---
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
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# 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