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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_fold1 |
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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_fold1 |
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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: 0.8118 |
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- Qwk: 0.4006 |
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- Mse: 0.8118 |
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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 | 3.3727 | 0.0150 | 3.3727 | |
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| No log | 0.25 | 4 | 1.5579 | 0.0418 | 1.5579 | |
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| No log | 0.375 | 6 | 0.8889 | 0.1524 | 0.8889 | |
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| No log | 0.5 | 8 | 0.7988 | 0.2568 | 0.7988 | |
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| No log | 0.625 | 10 | 0.9045 | 0.2445 | 0.9045 | |
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| No log | 0.75 | 12 | 1.3396 | 0.2180 | 1.3396 | |
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| No log | 0.875 | 14 | 0.8588 | 0.3519 | 0.8588 | |
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| No log | 1.0 | 16 | 0.5574 | 0.5527 | 0.5574 | |
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| No log | 1.125 | 18 | 0.5553 | 0.5503 | 0.5553 | |
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| No log | 1.25 | 20 | 0.8079 | 0.3672 | 0.8079 | |
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| No log | 1.375 | 22 | 1.3052 | 0.2456 | 1.3052 | |
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| No log | 1.5 | 24 | 0.9198 | 0.3104 | 0.9198 | |
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| No log | 1.625 | 26 | 0.5541 | 0.5285 | 0.5541 | |
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| No log | 1.75 | 28 | 0.5512 | 0.5290 | 0.5512 | |
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| No log | 1.875 | 30 | 0.6121 | 0.4768 | 0.6121 | |
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| No log | 2.0 | 32 | 0.8609 | 0.3129 | 0.8609 | |
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| No log | 2.125 | 34 | 0.9886 | 0.2731 | 0.9886 | |
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| No log | 2.25 | 36 | 0.8076 | 0.3795 | 0.8076 | |
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| No log | 2.375 | 38 | 0.6513 | 0.4757 | 0.6513 | |
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| No log | 2.5 | 40 | 0.6322 | 0.4656 | 0.6322 | |
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| No log | 2.625 | 42 | 0.8239 | 0.3721 | 0.8239 | |
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| No log | 2.75 | 44 | 0.8173 | 0.3657 | 0.8173 | |
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| No log | 2.875 | 46 | 0.5953 | 0.4602 | 0.5953 | |
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| No log | 3.0 | 48 | 0.4998 | 0.5491 | 0.4998 | |
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| No log | 3.125 | 50 | 0.4994 | 0.5388 | 0.4994 | |
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| No log | 3.25 | 52 | 0.5985 | 0.4558 | 0.5985 | |
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| No log | 3.375 | 54 | 0.8360 | 0.3362 | 0.8360 | |
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| No log | 3.5 | 56 | 0.7638 | 0.3694 | 0.7638 | |
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| No log | 3.625 | 58 | 0.5758 | 0.4882 | 0.5758 | |
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| No log | 3.75 | 60 | 0.5627 | 0.5091 | 0.5627 | |
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| No log | 3.875 | 62 | 0.6464 | 0.4616 | 0.6464 | |
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| No log | 4.0 | 64 | 0.7995 | 0.3939 | 0.7995 | |
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| No log | 4.125 | 66 | 0.8090 | 0.4038 | 0.8090 | |
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| No log | 4.25 | 68 | 0.7637 | 0.4270 | 0.7637 | |
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| No log | 4.375 | 70 | 0.6773 | 0.4614 | 0.6773 | |
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| No log | 4.5 | 72 | 0.6071 | 0.4596 | 0.6071 | |
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| No log | 4.625 | 74 | 0.6404 | 0.4305 | 0.6404 | |
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| No log | 4.75 | 76 | 0.7606 | 0.3850 | 0.7606 | |
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| No log | 4.875 | 78 | 0.7167 | 0.4134 | 0.7167 | |
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| No log | 5.0 | 80 | 0.6509 | 0.4134 | 0.6509 | |
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| No log | 5.125 | 82 | 0.6798 | 0.4551 | 0.6798 | |
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| No log | 5.25 | 84 | 0.7948 | 0.3986 | 0.7948 | |
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| No log | 5.375 | 86 | 0.8620 | 0.3562 | 0.8620 | |
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| No log | 5.5 | 88 | 0.8876 | 0.3559 | 0.8876 | |
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| No log | 5.625 | 90 | 0.7515 | 0.4248 | 0.7515 | |
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| No log | 5.75 | 92 | 0.7108 | 0.4577 | 0.7108 | |
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| No log | 5.875 | 94 | 0.7862 | 0.4061 | 0.7862 | |
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| No log | 6.0 | 96 | 0.8416 | 0.3952 | 0.8416 | |
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| No log | 6.125 | 98 | 0.7997 | 0.4122 | 0.7997 | |
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| No log | 6.25 | 100 | 0.8258 | 0.3932 | 0.8258 | |
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| No log | 6.375 | 102 | 0.7838 | 0.4124 | 0.7838 | |
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| No log | 6.5 | 104 | 0.7944 | 0.4076 | 0.7944 | |
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| No log | 6.625 | 106 | 0.8231 | 0.3830 | 0.8231 | |
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| No log | 6.75 | 108 | 0.7694 | 0.4134 | 0.7694 | |
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| No log | 6.875 | 110 | 0.7985 | 0.3792 | 0.7985 | |
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| No log | 7.0 | 112 | 0.8356 | 0.3632 | 0.8356 | |
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| No log | 7.125 | 114 | 0.8848 | 0.3450 | 0.8848 | |
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| No log | 7.25 | 116 | 0.8497 | 0.3620 | 0.8497 | |
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| No log | 7.375 | 118 | 0.7434 | 0.4183 | 0.7434 | |
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| No log | 7.5 | 120 | 0.7023 | 0.4781 | 0.7023 | |
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| No log | 7.625 | 122 | 0.7498 | 0.4429 | 0.7498 | |
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| No log | 7.75 | 124 | 0.9144 | 0.3785 | 0.9144 | |
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| No log | 7.875 | 126 | 1.0497 | 0.3405 | 1.0497 | |
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| No log | 8.0 | 128 | 1.0554 | 0.3506 | 1.0554 | |
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| No log | 8.125 | 130 | 0.9425 | 0.3693 | 0.9425 | |
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| No log | 8.25 | 132 | 0.8329 | 0.4324 | 0.8329 | |
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| No log | 8.375 | 134 | 0.7552 | 0.4623 | 0.7552 | |
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| No log | 8.5 | 136 | 0.7557 | 0.4559 | 0.7557 | |
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| No log | 8.625 | 138 | 0.7684 | 0.4526 | 0.7684 | |
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| No log | 8.75 | 140 | 0.8092 | 0.4060 | 0.8092 | |
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| No log | 8.875 | 142 | 0.8508 | 0.3842 | 0.8508 | |
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| No log | 9.0 | 144 | 0.8605 | 0.3817 | 0.8605 | |
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| No log | 9.125 | 146 | 0.8641 | 0.3822 | 0.8641 | |
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| No log | 9.25 | 148 | 0.8326 | 0.3939 | 0.8326 | |
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| No log | 9.375 | 150 | 0.8206 | 0.3946 | 0.8206 | |
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| No log | 9.5 | 152 | 0.7988 | 0.4013 | 0.7988 | |
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| No log | 9.625 | 154 | 0.7932 | 0.4040 | 0.7932 | |
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| No log | 9.75 | 156 | 0.7973 | 0.4040 | 0.7973 | |
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| No log | 9.875 | 158 | 0.8061 | 0.4006 | 0.8061 | |
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| No log | 10.0 | 160 | 0.8118 | 0.4006 | 0.8118 | |
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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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