--- base_model: aubmindlab/bert-base-arabertv02 tags: - generated_from_trainer model-index: - name: arabert_cross_organization_task1_fold5 results: [] --- # arabert_cross_organization_task1_fold5 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: 0.4714 - Qwk: 0.7597 - Mse: 0.4723 ## 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 | 1.3842 | 0.1724 | 1.3839 | | No log | 0.2667 | 4 | 0.9780 | 0.3534 | 0.9789 | | No log | 0.4 | 6 | 1.4683 | 0.4626 | 1.4695 | | No log | 0.5333 | 8 | 1.2844 | 0.5615 | 1.2856 | | No log | 0.6667 | 10 | 0.8166 | 0.5501 | 0.8177 | | No log | 0.8 | 12 | 0.6646 | 0.5615 | 0.6656 | | No log | 0.9333 | 14 | 0.6181 | 0.6308 | 0.6192 | | No log | 1.0667 | 16 | 0.6395 | 0.7598 | 0.6406 | | No log | 1.2 | 18 | 0.5961 | 0.7503 | 0.5973 | | No log | 1.3333 | 20 | 0.5222 | 0.7439 | 0.5233 | | No log | 1.4667 | 22 | 0.5709 | 0.7564 | 0.5721 | | No log | 1.6 | 24 | 0.5337 | 0.7493 | 0.5348 | | No log | 1.7333 | 26 | 0.5381 | 0.7745 | 0.5392 | | No log | 1.8667 | 28 | 0.5385 | 0.7823 | 0.5395 | | No log | 2.0 | 30 | 0.5204 | 0.7694 | 0.5215 | | No log | 2.1333 | 32 | 0.5481 | 0.7727 | 0.5492 | | No log | 2.2667 | 34 | 0.4767 | 0.7478 | 0.4777 | | No log | 2.4 | 36 | 0.4552 | 0.7226 | 0.4561 | | No log | 2.5333 | 38 | 0.4590 | 0.7366 | 0.4599 | | No log | 2.6667 | 40 | 0.4443 | 0.7177 | 0.4451 | | No log | 2.8 | 42 | 0.4514 | 0.7415 | 0.4522 | | No log | 2.9333 | 44 | 0.5579 | 0.8045 | 0.5590 | | No log | 3.0667 | 46 | 0.7664 | 0.7863 | 0.7678 | | No log | 3.2 | 48 | 0.6906 | 0.7958 | 0.6919 | | No log | 3.3333 | 50 | 0.4951 | 0.7710 | 0.4961 | | No log | 3.4667 | 52 | 0.4583 | 0.7404 | 0.4591 | | No log | 3.6 | 54 | 0.4705 | 0.7615 | 0.4715 | | No log | 3.7333 | 56 | 0.4873 | 0.7626 | 0.4884 | | No log | 3.8667 | 58 | 0.4498 | 0.7546 | 0.4507 | | No log | 4.0 | 60 | 0.4307 | 0.7203 | 0.4315 | | No log | 4.1333 | 62 | 0.4439 | 0.7456 | 0.4449 | | No log | 4.2667 | 64 | 0.4697 | 0.7618 | 0.4707 | | No log | 4.4 | 66 | 0.4679 | 0.7644 | 0.4689 | | No log | 4.5333 | 68 | 0.4351 | 0.7431 | 0.4359 | | No log | 4.6667 | 70 | 0.4379 | 0.7504 | 0.4388 | | No log | 4.8 | 72 | 0.4599 | 0.7566 | 0.4608 | | No log | 4.9333 | 74 | 0.4597 | 0.7542 | 0.4606 | | No log | 5.0667 | 76 | 0.4453 | 0.7436 | 0.4462 | | No log | 5.2 | 78 | 0.4534 | 0.7462 | 0.4543 | | No log | 5.3333 | 80 | 0.4416 | 0.7345 | 0.4424 | | No log | 5.4667 | 82 | 0.4467 | 0.7332 | 0.4475 | | No log | 5.6 | 84 | 0.4936 | 0.7561 | 0.4946 | | No log | 5.7333 | 86 | 0.5611 | 0.7947 | 0.5622 | | No log | 5.8667 | 88 | 0.5154 | 0.7747 | 0.5164 | | No log | 6.0 | 90 | 0.4950 | 0.7700 | 0.4960 | | No log | 6.1333 | 92 | 0.4817 | 0.7588 | 0.4827 | | No log | 6.2667 | 94 | 0.4527 | 0.7499 | 0.4536 | | No log | 6.4 | 96 | 0.4380 | 0.7228 | 0.4388 | | No log | 6.5333 | 98 | 0.4383 | 0.7248 | 0.4391 | | No log | 6.6667 | 100 | 0.4494 | 0.7409 | 0.4502 | | No log | 6.8 | 102 | 0.4509 | 0.7297 | 0.4518 | | No log | 6.9333 | 104 | 0.4765 | 0.7516 | 0.4775 | | No log | 7.0667 | 106 | 0.4854 | 0.7631 | 0.4864 | | No log | 7.2 | 108 | 0.4991 | 0.7766 | 0.5001 | | No log | 7.3333 | 110 | 0.5297 | 0.7654 | 0.5308 | | No log | 7.4667 | 112 | 0.5591 | 0.7862 | 0.5603 | | No log | 7.6 | 114 | 0.5193 | 0.7711 | 0.5204 | | No log | 7.7333 | 116 | 0.4710 | 0.7587 | 0.4719 | | No log | 7.8667 | 118 | 0.4527 | 0.7448 | 0.4536 | | No log | 8.0 | 120 | 0.4573 | 0.7500 | 0.4582 | | No log | 8.1333 | 122 | 0.4652 | 0.7567 | 0.4661 | | No log | 8.2667 | 124 | 0.4728 | 0.7600 | 0.4738 | | No log | 8.4 | 126 | 0.4700 | 0.7563 | 0.4709 | | No log | 8.5333 | 128 | 0.4667 | 0.7515 | 0.4677 | | No log | 8.6667 | 130 | 0.4712 | 0.7538 | 0.4722 | | No log | 8.8 | 132 | 0.4748 | 0.7555 | 0.4757 | | No log | 8.9333 | 134 | 0.4694 | 0.7533 | 0.4703 | | No log | 9.0667 | 136 | 0.4637 | 0.7429 | 0.4646 | | No log | 9.2 | 138 | 0.4616 | 0.7432 | 0.4625 | | No log | 9.3333 | 140 | 0.4664 | 0.7454 | 0.4673 | | No log | 9.4667 | 142 | 0.4737 | 0.7588 | 0.4746 | | No log | 9.6 | 144 | 0.4748 | 0.7567 | 0.4757 | | No log | 9.7333 | 146 | 0.4743 | 0.7586 | 0.4752 | | No log | 9.8667 | 148 | 0.4718 | 0.7597 | 0.4727 | | No log | 10.0 | 150 | 0.4714 | 0.7597 | 0.4723 | ### Framework versions - Transformers 4.44.0 - Pytorch 2.4.0 - Datasets 2.21.0 - Tokenizers 0.19.1