---
base_model: aubmindlab/bert-base-arabertv02
tags:
- generated_from_trainer
model-index:
- name: arabert_cross_vocabulary_task5_fold5
  results: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# arabert_cross_vocabulary_task5_fold5

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: 0.3339
- Qwk: 0.8270
- Mse: 0.3339

## 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    | 3.7424          | 0.0021 | 3.7424 |
| No log        | 0.0702 | 4    | 1.7368          | 0.2488 | 1.7368 |
| No log        | 0.1053 | 6    | 0.8992          | 0.2497 | 0.8992 |
| No log        | 0.1404 | 8    | 0.7327          | 0.6064 | 0.7327 |
| No log        | 0.1754 | 10   | 0.8042          | 0.5559 | 0.8042 |
| No log        | 0.2105 | 12   | 0.9939          | 0.6771 | 0.9939 |
| No log        | 0.2456 | 14   | 0.9616          | 0.6986 | 0.9616 |
| No log        | 0.2807 | 16   | 0.8004          | 0.6308 | 0.8004 |
| No log        | 0.3158 | 18   | 0.6373          | 0.6016 | 0.6373 |
| No log        | 0.3509 | 20   | 0.6361          | 0.6470 | 0.6361 |
| No log        | 0.3860 | 22   | 0.5967          | 0.7168 | 0.5967 |
| No log        | 0.4211 | 24   | 0.4872          | 0.7622 | 0.4872 |
| No log        | 0.4561 | 26   | 0.4462          | 0.7864 | 0.4462 |
| No log        | 0.4912 | 28   | 0.4171          | 0.8029 | 0.4171 |
| No log        | 0.5263 | 30   | 0.3844          | 0.8207 | 0.3844 |
| No log        | 0.5614 | 32   | 0.4099          | 0.8533 | 0.4099 |
| No log        | 0.5965 | 34   | 0.4527          | 0.8592 | 0.4527 |
| No log        | 0.6316 | 36   | 0.4782          | 0.8573 | 0.4782 |
| No log        | 0.6667 | 38   | 0.4629          | 0.8620 | 0.4629 |
| No log        | 0.7018 | 40   | 0.3818          | 0.8464 | 0.3818 |
| No log        | 0.7368 | 42   | 0.3245          | 0.8055 | 0.3245 |
| No log        | 0.7719 | 44   | 0.3187          | 0.7617 | 0.3187 |
| No log        | 0.8070 | 46   | 0.3176          | 0.7494 | 0.3176 |
| No log        | 0.8421 | 48   | 0.3184          | 0.7414 | 0.3184 |
| No log        | 0.8772 | 50   | 0.3131          | 0.7715 | 0.3131 |
| No log        | 0.9123 | 52   | 0.3167          | 0.8053 | 0.3167 |
| No log        | 0.9474 | 54   | 0.3273          | 0.8208 | 0.3273 |
| No log        | 0.9825 | 56   | 0.3339          | 0.8270 | 0.3339 |


### Framework versions

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