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--- |
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base_model: aubmindlab/bert-base-arabertv02 |
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tags: |
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- generated_from_trainer |
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model-index: |
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- name: arabert_cross_organization_task6_fold4 |
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results: [] |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# arabert_cross_organization_task6_fold4 |
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This model is a fine-tuned version of [aubmindlab/bert-base-arabertv02](https://huggingface.co/aubmindlab/bert-base-arabertv02) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.5513 |
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- Qwk: 0.8014 |
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- Mse: 0.5513 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 2e-05 |
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- train_batch_size: 64 |
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- eval_batch_size: 64 |
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- seed: 42 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- num_epochs: 10 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Qwk | Mse | |
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|:-------------:|:-----:|:----:|:---------------:|:------:|:------:| |
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| No log | 0.125 | 2 | 1.7949 | 0.0785 | 1.7949 | |
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| No log | 0.25 | 4 | 1.3176 | 0.3355 | 1.3176 | |
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| No log | 0.375 | 6 | 1.0133 | 0.4987 | 1.0133 | |
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| No log | 0.5 | 8 | 0.8195 | 0.6242 | 0.8195 | |
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| No log | 0.625 | 10 | 0.6810 | 0.6733 | 0.6810 | |
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| No log | 0.75 | 12 | 0.6997 | 0.7476 | 0.6997 | |
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| No log | 0.875 | 14 | 0.8146 | 0.7416 | 0.8146 | |
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| No log | 1.0 | 16 | 0.7351 | 0.6641 | 0.7351 | |
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| No log | 1.125 | 18 | 0.6322 | 0.6459 | 0.6322 | |
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| No log | 1.25 | 20 | 0.6009 | 0.7063 | 0.6009 | |
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| No log | 1.375 | 22 | 0.6280 | 0.7695 | 0.6280 | |
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| No log | 1.5 | 24 | 0.6339 | 0.7783 | 0.6339 | |
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| No log | 1.625 | 26 | 0.6448 | 0.7702 | 0.6448 | |
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| No log | 1.75 | 28 | 0.5640 | 0.7779 | 0.5640 | |
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| No log | 1.875 | 30 | 0.5431 | 0.7439 | 0.5431 | |
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| No log | 2.0 | 32 | 0.5812 | 0.7734 | 0.5812 | |
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| No log | 2.125 | 34 | 0.6724 | 0.7815 | 0.6724 | |
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| No log | 2.25 | 36 | 0.7187 | 0.7729 | 0.7187 | |
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| No log | 2.375 | 38 | 0.5409 | 0.7541 | 0.5409 | |
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| No log | 2.5 | 40 | 0.5281 | 0.7293 | 0.5281 | |
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| No log | 2.625 | 42 | 0.6063 | 0.7901 | 0.6063 | |
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| No log | 2.75 | 44 | 0.6529 | 0.7684 | 0.6529 | |
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| No log | 2.875 | 46 | 0.5454 | 0.7558 | 0.5454 | |
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| No log | 3.0 | 48 | 0.5428 | 0.7521 | 0.5428 | |
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| No log | 3.125 | 50 | 0.6258 | 0.7773 | 0.6258 | |
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| No log | 3.25 | 52 | 0.6788 | 0.7753 | 0.6788 | |
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| No log | 3.375 | 54 | 0.5643 | 0.7870 | 0.5643 | |
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| No log | 3.5 | 56 | 0.5302 | 0.7485 | 0.5302 | |
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| No log | 3.625 | 58 | 0.5557 | 0.7880 | 0.5557 | |
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| No log | 3.75 | 60 | 0.6261 | 0.7858 | 0.6261 | |
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| No log | 3.875 | 62 | 0.5642 | 0.7914 | 0.5642 | |
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| No log | 4.0 | 64 | 0.5355 | 0.7849 | 0.5355 | |
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| No log | 4.125 | 66 | 0.5320 | 0.7895 | 0.5320 | |
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| No log | 4.25 | 68 | 0.5634 | 0.7939 | 0.5634 | |
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| No log | 4.375 | 70 | 0.5815 | 0.7876 | 0.5815 | |
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| No log | 4.5 | 72 | 0.5643 | 0.7947 | 0.5643 | |
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| No log | 4.625 | 74 | 0.5290 | 0.7923 | 0.5290 | |
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| No log | 4.75 | 76 | 0.5541 | 0.7948 | 0.5541 | |
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| No log | 4.875 | 78 | 0.6162 | 0.7857 | 0.6162 | |
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| No log | 5.0 | 80 | 0.5720 | 0.7885 | 0.5720 | |
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| No log | 5.125 | 82 | 0.5198 | 0.7815 | 0.5198 | |
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| No log | 5.25 | 84 | 0.5301 | 0.7775 | 0.5301 | |
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| No log | 5.375 | 86 | 0.5941 | 0.7837 | 0.5941 | |
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| No log | 5.5 | 88 | 0.5713 | 0.7985 | 0.5713 | |
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| No log | 5.625 | 90 | 0.5275 | 0.7835 | 0.5275 | |
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| No log | 5.75 | 92 | 0.5396 | 0.7877 | 0.5396 | |
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| No log | 5.875 | 94 | 0.5560 | 0.7929 | 0.5560 | |
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| No log | 6.0 | 96 | 0.5601 | 0.7822 | 0.5601 | |
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| No log | 6.125 | 98 | 0.5313 | 0.7829 | 0.5313 | |
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| No log | 6.25 | 100 | 0.5126 | 0.7686 | 0.5126 | |
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| No log | 6.375 | 102 | 0.5279 | 0.7834 | 0.5279 | |
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| No log | 6.5 | 104 | 0.5844 | 0.8096 | 0.5844 | |
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| No log | 6.625 | 106 | 0.5860 | 0.8086 | 0.5860 | |
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| No log | 6.75 | 108 | 0.5568 | 0.7963 | 0.5568 | |
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| No log | 6.875 | 110 | 0.5406 | 0.7844 | 0.5406 | |
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| No log | 7.0 | 112 | 0.5425 | 0.7887 | 0.5425 | |
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| No log | 7.125 | 114 | 0.5630 | 0.8032 | 0.5630 | |
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| No log | 7.25 | 116 | 0.5942 | 0.8088 | 0.5942 | |
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| No log | 7.375 | 118 | 0.6014 | 0.8083 | 0.6014 | |
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| No log | 7.5 | 120 | 0.5778 | 0.8081 | 0.5778 | |
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| No log | 7.625 | 122 | 0.5335 | 0.7946 | 0.5335 | |
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| No log | 7.75 | 124 | 0.5308 | 0.7931 | 0.5308 | |
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| No log | 7.875 | 126 | 0.5490 | 0.7917 | 0.5490 | |
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| No log | 8.0 | 128 | 0.5644 | 0.8045 | 0.5644 | |
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| No log | 8.125 | 130 | 0.5800 | 0.8050 | 0.5800 | |
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| No log | 8.25 | 132 | 0.5999 | 0.8207 | 0.5999 | |
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| No log | 8.375 | 134 | 0.5872 | 0.8085 | 0.5872 | |
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| No log | 8.5 | 136 | 0.5666 | 0.8035 | 0.5666 | |
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| No log | 8.625 | 138 | 0.5568 | 0.8039 | 0.5568 | |
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| No log | 8.75 | 140 | 0.5546 | 0.8000 | 0.5546 | |
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| No log | 8.875 | 142 | 0.5612 | 0.8043 | 0.5612 | |
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| No log | 9.0 | 144 | 0.5764 | 0.8146 | 0.5764 | |
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| No log | 9.125 | 146 | 0.5943 | 0.8208 | 0.5943 | |
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| No log | 9.25 | 148 | 0.6016 | 0.8226 | 0.6016 | |
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| No log | 9.375 | 150 | 0.5924 | 0.8205 | 0.5924 | |
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| No log | 9.5 | 152 | 0.5765 | 0.8109 | 0.5765 | |
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| No log | 9.625 | 154 | 0.5638 | 0.8056 | 0.5638 | |
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| No log | 9.75 | 156 | 0.5558 | 0.8031 | 0.5558 | |
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| No log | 9.875 | 158 | 0.5516 | 0.7992 | 0.5516 | |
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| No log | 10.0 | 160 | 0.5513 | 0.8014 | 0.5513 | |
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### Framework versions |
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- Transformers 4.44.0 |
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- Pytorch 2.4.0 |
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- Datasets 2.21.0 |
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- Tokenizers 0.19.1 |
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