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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_task2_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_task2_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: 0.1850 |
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- Qwk: 0.0597 |
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- Mse: 0.1935 |
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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 | 0.9871 | 0.0205 | 0.9763 | |
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| No log | 0.6667 | 4 | 0.1822 | 0.1049 | 0.1846 | |
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| No log | 1.0 | 6 | 0.2694 | -0.0176 | 0.2773 | |
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| No log | 1.3333 | 8 | 0.2612 | -0.0791 | 0.2700 | |
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| No log | 1.6667 | 10 | 0.2291 | 0.0187 | 0.2405 | |
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| No log | 2.0 | 12 | 0.1605 | 0.0755 | 0.1675 | |
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| No log | 2.3333 | 14 | 0.1556 | 0.1049 | 0.1617 | |
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| No log | 2.6667 | 16 | 0.1455 | 0.1370 | 0.1500 | |
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| No log | 3.0 | 18 | 0.1386 | 0.0 | 0.1414 | |
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| No log | 3.3333 | 20 | 0.1423 | 0.0 | 0.1448 | |
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| No log | 3.6667 | 22 | 0.1393 | 0.0 | 0.1429 | |
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| No log | 4.0 | 24 | 0.1807 | 0.4815 | 0.1875 | |
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| No log | 4.3333 | 26 | 0.2622 | 0.0 | 0.2718 | |
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| No log | 4.6667 | 28 | 0.2372 | 0.0 | 0.2476 | |
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| No log | 5.0 | 30 | 0.1765 | -0.1290 | 0.1845 | |
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| No log | 5.3333 | 32 | 0.1633 | 0.0483 | 0.1688 | |
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| No log | 5.6667 | 34 | 0.1551 | 0.0755 | 0.1613 | |
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| No log | 6.0 | 36 | 0.1689 | 0.1674 | 0.1757 | |
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| No log | 6.3333 | 38 | 0.1736 | 0.2687 | 0.1798 | |
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| No log | 6.6667 | 40 | 0.1678 | 0.2150 | 0.1732 | |
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| No log | 7.0 | 42 | 0.1581 | 0.1250 | 0.1631 | |
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| No log | 7.3333 | 44 | 0.1540 | 0.0526 | 0.1591 | |
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| No log | 7.6667 | 46 | 0.1536 | 0.0526 | 0.1590 | |
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| No log | 8.0 | 48 | 0.1603 | 0.1250 | 0.1666 | |
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| No log | 8.3333 | 50 | 0.1716 | 0.2150 | 0.1790 | |
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| No log | 8.6667 | 52 | 0.1797 | 0.0597 | 0.1878 | |
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| No log | 9.0 | 54 | 0.1828 | 0.0597 | 0.1911 | |
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| No log | 9.3333 | 56 | 0.1841 | 0.0597 | 0.1925 | |
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| No log | 9.6667 | 58 | 0.1849 | 0.0597 | 0.1933 | |
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| No log | 10.0 | 60 | 0.1850 | 0.0597 | 0.1935 | |
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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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