---
base_model: aubmindlab/bert-base-arabertv02
tags:
- generated_from_trainer
model-index:
- name: arabert_cross_relevance_task7_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_cross_relevance_task7_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: 0.2223
- Qwk: 0.0339
- Mse: 0.2223

## 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: 1

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Qwk    | Mse    |
|:-------------:|:------:|:----:|:---------------:|:------:|:------:|
| No log        | 0.0351 | 2    | 0.4577          | 0.0305 | 0.4577 |
| No log        | 0.0702 | 4    | 0.2924          | 0.0268 | 0.2924 |
| No log        | 0.1053 | 6    | 0.6165          | 0.0339 | 0.6165 |
| No log        | 0.1404 | 8    | 1.1332          | 0.0071 | 1.1332 |
| No log        | 0.1754 | 10   | 1.0089          | 0.0084 | 1.0089 |
| No log        | 0.2105 | 12   | 0.6577          | 0.0383 | 0.6577 |
| No log        | 0.2456 | 14   | 0.3893          | 0.0245 | 0.3893 |
| No log        | 0.2807 | 16   | 0.2631          | 0.0199 | 0.2631 |
| No log        | 0.3158 | 18   | 0.2402          | 0.0199 | 0.2402 |
| No log        | 0.3509 | 20   | 0.2586          | 0.0199 | 0.2586 |
| No log        | 0.3860 | 22   | 0.3150          | 0.0288 | 0.3150 |
| No log        | 0.4211 | 24   | 0.4282          | 0.0399 | 0.4282 |
| No log        | 0.4561 | 26   | 0.4430          | 0.0382 | 0.4430 |
| No log        | 0.4912 | 28   | 0.4205          | 0.0399 | 0.4205 |
| No log        | 0.5263 | 30   | 0.3631          | 0.0433 | 0.3631 |
| No log        | 0.5614 | 32   | 0.3063          | 0.0507 | 0.3063 |
| No log        | 0.5965 | 34   | 0.3162          | 0.0488 | 0.3162 |
| No log        | 0.6316 | 36   | 0.3646          | 0.0451 | 0.3646 |
| No log        | 0.6667 | 38   | 0.3766          | 0.0433 | 0.3766 |
| No log        | 0.7018 | 40   | 0.3470          | 0.0469 | 0.3470 |
| No log        | 0.7368 | 42   | 0.3331          | 0.0436 | 0.3331 |
| No log        | 0.7719 | 44   | 0.3000          | 0.0495 | 0.3000 |
| No log        | 0.8070 | 46   | 0.2692          | 0.0439 | 0.2692 |
| No log        | 0.8421 | 48   | 0.2579          | 0.0381 | 0.2579 |
| No log        | 0.8772 | 50   | 0.2450          | 0.0401 | 0.2450 |
| No log        | 0.9123 | 52   | 0.2306          | 0.0339 | 0.2306 |
| No log        | 0.9474 | 54   | 0.2223          | 0.0339 | 0.2223 |
| No log        | 0.9825 | 56   | 0.2223          | 0.0339 | 0.2223 |


### Framework versions

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