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--- |
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library_name: transformers |
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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_baseline_organization_task1_fold0 |
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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_baseline_organization_task1_fold0 |
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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: 0.6571 |
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- Qwk: 0.7618 |
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- Mse: 0.6571 |
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- Rmse: 0.8106 |
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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: 8 |
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- eval_batch_size: 8 |
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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 | Rmse | |
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|:-------------:|:------:|:----:|:---------------:|:-------:|:------:|:------:| |
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| No log | 0.1818 | 2 | 4.7070 | -0.0581 | 4.7070 | 2.1696 | |
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| No log | 0.3636 | 4 | 2.5400 | 0.0 | 2.5400 | 1.5937 | |
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| No log | 0.5455 | 6 | 1.5937 | 0.1750 | 1.5937 | 1.2624 | |
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| No log | 0.7273 | 8 | 1.0172 | 0.1582 | 1.0172 | 1.0086 | |
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| No log | 0.9091 | 10 | 0.9129 | 0.4929 | 0.9129 | 0.9555 | |
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| No log | 1.0909 | 12 | 0.8654 | 0.4763 | 0.8654 | 0.9302 | |
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| No log | 1.2727 | 14 | 0.8549 | 0.5268 | 0.8549 | 0.9246 | |
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| No log | 1.4545 | 16 | 0.7991 | 0.4934 | 0.7991 | 0.8939 | |
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| No log | 1.6364 | 18 | 0.8510 | 0.5288 | 0.8510 | 0.9225 | |
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| No log | 1.8182 | 20 | 0.8834 | 0.5288 | 0.8834 | 0.9399 | |
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| No log | 2.0 | 22 | 0.9534 | 0.5304 | 0.9534 | 0.9764 | |
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| No log | 2.1818 | 24 | 0.8760 | 0.5698 | 0.8760 | 0.9360 | |
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| No log | 2.3636 | 26 | 0.7263 | 0.6182 | 0.7263 | 0.8522 | |
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| No log | 2.5455 | 28 | 0.7632 | 0.5093 | 0.7632 | 0.8736 | |
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| No log | 2.7273 | 30 | 0.7407 | 0.5845 | 0.7407 | 0.8606 | |
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| No log | 2.9091 | 32 | 0.8857 | 0.5674 | 0.8857 | 0.9411 | |
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| No log | 3.0909 | 34 | 0.9844 | 0.6301 | 0.9844 | 0.9922 | |
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| No log | 3.2727 | 36 | 0.9963 | 0.6301 | 0.9963 | 0.9981 | |
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| No log | 3.4545 | 38 | 0.8665 | 0.6209 | 0.8665 | 0.9308 | |
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| No log | 3.6364 | 40 | 0.7832 | 0.6354 | 0.7832 | 0.8850 | |
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| No log | 3.8182 | 42 | 0.8425 | 0.6715 | 0.8425 | 0.9179 | |
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| No log | 4.0 | 44 | 0.8849 | 0.5591 | 0.8849 | 0.9407 | |
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| No log | 4.1818 | 46 | 0.8290 | 0.5862 | 0.8290 | 0.9105 | |
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| No log | 4.3636 | 48 | 0.7263 | 0.6866 | 0.7263 | 0.8523 | |
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| No log | 4.5455 | 50 | 0.7922 | 0.7786 | 0.7922 | 0.8901 | |
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| No log | 4.7273 | 52 | 0.9204 | 0.7037 | 0.9204 | 0.9594 | |
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| No log | 4.9091 | 54 | 0.8995 | 0.6836 | 0.8995 | 0.9484 | |
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| No log | 5.0909 | 56 | 0.7549 | 0.6968 | 0.7549 | 0.8688 | |
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| No log | 5.2727 | 58 | 0.6694 | 0.6539 | 0.6694 | 0.8182 | |
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| No log | 5.4545 | 60 | 0.6618 | 0.6102 | 0.6618 | 0.8135 | |
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| No log | 5.6364 | 62 | 0.6895 | 0.7449 | 0.6895 | 0.8304 | |
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| No log | 5.8182 | 64 | 0.7854 | 0.7181 | 0.7854 | 0.8863 | |
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| No log | 6.0 | 66 | 0.8328 | 0.7181 | 0.8328 | 0.9126 | |
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| No log | 6.1818 | 68 | 0.8134 | 0.6764 | 0.8134 | 0.9019 | |
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| No log | 6.3636 | 70 | 0.8151 | 0.7363 | 0.8151 | 0.9028 | |
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| No log | 6.5455 | 72 | 0.7853 | 0.7363 | 0.7853 | 0.8862 | |
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| No log | 6.7273 | 74 | 0.7443 | 0.7786 | 0.7443 | 0.8627 | |
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| No log | 6.9091 | 76 | 0.7261 | 0.7618 | 0.7261 | 0.8521 | |
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| No log | 7.0909 | 78 | 0.7315 | 0.7008 | 0.7315 | 0.8553 | |
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| No log | 7.2727 | 80 | 0.7472 | 0.7181 | 0.7472 | 0.8644 | |
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| No log | 7.4545 | 82 | 0.7769 | 0.7181 | 0.7769 | 0.8814 | |
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| No log | 7.6364 | 84 | 0.7755 | 0.6893 | 0.7755 | 0.8807 | |
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| No log | 7.8182 | 86 | 0.7231 | 0.7008 | 0.7231 | 0.8504 | |
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| No log | 8.0 | 88 | 0.6836 | 0.7008 | 0.6836 | 0.8268 | |
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| No log | 8.1818 | 90 | 0.6640 | 0.7618 | 0.6640 | 0.8148 | |
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| No log | 8.3636 | 92 | 0.6503 | 0.7618 | 0.6503 | 0.8064 | |
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| No log | 8.5455 | 94 | 0.6490 | 0.7371 | 0.6490 | 0.8056 | |
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| No log | 8.7273 | 96 | 0.6511 | 0.7618 | 0.6511 | 0.8069 | |
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| No log | 8.9091 | 98 | 0.6544 | 0.7618 | 0.6544 | 0.8090 | |
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| No log | 9.0909 | 100 | 0.6537 | 0.7618 | 0.6537 | 0.8085 | |
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| No log | 9.2727 | 102 | 0.6535 | 0.7618 | 0.6535 | 0.8084 | |
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| No log | 9.4545 | 104 | 0.6564 | 0.7618 | 0.6564 | 0.8102 | |
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| No log | 9.6364 | 106 | 0.6574 | 0.7618 | 0.6574 | 0.8108 | |
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| No log | 9.8182 | 108 | 0.6573 | 0.7618 | 0.6573 | 0.8107 | |
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| No log | 10.0 | 110 | 0.6571 | 0.7618 | 0.6571 | 0.8106 | |
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### Framework versions |
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- Transformers 4.44.2 |
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- Pytorch 2.4.0+cu118 |
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- Datasets 2.21.0 |
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- Tokenizers 0.19.1 |
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