--- base_model: aubmindlab/bert-base-arabertv02 tags: - generated_from_trainer model-index: - name: arabert_cross_organization_task6_fold4 results: [] --- # arabert_cross_organization_task6_fold4 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: 0.5513 - Qwk: 0.8014 - Mse: 0.5513 ## 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 | 0.125 | 2 | 1.7949 | 0.0785 | 1.7949 | | No log | 0.25 | 4 | 1.3176 | 0.3355 | 1.3176 | | No log | 0.375 | 6 | 1.0133 | 0.4987 | 1.0133 | | No log | 0.5 | 8 | 0.8195 | 0.6242 | 0.8195 | | No log | 0.625 | 10 | 0.6810 | 0.6733 | 0.6810 | | No log | 0.75 | 12 | 0.6997 | 0.7476 | 0.6997 | | No log | 0.875 | 14 | 0.8146 | 0.7416 | 0.8146 | | No log | 1.0 | 16 | 0.7351 | 0.6641 | 0.7351 | | No log | 1.125 | 18 | 0.6322 | 0.6459 | 0.6322 | | No log | 1.25 | 20 | 0.6009 | 0.7063 | 0.6009 | | No log | 1.375 | 22 | 0.6280 | 0.7695 | 0.6280 | | No log | 1.5 | 24 | 0.6339 | 0.7783 | 0.6339 | | No log | 1.625 | 26 | 0.6448 | 0.7702 | 0.6448 | | No log | 1.75 | 28 | 0.5640 | 0.7779 | 0.5640 | | No log | 1.875 | 30 | 0.5431 | 0.7439 | 0.5431 | | No log | 2.0 | 32 | 0.5812 | 0.7734 | 0.5812 | | No log | 2.125 | 34 | 0.6724 | 0.7815 | 0.6724 | | No log | 2.25 | 36 | 0.7187 | 0.7729 | 0.7187 | | No log | 2.375 | 38 | 0.5409 | 0.7541 | 0.5409 | | No log | 2.5 | 40 | 0.5281 | 0.7293 | 0.5281 | | No log | 2.625 | 42 | 0.6063 | 0.7901 | 0.6063 | | No log | 2.75 | 44 | 0.6529 | 0.7684 | 0.6529 | | No log | 2.875 | 46 | 0.5454 | 0.7558 | 0.5454 | | No log | 3.0 | 48 | 0.5428 | 0.7521 | 0.5428 | | No log | 3.125 | 50 | 0.6258 | 0.7773 | 0.6258 | | No log | 3.25 | 52 | 0.6788 | 0.7753 | 0.6788 | | No log | 3.375 | 54 | 0.5643 | 0.7870 | 0.5643 | | No log | 3.5 | 56 | 0.5302 | 0.7485 | 0.5302 | | No log | 3.625 | 58 | 0.5557 | 0.7880 | 0.5557 | | No log | 3.75 | 60 | 0.6261 | 0.7858 | 0.6261 | | No log | 3.875 | 62 | 0.5642 | 0.7914 | 0.5642 | | No log | 4.0 | 64 | 0.5355 | 0.7849 | 0.5355 | | No log | 4.125 | 66 | 0.5320 | 0.7895 | 0.5320 | | No log | 4.25 | 68 | 0.5634 | 0.7939 | 0.5634 | | No log | 4.375 | 70 | 0.5815 | 0.7876 | 0.5815 | | No log | 4.5 | 72 | 0.5643 | 0.7947 | 0.5643 | | No log | 4.625 | 74 | 0.5290 | 0.7923 | 0.5290 | | No log | 4.75 | 76 | 0.5541 | 0.7948 | 0.5541 | | No log | 4.875 | 78 | 0.6162 | 0.7857 | 0.6162 | | No log | 5.0 | 80 | 0.5720 | 0.7885 | 0.5720 | | No log | 5.125 | 82 | 0.5198 | 0.7815 | 0.5198 | | No log | 5.25 | 84 | 0.5301 | 0.7775 | 0.5301 | | No log | 5.375 | 86 | 0.5941 | 0.7837 | 0.5941 | | No log | 5.5 | 88 | 0.5713 | 0.7985 | 0.5713 | | No log | 5.625 | 90 | 0.5275 | 0.7835 | 0.5275 | | No log | 5.75 | 92 | 0.5396 | 0.7877 | 0.5396 | | No log | 5.875 | 94 | 0.5560 | 0.7929 | 0.5560 | | No log | 6.0 | 96 | 0.5601 | 0.7822 | 0.5601 | | No log | 6.125 | 98 | 0.5313 | 0.7829 | 0.5313 | | No log | 6.25 | 100 | 0.5126 | 0.7686 | 0.5126 | | No log | 6.375 | 102 | 0.5279 | 0.7834 | 0.5279 | | No log | 6.5 | 104 | 0.5844 | 0.8096 | 0.5844 | | No log | 6.625 | 106 | 0.5860 | 0.8086 | 0.5860 | | No log | 6.75 | 108 | 0.5568 | 0.7963 | 0.5568 | | No log | 6.875 | 110 | 0.5406 | 0.7844 | 0.5406 | | No log | 7.0 | 112 | 0.5425 | 0.7887 | 0.5425 | | No log | 7.125 | 114 | 0.5630 | 0.8032 | 0.5630 | | No log | 7.25 | 116 | 0.5942 | 0.8088 | 0.5942 | | No log | 7.375 | 118 | 0.6014 | 0.8083 | 0.6014 | | No log | 7.5 | 120 | 0.5778 | 0.8081 | 0.5778 | | No log | 7.625 | 122 | 0.5335 | 0.7946 | 0.5335 | | No log | 7.75 | 124 | 0.5308 | 0.7931 | 0.5308 | | No log | 7.875 | 126 | 0.5490 | 0.7917 | 0.5490 | | No log | 8.0 | 128 | 0.5644 | 0.8045 | 0.5644 | | No log | 8.125 | 130 | 0.5800 | 0.8050 | 0.5800 | | No log | 8.25 | 132 | 0.5999 | 0.8207 | 0.5999 | | No log | 8.375 | 134 | 0.5872 | 0.8085 | 0.5872 | | No log | 8.5 | 136 | 0.5666 | 0.8035 | 0.5666 | | No log | 8.625 | 138 | 0.5568 | 0.8039 | 0.5568 | | No log | 8.75 | 140 | 0.5546 | 0.8000 | 0.5546 | | No log | 8.875 | 142 | 0.5612 | 0.8043 | 0.5612 | | No log | 9.0 | 144 | 0.5764 | 0.8146 | 0.5764 | | No log | 9.125 | 146 | 0.5943 | 0.8208 | 0.5943 | | No log | 9.25 | 148 | 0.6016 | 0.8226 | 0.6016 | | No log | 9.375 | 150 | 0.5924 | 0.8205 | 0.5924 | | No log | 9.5 | 152 | 0.5765 | 0.8109 | 0.5765 | | No log | 9.625 | 154 | 0.5638 | 0.8056 | 0.5638 | | No log | 9.75 | 156 | 0.5558 | 0.8031 | 0.5558 | | No log | 9.875 | 158 | 0.5516 | 0.7992 | 0.5516 | | No log | 10.0 | 160 | 0.5513 | 0.8014 | 0.5513 | ### Framework versions - Transformers 4.44.0 - Pytorch 2.4.0 - Datasets 2.21.0 - Tokenizers 0.19.1