--- base_model: aubmindlab/bert-base-arabertv02 tags: - generated_from_trainer model-index: - name: arabert_cross_organization_task4_fold4 results: [] --- # arabert_cross_organization_task4_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: 0.5141 - Qwk: 0.7810 - Mse: 0.5141 ## 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 | 0.1111 | 2 | 1.8597 | 0.1122 | 1.8597 | | No log | 0.2222 | 4 | 1.3020 | 0.0409 | 1.3020 | | No log | 0.3333 | 6 | 1.0459 | 0.3503 | 1.0459 | | No log | 0.4444 | 8 | 0.7138 | 0.4906 | 0.7138 | | No log | 0.5556 | 10 | 0.5900 | 0.6224 | 0.5900 | | No log | 0.6667 | 12 | 0.5708 | 0.6214 | 0.5708 | | No log | 0.7778 | 14 | 0.4881 | 0.6661 | 0.4881 | | No log | 0.8889 | 16 | 0.4843 | 0.6880 | 0.4843 | | No log | 1.0 | 18 | 0.5772 | 0.7185 | 0.5772 | | No log | 1.1111 | 20 | 0.4344 | 0.7515 | 0.4344 | | No log | 1.2222 | 22 | 0.4173 | 0.6853 | 0.4173 | | No log | 1.3333 | 24 | 0.4326 | 0.7470 | 0.4326 | | No log | 1.4444 | 26 | 0.6327 | 0.7329 | 0.6327 | | No log | 1.5556 | 28 | 0.6486 | 0.7640 | 0.6486 | | No log | 1.6667 | 30 | 0.4510 | 0.7633 | 0.4510 | | No log | 1.7778 | 32 | 0.3885 | 0.7569 | 0.3885 | | No log | 1.8889 | 34 | 0.4049 | 0.7722 | 0.4049 | | No log | 2.0 | 36 | 0.5466 | 0.7900 | 0.5466 | | No log | 2.1111 | 38 | 0.5445 | 0.7886 | 0.5445 | | No log | 2.2222 | 40 | 0.4445 | 0.7553 | 0.4445 | | No log | 2.3333 | 42 | 0.4182 | 0.7437 | 0.4182 | | No log | 2.4444 | 44 | 0.4202 | 0.7536 | 0.4202 | | No log | 2.5556 | 46 | 0.5364 | 0.7929 | 0.5364 | | No log | 2.6667 | 48 | 0.6070 | 0.7880 | 0.6070 | | No log | 2.7778 | 50 | 0.4960 | 0.7859 | 0.4960 | | No log | 2.8889 | 52 | 0.4044 | 0.7719 | 0.4044 | | No log | 3.0 | 54 | 0.3938 | 0.7606 | 0.3938 | | No log | 3.1111 | 56 | 0.4669 | 0.7947 | 0.4669 | | No log | 3.2222 | 58 | 0.5343 | 0.7820 | 0.5343 | | No log | 3.3333 | 60 | 0.4763 | 0.7853 | 0.4763 | | No log | 3.4444 | 62 | 0.4091 | 0.7835 | 0.4091 | | No log | 3.5556 | 64 | 0.4119 | 0.7882 | 0.4119 | | No log | 3.6667 | 66 | 0.4525 | 0.7813 | 0.4525 | | No log | 3.7778 | 68 | 0.4761 | 0.7828 | 0.4761 | | No log | 3.8889 | 70 | 0.4893 | 0.7931 | 0.4893 | | No log | 4.0 | 72 | 0.4435 | 0.7862 | 0.4435 | | No log | 4.1111 | 74 | 0.4754 | 0.7918 | 0.4754 | | No log | 4.2222 | 76 | 0.5004 | 0.7931 | 0.5004 | | No log | 4.3333 | 78 | 0.5554 | 0.8090 | 0.5554 | | No log | 4.4444 | 80 | 0.5319 | 0.7947 | 0.5319 | | No log | 4.5556 | 82 | 0.4459 | 0.7781 | 0.4459 | | No log | 4.6667 | 84 | 0.4355 | 0.7725 | 0.4355 | | No log | 4.7778 | 86 | 0.4699 | 0.7823 | 0.4699 | | No log | 4.8889 | 88 | 0.4860 | 0.7900 | 0.4860 | | No log | 5.0 | 90 | 0.4400 | 0.7892 | 0.4400 | | No log | 5.1111 | 92 | 0.4221 | 0.7376 | 0.4221 | | No log | 5.2222 | 94 | 0.4264 | 0.7879 | 0.4264 | | No log | 5.3333 | 96 | 0.4728 | 0.8106 | 0.4728 | | No log | 5.4444 | 98 | 0.5254 | 0.8043 | 0.5254 | | No log | 5.5556 | 100 | 0.4876 | 0.8048 | 0.4876 | | No log | 5.6667 | 102 | 0.4400 | 0.7767 | 0.4400 | | No log | 5.7778 | 104 | 0.4191 | 0.7487 | 0.4191 | | No log | 5.8889 | 106 | 0.4281 | 0.7643 | 0.4281 | | No log | 6.0 | 108 | 0.4819 | 0.7888 | 0.4819 | | No log | 6.1111 | 110 | 0.5487 | 0.8063 | 0.5487 | | No log | 6.2222 | 112 | 0.6060 | 0.7903 | 0.6060 | | No log | 6.3333 | 114 | 0.5618 | 0.7847 | 0.5618 | | No log | 6.4444 | 116 | 0.5080 | 0.7689 | 0.5080 | | No log | 6.5556 | 118 | 0.4883 | 0.7543 | 0.4883 | | No log | 6.6667 | 120 | 0.4979 | 0.7597 | 0.4979 | | No log | 6.7778 | 122 | 0.5155 | 0.7757 | 0.5155 | | No log | 6.8889 | 124 | 0.5239 | 0.7883 | 0.5239 | | No log | 7.0 | 126 | 0.5025 | 0.7973 | 0.5025 | | No log | 7.1111 | 128 | 0.4784 | 0.7894 | 0.4784 | | No log | 7.2222 | 130 | 0.4608 | 0.7714 | 0.4608 | | No log | 7.3333 | 132 | 0.4592 | 0.7608 | 0.4592 | | No log | 7.4444 | 134 | 0.4736 | 0.7898 | 0.4736 | | No log | 7.5556 | 136 | 0.5099 | 0.7905 | 0.5099 | | No log | 7.6667 | 138 | 0.5575 | 0.8010 | 0.5575 | | No log | 7.7778 | 140 | 0.5556 | 0.8167 | 0.5556 | | No log | 7.8889 | 142 | 0.5181 | 0.7957 | 0.5181 | | No log | 8.0 | 144 | 0.4691 | 0.7885 | 0.4691 | | No log | 8.1111 | 146 | 0.4424 | 0.7890 | 0.4424 | | No log | 8.2222 | 148 | 0.4411 | 0.7803 | 0.4411 | | No log | 8.3333 | 150 | 0.4646 | 0.7859 | 0.4646 | | No log | 8.4444 | 152 | 0.4939 | 0.8070 | 0.4939 | | No log | 8.5556 | 154 | 0.5186 | 0.8172 | 0.5186 | | No log | 8.6667 | 156 | 0.5307 | 0.8195 | 0.5307 | | No log | 8.7778 | 158 | 0.5184 | 0.8146 | 0.5184 | | No log | 8.8889 | 160 | 0.4898 | 0.8099 | 0.4898 | | No log | 9.0 | 162 | 0.4741 | 0.7957 | 0.4741 | | No log | 9.1111 | 164 | 0.4724 | 0.7948 | 0.4724 | | No log | 9.2222 | 166 | 0.4828 | 0.7937 | 0.4828 | | No log | 9.3333 | 168 | 0.4861 | 0.7937 | 0.4861 | | No log | 9.4444 | 170 | 0.4940 | 0.7848 | 0.4940 | | No log | 9.5556 | 172 | 0.5042 | 0.7810 | 0.5042 | | No log | 9.6667 | 174 | 0.5129 | 0.7810 | 0.5129 | | No log | 9.7778 | 176 | 0.5142 | 0.7810 | 0.5142 | | No log | 9.8889 | 178 | 0.5135 | 0.7810 | 0.5135 | | No log | 10.0 | 180 | 0.5141 | 0.7810 | 0.5141 | ### Framework versions - Transformers 4.44.0 - Pytorch 2.4.0 - Datasets 2.21.0 - Tokenizers 0.19.1