--- base_model: aubmindlab/bert-base-arabertv02 tags: - generated_from_trainer model-index: - name: arabert_cross_organization_task1_fold4 results: [] --- # arabert_cross_organization_task1_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: 0.4588 - Qwk: 0.6885 - Mse: 0.4588 ## 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 | 3.0429 | 0.0044 | 3.0429 | | No log | 0.25 | 4 | 1.6578 | 0.1373 | 1.6578 | | No log | 0.375 | 6 | 0.9445 | 0.3393 | 0.9445 | | No log | 0.5 | 8 | 0.7446 | 0.4700 | 0.7446 | | No log | 0.625 | 10 | 0.8109 | 0.4239 | 0.8109 | | No log | 0.75 | 12 | 0.5652 | 0.6068 | 0.5652 | | No log | 0.875 | 14 | 0.6877 | 0.6129 | 0.6877 | | No log | 1.0 | 16 | 0.5401 | 0.6022 | 0.5401 | | No log | 1.125 | 18 | 0.5571 | 0.5613 | 0.5571 | | No log | 1.25 | 20 | 0.4854 | 0.6440 | 0.4854 | | No log | 1.375 | 22 | 0.5443 | 0.7366 | 0.5443 | | No log | 1.5 | 24 | 0.5077 | 0.7444 | 0.5077 | | No log | 1.625 | 26 | 0.5015 | 0.6266 | 0.5015 | | No log | 1.75 | 28 | 0.5012 | 0.6164 | 0.5012 | | No log | 1.875 | 30 | 0.4504 | 0.7043 | 0.4504 | | No log | 2.0 | 32 | 0.4864 | 0.7187 | 0.4864 | | No log | 2.125 | 34 | 0.4305 | 0.7243 | 0.4305 | | No log | 2.25 | 36 | 0.4572 | 0.6579 | 0.4572 | | No log | 2.375 | 38 | 0.4545 | 0.7032 | 0.4545 | | No log | 2.5 | 40 | 0.4159 | 0.7123 | 0.4159 | | No log | 2.625 | 42 | 0.4122 | 0.7591 | 0.4122 | | No log | 2.75 | 44 | 0.4424 | 0.7617 | 0.4424 | | No log | 2.875 | 46 | 0.4110 | 0.7600 | 0.4110 | | No log | 3.0 | 48 | 0.3993 | 0.7372 | 0.3993 | | No log | 3.125 | 50 | 0.3990 | 0.7391 | 0.3990 | | No log | 3.25 | 52 | 0.3923 | 0.7306 | 0.3923 | | No log | 3.375 | 54 | 0.4375 | 0.7685 | 0.4375 | | No log | 3.5 | 56 | 0.4628 | 0.7698 | 0.4628 | | No log | 3.625 | 58 | 0.4089 | 0.7365 | 0.4089 | | No log | 3.75 | 60 | 0.4113 | 0.7238 | 0.4113 | | No log | 3.875 | 62 | 0.4117 | 0.7308 | 0.4117 | | No log | 4.0 | 64 | 0.4183 | 0.7175 | 0.4183 | | No log | 4.125 | 66 | 0.4326 | 0.7175 | 0.4326 | | No log | 4.25 | 68 | 0.4439 | 0.7360 | 0.4439 | | No log | 4.375 | 70 | 0.4530 | 0.7375 | 0.4530 | | No log | 4.5 | 72 | 0.4458 | 0.7040 | 0.4458 | | No log | 4.625 | 74 | 0.4431 | 0.7054 | 0.4431 | | No log | 4.75 | 76 | 0.4403 | 0.6980 | 0.4403 | | No log | 4.875 | 78 | 0.4350 | 0.7144 | 0.4350 | | No log | 5.0 | 80 | 0.4311 | 0.7511 | 0.4311 | | No log | 5.125 | 82 | 0.4257 | 0.7418 | 0.4257 | | No log | 5.25 | 84 | 0.4298 | 0.7174 | 0.4298 | | No log | 5.375 | 86 | 0.4420 | 0.6877 | 0.4420 | | No log | 5.5 | 88 | 0.4344 | 0.7174 | 0.4344 | | No log | 5.625 | 90 | 0.4324 | 0.7146 | 0.4324 | | No log | 5.75 | 92 | 0.4363 | 0.7566 | 0.4363 | | No log | 5.875 | 94 | 0.4499 | 0.7689 | 0.4499 | | No log | 6.0 | 96 | 0.4217 | 0.7367 | 0.4217 | | No log | 6.125 | 98 | 0.4252 | 0.7237 | 0.4252 | | No log | 6.25 | 100 | 0.4235 | 0.7141 | 0.4235 | | No log | 6.375 | 102 | 0.4211 | 0.7230 | 0.4211 | | No log | 6.5 | 104 | 0.4285 | 0.7493 | 0.4285 | | No log | 6.625 | 106 | 0.4367 | 0.7530 | 0.4367 | | No log | 6.75 | 108 | 0.4214 | 0.7457 | 0.4214 | | No log | 6.875 | 110 | 0.4380 | 0.6930 | 0.4380 | | No log | 7.0 | 112 | 0.4555 | 0.6727 | 0.4555 | | No log | 7.125 | 114 | 0.4358 | 0.6947 | 0.4358 | | No log | 7.25 | 116 | 0.4270 | 0.7277 | 0.4270 | | No log | 7.375 | 118 | 0.4349 | 0.7457 | 0.4349 | | No log | 7.5 | 120 | 0.4430 | 0.7382 | 0.4430 | | No log | 7.625 | 122 | 0.4539 | 0.7257 | 0.4539 | | No log | 7.75 | 124 | 0.4623 | 0.7204 | 0.4623 | | No log | 7.875 | 126 | 0.4640 | 0.7110 | 0.4640 | | No log | 8.0 | 128 | 0.4644 | 0.7115 | 0.4644 | | No log | 8.125 | 130 | 0.4639 | 0.7095 | 0.4639 | | No log | 8.25 | 132 | 0.4612 | 0.7073 | 0.4612 | | No log | 8.375 | 134 | 0.4652 | 0.6865 | 0.4652 | | No log | 8.5 | 136 | 0.4689 | 0.6753 | 0.4689 | | No log | 8.625 | 138 | 0.4608 | 0.6849 | 0.4608 | | No log | 8.75 | 140 | 0.4553 | 0.6907 | 0.4553 | | No log | 8.875 | 142 | 0.4538 | 0.6930 | 0.4538 | | No log | 9.0 | 144 | 0.4537 | 0.7172 | 0.4537 | | No log | 9.125 | 146 | 0.4564 | 0.7273 | 0.4564 | | No log | 9.25 | 148 | 0.4582 | 0.7294 | 0.4582 | | No log | 9.375 | 150 | 0.4572 | 0.7267 | 0.4572 | | No log | 9.5 | 152 | 0.4559 | 0.7093 | 0.4559 | | No log | 9.625 | 154 | 0.4566 | 0.7000 | 0.4566 | | No log | 9.75 | 156 | 0.4582 | 0.6885 | 0.4582 | | No log | 9.875 | 158 | 0.4588 | 0.6885 | 0.4588 | | No log | 10.0 | 160 | 0.4588 | 0.6885 | 0.4588 | ### Framework versions - Transformers 4.44.0 - Pytorch 2.4.0 - Datasets 2.21.0 - Tokenizers 0.19.1