | 8.7143 | 244 | 0.7204 | 0.7042 | 0.7204 | 0.8487 | | No log | 8.7857 | 246 | 0.7168 | 0.6872 | 0.7168 | 0.8466 | | No log | 8.8571 | 248 | 0.7320 | 0.6872 | 0.7320 | 0.8556 | | No log | 8.9286 | 250 | 0.7438 | 0.6872 | 0.7438 | 0.8624 | | No log | 9.0 | 252 | 0.7619 | 0.6872 | 0.7619 | 0.8729 | | No log | 9.0714 | 254 | 0.7843 | 0.7042 | 0.7843 | 0.8856 | | No log | 9.1429 | 256 | 0.7976 | 0.7042 | 0.7976 | 0.8931 | | No log | 9.2143 | 258 | 0.7966 | 0.7042 | 0.7966 | 0.8925 | | No log | 9.2857 | 260 | 0.7947 | 0.7042 | 0.7947 | 0.8915 | | No log | 9.3571 | 262 | 0.7958 | 0.7042 | 0.7958 | 0.8921 | | No log | 9.4286 | 264 | 0.7946 | 0.7042 | 0.7946 | 0.8914 | | No log | 9.5 | 266 | 0.7873 | 0.7042 | 0.7873 | 0.8873 | | No log | 9.5714 | 268 | 0.7777 | 0.7042 | 0.7777 | 0.8819 | | No log | 9.6429 | 270 | 0.7712 | 0.7042 | 0.7712 | 0.8782 | | No log | 9.7143 | 272 | 0.7660 | 0.7042 | 0.7660 | 0.8752 | | No log | 9.7857 | 274 | 0.7634 | 0.7042 | 0.7634 | 0.8737 | | No log | 9.8571 | 276 | 0.7619 | 0.7042 | 0.7619 | 0.8729 | | No log | 9.9286 | 278 | 0.7619 | 0.7042 | 0.7619 | 0.8729 | | No log | 10.0 | 280 | 0.7620 | 0.7042 | 0.7620 | 0.8729 | ### Framework versions - Transformers 4.44.2 - Pytorch 2.4.0+cu118 - Datasets 2.21.0 - Tokenizers 0.19.1