---
base_model: aubmindlab/bert-base-arabertv02
tags:
- generated_from_trainer
model-index:
- name: arabert_baseline_relevance_task2_fold1
  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_task2_fold1

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: 0.1509
- Qwk: 0.1747
- Mse: 0.1416

## 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    | 0.7029          | -0.0252 | 0.7093 |
| No log        | 0.6667 | 4    | 0.1462          | -0.1951 | 0.1344 |
| No log        | 1.0    | 6    | 0.2440          | 0.0     | 0.2429 |
| No log        | 1.3333 | 8    | 0.1359          | 0.0     | 0.1325 |
| No log        | 1.6667 | 10   | 0.1162          | 0.0219  | 0.1100 |
| No log        | 2.0    | 12   | 0.1085          | 0.0345  | 0.1016 |
| No log        | 2.3333 | 14   | 0.1197          | 0.0     | 0.1139 |
| No log        | 2.6667 | 16   | 0.1465          | 0.0     | 0.1427 |
| No log        | 3.0    | 18   | 0.1499          | 0.0     | 0.1462 |
| No log        | 3.3333 | 20   | 0.1428          | 0.0     | 0.1389 |
| No log        | 3.6667 | 22   | 0.1187          | 0.0483  | 0.1125 |
| No log        | 4.0    | 24   | 0.1323          | 0.1217  | 0.1220 |
| No log        | 4.3333 | 26   | 0.1627          | -0.1667 | 0.1498 |
| No log        | 4.6667 | 28   | 0.1511          | 0.2075  | 0.1392 |
| No log        | 5.0    | 30   | 0.1317          | 0.1217  | 0.1222 |
| No log        | 5.3333 | 32   | 0.1331          | 0.0637  | 0.1256 |
| No log        | 5.6667 | 34   | 0.1407          | 0.0105  | 0.1341 |
| No log        | 6.0    | 36   | 0.1544          | 0.0105  | 0.1484 |
| No log        | 6.3333 | 38   | 0.1639          | 0.0219  | 0.1582 |
| No log        | 6.6667 | 40   | 0.1595          | 0.0483  | 0.1529 |
| No log        | 7.0    | 42   | 0.1480          | 0.0808  | 0.1402 |
| No log        | 7.3333 | 44   | 0.1440          | 0.1000  | 0.1358 |
| No log        | 7.6667 | 46   | 0.1462          | 0.1000  | 0.1382 |
| No log        | 8.0    | 48   | 0.1490          | 0.1747  | 0.1404 |
| No log        | 8.3333 | 50   | 0.1535          | 0.1747  | 0.1441 |
| No log        | 8.6667 | 52   | 0.1570          | 0.0094  | 0.1473 |
| No log        | 9.0    | 54   | 0.1553          | 0.0094  | 0.1456 |
| No log        | 9.3333 | 56   | 0.1529          | 0.1747  | 0.1436 |
| No log        | 9.6667 | 58   | 0.1515          | 0.1747  | 0.1422 |
| No log        | 10.0   | 60   | 0.1509          | 0.1747  | 0.1416 |


### Framework versions

- Transformers 4.44.0
- Pytorch 2.4.0
- Datasets 2.21.0
- Tokenizers 0.19.1