--- base_model: aubmindlab/bert-base-arabertv02 tags: - generated_from_trainer model-index: - name: arabert_cross_organization_task5_fold6 results: [] --- # arabert_cross_organization_task5_fold6 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: 0.6701 - Qwk: 0.5451 - Mse: 0.6691 ## 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 | 2.7122 | 0.0168 | 2.7155 | | No log | 0.25 | 4 | 1.4877 | 0.0689 | 1.4872 | | No log | 0.375 | 6 | 0.8699 | 0.3929 | 0.8691 | | No log | 0.5 | 8 | 0.8864 | 0.4400 | 0.8851 | | No log | 0.625 | 10 | 0.6806 | 0.4650 | 0.6795 | | No log | 0.75 | 12 | 0.5592 | 0.6237 | 0.5585 | | No log | 0.875 | 14 | 0.5290 | 0.6160 | 0.5281 | | No log | 1.0 | 16 | 0.6313 | 0.5082 | 0.6296 | | No log | 1.125 | 18 | 0.5309 | 0.6193 | 0.5297 | | No log | 1.25 | 20 | 0.5510 | 0.7458 | 0.5512 | | No log | 1.375 | 22 | 0.5012 | 0.7370 | 0.5009 | | No log | 1.5 | 24 | 0.5238 | 0.5768 | 0.5223 | | No log | 1.625 | 26 | 0.4818 | 0.6624 | 0.4807 | | No log | 1.75 | 28 | 0.4667 | 0.6909 | 0.4659 | | No log | 1.875 | 30 | 0.4599 | 0.6935 | 0.4592 | | No log | 2.0 | 32 | 0.4866 | 0.6274 | 0.4855 | | No log | 2.125 | 34 | 0.5237 | 0.5988 | 0.5225 | | No log | 2.25 | 36 | 0.5515 | 0.6989 | 0.5510 | | No log | 2.375 | 38 | 0.5435 | 0.6886 | 0.5430 | | No log | 2.5 | 40 | 0.5180 | 0.6261 | 0.5171 | | No log | 2.625 | 42 | 0.4674 | 0.7076 | 0.4669 | | No log | 2.75 | 44 | 0.4765 | 0.7329 | 0.4764 | | No log | 2.875 | 46 | 0.4676 | 0.7156 | 0.4672 | | No log | 3.0 | 48 | 0.5297 | 0.5702 | 0.5287 | | No log | 3.125 | 50 | 0.5444 | 0.5777 | 0.5434 | | No log | 3.25 | 52 | 0.5202 | 0.6120 | 0.5195 | | No log | 3.375 | 54 | 0.5330 | 0.6250 | 0.5324 | | No log | 3.5 | 56 | 0.5282 | 0.6303 | 0.5276 | | No log | 3.625 | 58 | 0.5263 | 0.6051 | 0.5256 | | No log | 3.75 | 60 | 0.4900 | 0.6534 | 0.4896 | | No log | 3.875 | 62 | 0.4962 | 0.7418 | 0.4960 | | No log | 4.0 | 64 | 0.5088 | 0.6350 | 0.5084 | | No log | 4.125 | 66 | 0.6180 | 0.5459 | 0.6170 | | No log | 4.25 | 68 | 0.7619 | 0.4764 | 0.7604 | | No log | 4.375 | 70 | 0.6897 | 0.5533 | 0.6885 | | No log | 4.5 | 72 | 0.5780 | 0.6397 | 0.5775 | | No log | 4.625 | 74 | 0.5272 | 0.6578 | 0.5269 | | No log | 4.75 | 76 | 0.5132 | 0.6359 | 0.5126 | | No log | 4.875 | 78 | 0.5879 | 0.5243 | 0.5869 | | No log | 5.0 | 80 | 0.5987 | 0.5203 | 0.5977 | | No log | 5.125 | 82 | 0.5609 | 0.5675 | 0.5602 | | No log | 5.25 | 84 | 0.5591 | 0.6400 | 0.5588 | | No log | 5.375 | 86 | 0.5977 | 0.6016 | 0.5971 | | No log | 5.5 | 88 | 0.6286 | 0.5753 | 0.6277 | | No log | 5.625 | 90 | 0.6304 | 0.5617 | 0.6293 | | No log | 5.75 | 92 | 0.6330 | 0.5239 | 0.6318 | | No log | 5.875 | 94 | 0.5530 | 0.5293 | 0.5520 | | No log | 6.0 | 96 | 0.4931 | 0.6645 | 0.4927 | | No log | 6.125 | 98 | 0.5014 | 0.7126 | 0.5012 | | No log | 6.25 | 100 | 0.5246 | 0.6717 | 0.5242 | | No log | 6.375 | 102 | 0.6077 | 0.5609 | 0.6068 | | No log | 6.5 | 104 | 0.7359 | 0.5061 | 0.7344 | | No log | 6.625 | 106 | 0.7769 | 0.5024 | 0.7753 | | No log | 6.75 | 108 | 0.7401 | 0.5143 | 0.7386 | | No log | 6.875 | 110 | 0.6620 | 0.5573 | 0.6609 | | No log | 7.0 | 112 | 0.6087 | 0.5571 | 0.6078 | | No log | 7.125 | 114 | 0.5917 | 0.5262 | 0.5907 | | No log | 7.25 | 116 | 0.5659 | 0.5230 | 0.5650 | | No log | 7.375 | 118 | 0.5449 | 0.5886 | 0.5442 | | No log | 7.5 | 120 | 0.5472 | 0.6127 | 0.5467 | | No log | 7.625 | 122 | 0.5711 | 0.6000 | 0.5706 | | No log | 7.75 | 124 | 0.6023 | 0.5779 | 0.6016 | | No log | 7.875 | 126 | 0.6306 | 0.5660 | 0.6297 | | No log | 8.0 | 128 | 0.6471 | 0.5684 | 0.6461 | | No log | 8.125 | 130 | 0.6631 | 0.5684 | 0.6621 | | No log | 8.25 | 132 | 0.6608 | 0.5684 | 0.6598 | | No log | 8.375 | 134 | 0.6696 | 0.5678 | 0.6686 | | No log | 8.5 | 136 | 0.6628 | 0.5569 | 0.6618 | | No log | 8.625 | 138 | 0.6415 | 0.5560 | 0.6405 | | No log | 8.75 | 140 | 0.6188 | 0.5664 | 0.6180 | | No log | 8.875 | 142 | 0.5990 | 0.5782 | 0.5984 | | No log | 9.0 | 144 | 0.5948 | 0.6002 | 0.5943 | | No log | 9.125 | 146 | 0.5994 | 0.5935 | 0.5989 | | No log | 9.25 | 148 | 0.6090 | 0.5803 | 0.6084 | | No log | 9.375 | 150 | 0.6188 | 0.5734 | 0.6182 | | No log | 9.5 | 152 | 0.6329 | 0.5705 | 0.6322 | | No log | 9.625 | 154 | 0.6491 | 0.5642 | 0.6482 | | No log | 9.75 | 156 | 0.6638 | 0.5451 | 0.6628 | | No log | 9.875 | 158 | 0.6687 | 0.5451 | 0.6678 | | No log | 10.0 | 160 | 0.6701 | 0.5451 | 0.6691 | ### Framework versions - Transformers 4.44.0 - Pytorch 2.4.0 - Datasets 2.21.0 - Tokenizers 0.19.1