--- base_model: aubmindlab/bert-base-arabertv02 tags: - generated_from_trainer model-index: - name: arabert_cross_organization_task4_fold0 results: [] --- # arabert_cross_organization_task4_fold0 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: 0.7387 - Qwk: 0.5925 - Mse: 0.7387 ## 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 | 3.7003 | 0.0594 | 3.6971 | | No log | 0.2353 | 4 | 2.4103 | 0.0127 | 2.4085 | | No log | 0.3529 | 6 | 1.7582 | 0.2177 | 1.7571 | | No log | 0.4706 | 8 | 1.5666 | 0.3222 | 1.5658 | | No log | 0.5882 | 10 | 0.9860 | 0.4576 | 0.9866 | | No log | 0.7059 | 12 | 0.9585 | 0.5399 | 0.9591 | | No log | 0.8235 | 14 | 1.3953 | 0.4078 | 1.3949 | | No log | 0.9412 | 16 | 1.6278 | 0.3501 | 1.6269 | | No log | 1.0588 | 18 | 1.2485 | 0.4062 | 1.2480 | | No log | 1.1765 | 20 | 0.9325 | 0.4653 | 0.9323 | | No log | 1.2941 | 22 | 0.8750 | 0.5046 | 0.8747 | | No log | 1.4118 | 24 | 0.9684 | 0.4851 | 0.9679 | | No log | 1.5294 | 26 | 1.3655 | 0.3843 | 1.3644 | | No log | 1.6471 | 28 | 1.0699 | 0.4636 | 1.0693 | | No log | 1.7647 | 30 | 0.7323 | 0.6264 | 0.7322 | | No log | 1.8824 | 32 | 0.7409 | 0.6340 | 0.7408 | | No log | 2.0 | 34 | 0.7342 | 0.6187 | 0.7341 | | No log | 2.1176 | 36 | 0.7935 | 0.5765 | 0.7932 | | No log | 2.2353 | 38 | 1.0980 | 0.4535 | 1.0972 | | No log | 2.3529 | 40 | 1.0511 | 0.4714 | 1.0504 | | No log | 2.4706 | 42 | 0.8556 | 0.5268 | 0.8552 | | No log | 2.5882 | 44 | 0.7684 | 0.5983 | 0.7681 | | No log | 2.7059 | 46 | 0.8207 | 0.5721 | 0.8206 | | No log | 2.8235 | 48 | 0.9052 | 0.5593 | 0.9050 | | No log | 2.9412 | 50 | 0.8417 | 0.5743 | 0.8416 | | No log | 3.0588 | 52 | 0.7671 | 0.6038 | 0.7671 | | No log | 3.1765 | 54 | 0.7859 | 0.5949 | 0.7859 | | No log | 3.2941 | 56 | 0.8844 | 0.5560 | 0.8842 | | No log | 3.4118 | 58 | 0.9141 | 0.5516 | 0.9137 | | No log | 3.5294 | 60 | 0.7828 | 0.5943 | 0.7825 | | No log | 3.6471 | 62 | 0.7194 | 0.6082 | 0.7191 | | No log | 3.7647 | 64 | 0.7235 | 0.6070 | 0.7233 | | No log | 3.8824 | 66 | 0.7606 | 0.5926 | 0.7604 | | No log | 4.0 | 68 | 0.9394 | 0.5299 | 0.9388 | | No log | 4.1176 | 70 | 0.9642 | 0.5092 | 0.9636 | | No log | 4.2353 | 72 | 0.7479 | 0.5973 | 0.7477 | | No log | 4.3529 | 74 | 0.6642 | 0.6133 | 0.6642 | | No log | 4.4706 | 76 | 0.6706 | 0.6129 | 0.6705 | | No log | 4.5882 | 78 | 0.7184 | 0.6006 | 0.7183 | | No log | 4.7059 | 80 | 0.7525 | 0.5921 | 0.7523 | | No log | 4.8235 | 82 | 0.7429 | 0.5934 | 0.7428 | | No log | 4.9412 | 84 | 0.7555 | 0.5857 | 0.7554 | | No log | 5.0588 | 86 | 0.7166 | 0.5978 | 0.7165 | | No log | 5.1765 | 88 | 0.7034 | 0.5949 | 0.7033 | | No log | 5.2941 | 90 | 0.7179 | 0.5869 | 0.7178 | | No log | 5.4118 | 92 | 0.8419 | 0.5574 | 0.8416 | | No log | 5.5294 | 94 | 0.9040 | 0.5229 | 0.9037 | | No log | 5.6471 | 96 | 0.8841 | 0.5357 | 0.8839 | | No log | 5.7647 | 98 | 0.7383 | 0.5893 | 0.7383 | | No log | 5.8824 | 100 | 0.6787 | 0.6146 | 0.6789 | | No log | 6.0 | 102 | 0.6933 | 0.6105 | 0.6933 | | No log | 6.1176 | 104 | 0.7751 | 0.5812 | 0.7749 | | No log | 6.2353 | 106 | 0.7938 | 0.5700 | 0.7936 | | No log | 6.3529 | 108 | 0.7205 | 0.6067 | 0.7204 | | No log | 6.4706 | 110 | 0.6695 | 0.6121 | 0.6695 | | No log | 6.5882 | 112 | 0.6759 | 0.6171 | 0.6758 | | No log | 6.7059 | 114 | 0.6919 | 0.6099 | 0.6918 | | No log | 6.8235 | 116 | 0.7467 | 0.5990 | 0.7465 | | No log | 6.9412 | 118 | 0.7453 | 0.5978 | 0.7452 | | No log | 7.0588 | 120 | 0.7515 | 0.5967 | 0.7514 | | No log | 7.1765 | 122 | 0.7221 | 0.6021 | 0.7221 | | No log | 7.2941 | 124 | 0.7307 | 0.6043 | 0.7307 | | No log | 7.4118 | 126 | 0.7413 | 0.6043 | 0.7414 | | No log | 7.5294 | 128 | 0.7247 | 0.6004 | 0.7249 | | No log | 7.6471 | 130 | 0.7088 | 0.6058 | 0.7091 | | No log | 7.7647 | 132 | 0.7232 | 0.6037 | 0.7235 | | No log | 7.8824 | 134 | 0.7747 | 0.5935 | 0.7748 | | No log | 8.0 | 136 | 0.8677 | 0.5568 | 0.8676 | | No log | 8.1176 | 138 | 0.8769 | 0.5521 | 0.8768 | | No log | 8.2353 | 140 | 0.8117 | 0.5845 | 0.8116 | | No log | 8.3529 | 142 | 0.7379 | 0.5954 | 0.7379 | | No log | 8.4706 | 144 | 0.7203 | 0.6024 | 0.7203 | | No log | 8.5882 | 146 | 0.7255 | 0.6036 | 0.7255 | | No log | 8.7059 | 148 | 0.7177 | 0.6024 | 0.7177 | | No log | 8.8235 | 150 | 0.7099 | 0.6036 | 0.7099 | | No log | 8.9412 | 152 | 0.7149 | 0.6036 | 0.7150 | | No log | 9.0588 | 154 | 0.7323 | 0.5976 | 0.7323 | | No log | 9.1765 | 156 | 0.7453 | 0.5919 | 0.7453 | | No log | 9.2941 | 158 | 0.7533 | 0.5901 | 0.7533 | | No log | 9.4118 | 160 | 0.7538 | 0.5902 | 0.7538 | | No log | 9.5294 | 162 | 0.7490 | 0.5903 | 0.7490 | | No log | 9.6471 | 164 | 0.7411 | 0.5952 | 0.7411 | | No log | 9.7647 | 166 | 0.7407 | 0.5952 | 0.7407 | | No log | 9.8824 | 168 | 0.7383 | 0.5925 | 0.7383 | | No log | 10.0 | 170 | 0.7387 | 0.5925 | 0.7387 | ### Framework versions - Transformers 4.44.0 - Pytorch 2.4.0 - Datasets 2.21.0 - Tokenizers 0.19.1