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---
base_model: aubmindlab/bert-base-arabertv02
tags:
- generated_from_trainer
model-index:
- name: arabert_baseline_relevance_task1_fold0
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# arabert_baseline_relevance_task1_fold0
This model is a fine-tuned version of [aubmindlab/bert-base-arabertv02](https://huggingface.co/aubmindlab/bert-base-arabertv02) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1576
- Qwk: 0.2222
- Mse: 0.1601
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Qwk | Mse |
|:-------------:|:------:|:----:|:---------------:|:------:|:------:|
| No log | 0.3333 | 2 | 0.9197 | 0.0278 | 0.9116 |
| No log | 0.6667 | 4 | 0.1428 | 0.2105 | 0.1467 |
| No log | 1.0 | 6 | 0.1823 | 0.3467 | 0.1888 |
| No log | 1.3333 | 8 | 0.1817 | 0.0808 | 0.1888 |
| No log | 1.6667 | 10 | 0.2156 | 0.0 | 0.2206 |
| No log | 2.0 | 12 | 0.1825 | 0.0 | 0.1849 |
| No log | 2.3333 | 14 | 0.1725 | 0.0392 | 0.1727 |
| No log | 2.6667 | 16 | 0.2068 | 0.0392 | 0.2060 |
| No log | 3.0 | 18 | 0.1522 | 0.0808 | 0.1531 |
| No log | 3.3333 | 20 | 0.1588 | 0.1250 | 0.1623 |
| No log | 3.6667 | 22 | 0.1635 | 0.1250 | 0.1682 |
| No log | 4.0 | 24 | 0.1609 | 0.0808 | 0.1658 |
| No log | 4.3333 | 26 | 0.1526 | 0.0808 | 0.1565 |
| No log | 4.6667 | 28 | 0.1503 | 0.0392 | 0.1529 |
| No log | 5.0 | 30 | 0.1548 | 0.0392 | 0.1558 |
| No log | 5.3333 | 32 | 0.1602 | 0.0808 | 0.1608 |
| No log | 5.6667 | 34 | 0.1480 | 0.1720 | 0.1493 |
| No log | 6.0 | 36 | 0.1509 | 0.2759 | 0.1526 |
| No log | 6.3333 | 38 | 0.1504 | 0.3333 | 0.1525 |
| No log | 6.6667 | 40 | 0.1502 | 0.3333 | 0.1526 |
| No log | 7.0 | 42 | 0.1518 | 0.1720 | 0.1539 |
| No log | 7.3333 | 44 | 0.1602 | 0.1250 | 0.1617 |
| No log | 7.6667 | 46 | 0.1798 | 0.1250 | 0.1806 |
| No log | 8.0 | 48 | 0.1921 | 0.2364 | 0.1927 |
| No log | 8.3333 | 50 | 0.1893 | 0.2364 | 0.1900 |
| No log | 8.6667 | 52 | 0.1769 | 0.2222 | 0.1778 |
| No log | 9.0 | 54 | 0.1663 | 0.2759 | 0.1678 |
| No log | 9.3333 | 56 | 0.1609 | 0.2759 | 0.1629 |
| No log | 9.6667 | 58 | 0.1584 | 0.2222 | 0.1608 |
| No log | 10.0 | 60 | 0.1576 | 0.2222 | 0.1601 |
### Framework versions
- Transformers 4.44.0
- Pytorch 2.4.0
- Datasets 2.21.0
- Tokenizers 0.19.1
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