--- base_model: aubmindlab/bert-base-arabertv02 tags: - generated_from_trainer model-index: - name: arabert_cross_organization_task2_fold4 results: [] --- # arabert_cross_organization_task2_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: 0.4343 - Qwk: 0.7442 - Mse: 0.4343 ## 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 | 3.0559 | 0.0132 | 3.0559 | | No log | 0.25 | 4 | 1.5018 | 0.1018 | 1.5018 | | No log | 0.375 | 6 | 0.9403 | 0.3496 | 0.9403 | | No log | 0.5 | 8 | 1.0512 | 0.4227 | 1.0512 | | No log | 0.625 | 10 | 0.7038 | 0.5121 | 0.7038 | | No log | 0.75 | 12 | 0.7727 | 0.4566 | 0.7727 | | No log | 0.875 | 14 | 0.5989 | 0.5213 | 0.5989 | | No log | 1.0 | 16 | 0.6428 | 0.6397 | 0.6428 | | No log | 1.125 | 18 | 0.6331 | 0.7095 | 0.6331 | | No log | 1.25 | 20 | 0.5024 | 0.6478 | 0.5024 | | No log | 1.375 | 22 | 0.7624 | 0.4770 | 0.7624 | | No log | 1.5 | 24 | 0.5793 | 0.5485 | 0.5793 | | No log | 1.625 | 26 | 0.4887 | 0.6546 | 0.4887 | | No log | 1.75 | 28 | 0.5482 | 0.6854 | 0.5482 | | No log | 1.875 | 30 | 0.5328 | 0.7446 | 0.5328 | | No log | 2.0 | 32 | 0.4476 | 0.6785 | 0.4476 | | No log | 2.125 | 34 | 0.5184 | 0.5745 | 0.5184 | | No log | 2.25 | 36 | 0.4772 | 0.6201 | 0.4772 | | No log | 2.375 | 38 | 0.4229 | 0.7095 | 0.4229 | | No log | 2.5 | 40 | 0.4747 | 0.7500 | 0.4747 | | No log | 2.625 | 42 | 0.4556 | 0.7201 | 0.4556 | | No log | 2.75 | 44 | 0.4703 | 0.6407 | 0.4703 | | No log | 2.875 | 46 | 0.4875 | 0.6566 | 0.4875 | | No log | 3.0 | 48 | 0.5070 | 0.7290 | 0.5070 | | No log | 3.125 | 50 | 0.4950 | 0.7764 | 0.4950 | | No log | 3.25 | 52 | 0.4192 | 0.7444 | 0.4192 | | No log | 3.375 | 54 | 0.4132 | 0.6919 | 0.4132 | | No log | 3.5 | 56 | 0.4024 | 0.7128 | 0.4024 | | No log | 3.625 | 58 | 0.4094 | 0.7451 | 0.4094 | | No log | 3.75 | 60 | 0.4675 | 0.7828 | 0.4675 | | No log | 3.875 | 62 | 0.4559 | 0.7636 | 0.4559 | | No log | 4.0 | 64 | 0.4150 | 0.7449 | 0.4150 | | No log | 4.125 | 66 | 0.3994 | 0.7551 | 0.3994 | | No log | 4.25 | 68 | 0.3872 | 0.7513 | 0.3872 | | No log | 4.375 | 70 | 0.3951 | 0.7719 | 0.3951 | | No log | 4.5 | 72 | 0.4536 | 0.7801 | 0.4536 | | No log | 4.625 | 74 | 0.4695 | 0.7891 | 0.4695 | | No log | 4.75 | 76 | 0.4253 | 0.7787 | 0.4253 | | No log | 4.875 | 78 | 0.3967 | 0.7809 | 0.3967 | | No log | 5.0 | 80 | 0.3954 | 0.7506 | 0.3954 | | No log | 5.125 | 82 | 0.4062 | 0.7844 | 0.4062 | | No log | 5.25 | 84 | 0.4096 | 0.7688 | 0.4096 | | No log | 5.375 | 86 | 0.4305 | 0.7167 | 0.4305 | | No log | 5.5 | 88 | 0.4607 | 0.6647 | 0.4607 | | No log | 5.625 | 90 | 0.4776 | 0.6876 | 0.4776 | | No log | 5.75 | 92 | 0.4996 | 0.7150 | 0.4996 | | No log | 5.875 | 94 | 0.5241 | 0.7677 | 0.5241 | | No log | 6.0 | 96 | 0.5059 | 0.7933 | 0.5059 | | No log | 6.125 | 98 | 0.4470 | 0.7830 | 0.4470 | | No log | 6.25 | 100 | 0.4010 | 0.7665 | 0.4010 | | No log | 6.375 | 102 | 0.4147 | 0.6921 | 0.4147 | | No log | 6.5 | 104 | 0.4226 | 0.6845 | 0.4226 | | No log | 6.625 | 106 | 0.4193 | 0.7197 | 0.4193 | | No log | 6.75 | 108 | 0.4395 | 0.7571 | 0.4395 | | No log | 6.875 | 110 | 0.4602 | 0.7536 | 0.4602 | | No log | 7.0 | 112 | 0.4569 | 0.7332 | 0.4569 | | No log | 7.125 | 114 | 0.4359 | 0.7109 | 0.4359 | | No log | 7.25 | 116 | 0.4245 | 0.7097 | 0.4245 | | No log | 7.375 | 118 | 0.4142 | 0.7397 | 0.4142 | | No log | 7.5 | 120 | 0.4102 | 0.7558 | 0.4102 | | No log | 7.625 | 122 | 0.4179 | 0.7845 | 0.4179 | | No log | 7.75 | 124 | 0.4170 | 0.7876 | 0.4170 | | No log | 7.875 | 126 | 0.4173 | 0.7876 | 0.4173 | | No log | 8.0 | 128 | 0.4157 | 0.7629 | 0.4157 | | No log | 8.125 | 130 | 0.4165 | 0.7617 | 0.4165 | | No log | 8.25 | 132 | 0.4198 | 0.7551 | 0.4198 | | No log | 8.375 | 134 | 0.4256 | 0.7560 | 0.4256 | | No log | 8.5 | 136 | 0.4285 | 0.7405 | 0.4285 | | No log | 8.625 | 138 | 0.4320 | 0.7413 | 0.4320 | | No log | 8.75 | 140 | 0.4361 | 0.7522 | 0.4361 | | No log | 8.875 | 142 | 0.4387 | 0.7512 | 0.4387 | | No log | 9.0 | 144 | 0.4377 | 0.7502 | 0.4377 | | No log | 9.125 | 146 | 0.4356 | 0.7447 | 0.4356 | | No log | 9.25 | 148 | 0.4354 | 0.7421 | 0.4354 | | No log | 9.375 | 150 | 0.4355 | 0.7421 | 0.4355 | | No log | 9.5 | 152 | 0.4365 | 0.7421 | 0.4365 | | No log | 9.625 | 154 | 0.4364 | 0.7421 | 0.4364 | | No log | 9.75 | 156 | 0.4354 | 0.7509 | 0.4354 | | No log | 9.875 | 158 | 0.4347 | 0.7442 | 0.4347 | | No log | 10.0 | 160 | 0.4343 | 0.7442 | 0.4343 | ### Framework versions - Transformers 4.44.0 - Pytorch 2.4.0 - Datasets 2.21.0 - Tokenizers 0.19.1