--- base_model: aubmindlab/bert-base-arabertv02 tags: - generated_from_trainer model-index: - name: arabert_cross_organization_task4_fold5 results: [] --- # arabert_cross_organization_task4_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: 0.4843 - Qwk: 0.7666 - Mse: 0.4857 ## 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 | 0.9490 | 0.2150 | 0.9491 | | No log | 0.2353 | 4 | 0.8716 | 0.5434 | 0.8727 | | No log | 0.3529 | 6 | 0.6659 | 0.6268 | 0.6673 | | No log | 0.4706 | 8 | 0.5369 | 0.6777 | 0.5386 | | No log | 0.5882 | 10 | 0.6440 | 0.7813 | 0.6460 | | No log | 0.7059 | 12 | 0.5256 | 0.6777 | 0.5270 | | No log | 0.8235 | 14 | 0.4966 | 0.7039 | 0.4980 | | No log | 0.9412 | 16 | 0.7392 | 0.7069 | 0.7410 | | No log | 1.0588 | 18 | 0.8304 | 0.7187 | 0.8325 | | No log | 1.1765 | 20 | 0.6276 | 0.7906 | 0.6292 | | No log | 1.2941 | 22 | 0.4520 | 0.7197 | 0.4531 | | No log | 1.4118 | 24 | 0.4614 | 0.7447 | 0.4625 | | No log | 1.5294 | 26 | 0.5417 | 0.7758 | 0.5430 | | No log | 1.6471 | 28 | 0.4689 | 0.7534 | 0.4700 | | No log | 1.7647 | 30 | 0.4572 | 0.7731 | 0.4583 | | No log | 1.8824 | 32 | 0.6532 | 0.7981 | 0.6546 | | No log | 2.0 | 34 | 0.7591 | 0.7855 | 0.7607 | | No log | 2.1176 | 36 | 0.5854 | 0.7951 | 0.5867 | | No log | 2.2353 | 38 | 0.4474 | 0.7384 | 0.4483 | | No log | 2.3529 | 40 | 0.4483 | 0.7531 | 0.4492 | | No log | 2.4706 | 42 | 0.5873 | 0.8112 | 0.5887 | | No log | 2.5882 | 44 | 0.6933 | 0.8094 | 0.6948 | | No log | 2.7059 | 46 | 0.5657 | 0.7996 | 0.5670 | | No log | 2.8235 | 48 | 0.4349 | 0.7582 | 0.4358 | | No log | 2.9412 | 50 | 0.4131 | 0.7274 | 0.4139 | | No log | 3.0588 | 52 | 0.4231 | 0.7410 | 0.4241 | | No log | 3.1765 | 54 | 0.4993 | 0.7809 | 0.5006 | | No log | 3.2941 | 56 | 0.5623 | 0.8152 | 0.5637 | | No log | 3.4118 | 58 | 0.4818 | 0.7688 | 0.4831 | | No log | 3.5294 | 60 | 0.4394 | 0.7381 | 0.4405 | | No log | 3.6471 | 62 | 0.4350 | 0.7249 | 0.4361 | | No log | 3.7647 | 64 | 0.4743 | 0.7662 | 0.4756 | | No log | 3.8824 | 66 | 0.5838 | 0.8110 | 0.5853 | | No log | 4.0 | 68 | 0.6906 | 0.7966 | 0.6921 | | No log | 4.1176 | 70 | 0.6463 | 0.7987 | 0.6478 | | No log | 4.2353 | 72 | 0.4721 | 0.7595 | 0.4733 | | No log | 4.3529 | 74 | 0.4408 | 0.7408 | 0.4419 | | No log | 4.4706 | 76 | 0.4907 | 0.7768 | 0.4920 | | No log | 4.5882 | 78 | 0.5961 | 0.7936 | 0.5977 | | No log | 4.7059 | 80 | 0.5925 | 0.7830 | 0.5941 | | No log | 4.8235 | 82 | 0.5242 | 0.7638 | 0.5257 | | No log | 4.9412 | 84 | 0.4430 | 0.7330 | 0.4442 | | No log | 5.0588 | 86 | 0.4332 | 0.7022 | 0.4342 | | No log | 5.1765 | 88 | 0.4549 | 0.7664 | 0.4561 | | No log | 5.2941 | 90 | 0.5563 | 0.7685 | 0.5577 | | No log | 5.4118 | 92 | 0.6249 | 0.7924 | 0.6265 | | No log | 5.5294 | 94 | 0.5569 | 0.7721 | 0.5585 | | No log | 5.6471 | 96 | 0.4954 | 0.7701 | 0.4969 | | No log | 5.7647 | 98 | 0.4434 | 0.7420 | 0.4446 | | No log | 5.8824 | 100 | 0.4310 | 0.7481 | 0.4320 | | No log | 6.0 | 102 | 0.4622 | 0.7629 | 0.4634 | | No log | 6.1176 | 104 | 0.5374 | 0.7903 | 0.5387 | | No log | 6.2353 | 106 | 0.5235 | 0.7829 | 0.5248 | | No log | 6.3529 | 108 | 0.4601 | 0.7688 | 0.4612 | | No log | 6.4706 | 110 | 0.4260 | 0.7327 | 0.4269 | | No log | 6.5882 | 112 | 0.4318 | 0.7362 | 0.4327 | | No log | 6.7059 | 114 | 0.4747 | 0.7719 | 0.4758 | | No log | 6.8235 | 116 | 0.5753 | 0.7740 | 0.5767 | | No log | 6.9412 | 118 | 0.5967 | 0.7736 | 0.5981 | | No log | 7.0588 | 120 | 0.5343 | 0.7711 | 0.5356 | | No log | 7.1765 | 122 | 0.4499 | 0.7556 | 0.4510 | | No log | 7.2941 | 124 | 0.4329 | 0.7295 | 0.4337 | | No log | 7.4118 | 126 | 0.4364 | 0.7376 | 0.4373 | | No log | 7.5294 | 128 | 0.4672 | 0.7700 | 0.4683 | | No log | 7.6471 | 130 | 0.5413 | 0.7754 | 0.5427 | | No log | 7.7647 | 132 | 0.5644 | 0.7761 | 0.5659 | | No log | 7.8824 | 134 | 0.5227 | 0.7749 | 0.5241 | | No log | 8.0 | 136 | 0.4791 | 0.7688 | 0.4804 | | No log | 8.1176 | 138 | 0.4693 | 0.7580 | 0.4705 | | No log | 8.2353 | 140 | 0.4639 | 0.7580 | 0.4652 | | No log | 8.3529 | 142 | 0.4809 | 0.7685 | 0.4822 | | No log | 8.4706 | 144 | 0.4940 | 0.7700 | 0.4954 | | No log | 8.5882 | 146 | 0.4956 | 0.7700 | 0.4970 | | No log | 8.7059 | 148 | 0.4916 | 0.7753 | 0.4930 | | No log | 8.8235 | 150 | 0.4799 | 0.7651 | 0.4812 | | No log | 8.9412 | 152 | 0.4697 | 0.7580 | 0.4709 | | No log | 9.0588 | 154 | 0.4612 | 0.7573 | 0.4624 | | No log | 9.1765 | 156 | 0.4541 | 0.7549 | 0.4552 | | No log | 9.2941 | 158 | 0.4549 | 0.7566 | 0.4561 | | No log | 9.4118 | 160 | 0.4603 | 0.7527 | 0.4616 | | No log | 9.5294 | 162 | 0.4654 | 0.7544 | 0.4667 | | No log | 9.6471 | 164 | 0.4728 | 0.7597 | 0.4742 | | No log | 9.7647 | 166 | 0.4786 | 0.7597 | 0.4800 | | No log | 9.8824 | 168 | 0.4825 | 0.7685 | 0.4838 | | No log | 10.0 | 170 | 0.4843 | 0.7666 | 0.4857 | ### Framework versions - Transformers 4.44.0 - Pytorch 2.4.0 - Datasets 2.21.0 - Tokenizers 0.19.1