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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_baseline_relevance_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_relevance_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: 0.1576 |
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- Qwk: 0.2222 |
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- Mse: 0.1601 |
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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: 16 |
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- eval_batch_size: 16 |
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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 | 0.3333 | 2 | 0.9197 | 0.0278 | 0.9116 | |
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| No log | 0.6667 | 4 | 0.1428 | 0.2105 | 0.1467 | |
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| No log | 1.0 | 6 | 0.1823 | 0.3467 | 0.1888 | |
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| No log | 1.3333 | 8 | 0.1817 | 0.0808 | 0.1888 | |
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| No log | 1.6667 | 10 | 0.2156 | 0.0 | 0.2206 | |
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| No log | 2.0 | 12 | 0.1825 | 0.0 | 0.1849 | |
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| No log | 2.3333 | 14 | 0.1725 | 0.0392 | 0.1727 | |
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| No log | 2.6667 | 16 | 0.2068 | 0.0392 | 0.2060 | |
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| No log | 3.0 | 18 | 0.1522 | 0.0808 | 0.1531 | |
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| No log | 3.3333 | 20 | 0.1588 | 0.1250 | 0.1623 | |
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| No log | 3.6667 | 22 | 0.1635 | 0.1250 | 0.1682 | |
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| No log | 4.0 | 24 | 0.1609 | 0.0808 | 0.1658 | |
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| No log | 4.3333 | 26 | 0.1526 | 0.0808 | 0.1565 | |
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| No log | 4.6667 | 28 | 0.1503 | 0.0392 | 0.1529 | |
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| No log | 5.0 | 30 | 0.1548 | 0.0392 | 0.1558 | |
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| No log | 5.3333 | 32 | 0.1602 | 0.0808 | 0.1608 | |
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| No log | 5.6667 | 34 | 0.1480 | 0.1720 | 0.1493 | |
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| No log | 6.0 | 36 | 0.1509 | 0.2759 | 0.1526 | |
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| No log | 6.3333 | 38 | 0.1504 | 0.3333 | 0.1525 | |
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| No log | 6.6667 | 40 | 0.1502 | 0.3333 | 0.1526 | |
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| No log | 7.0 | 42 | 0.1518 | 0.1720 | 0.1539 | |
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| No log | 7.3333 | 44 | 0.1602 | 0.1250 | 0.1617 | |
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| No log | 7.6667 | 46 | 0.1798 | 0.1250 | 0.1806 | |
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| No log | 8.0 | 48 | 0.1921 | 0.2364 | 0.1927 | |
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| No log | 8.3333 | 50 | 0.1893 | 0.2364 | 0.1900 | |
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| No log | 8.6667 | 52 | 0.1769 | 0.2222 | 0.1778 | |
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| No log | 9.0 | 54 | 0.1663 | 0.2759 | 0.1678 | |
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| No log | 9.3333 | 56 | 0.1609 | 0.2759 | 0.1629 | |
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| No log | 9.6667 | 58 | 0.1584 | 0.2222 | 0.1608 | |
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| No log | 10.0 | 60 | 0.1576 | 0.2222 | 0.1601 | |
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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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