---
base_model: aubmindlab/bert-base-arabertv02
tags:
- generated_from_trainer
model-index:
- name: arabert_cross_vocabulary_task5_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_vocabulary_task5_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: 0.9484
- Qwk: 0.2440
- Mse: 0.9484

## 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    | 4.0285          | -0.0026 | 4.0285 |
| No log        | 0.0702 | 4    | 1.9411          | 0.0411  | 1.9411 |
| No log        | 0.1053 | 6    | 1.1570          | 0.0820  | 1.1570 |
| No log        | 0.1404 | 8    | 1.0653          | 0.0860  | 1.0653 |
| No log        | 0.1754 | 10   | 1.5739          | 0.0992  | 1.5739 |
| No log        | 0.2105 | 12   | 1.9614          | 0.1146  | 1.9614 |
| No log        | 0.2456 | 14   | 1.2558          | 0.1733  | 1.2558 |
| No log        | 0.2807 | 16   | 0.6707          | 0.2820  | 0.6707 |
| No log        | 0.3158 | 18   | 0.5871          | 0.3622  | 0.5871 |
| No log        | 0.3509 | 20   | 0.5667          | 0.4017  | 0.5667 |
| No log        | 0.3860 | 22   | 0.6361          | 0.3990  | 0.6361 |
| No log        | 0.4211 | 24   | 0.9147          | 0.3506  | 0.9147 |
| No log        | 0.4561 | 26   | 1.1469          | 0.3145  | 1.1469 |
| No log        | 0.4912 | 28   | 1.6471          | 0.2494  | 1.6471 |
| No log        | 0.5263 | 30   | 1.7498          | 0.2180  | 1.7498 |
| No log        | 0.5614 | 32   | 1.2305          | 0.2731  | 1.2305 |
| No log        | 0.5965 | 34   | 0.7970          | 0.3383  | 0.7970 |
| No log        | 0.6316 | 36   | 0.5754          | 0.4131  | 0.5754 |
| No log        | 0.6667 | 38   | 0.5427          | 0.4406  | 0.5427 |
| No log        | 0.7018 | 40   | 0.5537          | 0.4387  | 0.5537 |
| No log        | 0.7368 | 42   | 0.5912          | 0.4077  | 0.5912 |
| No log        | 0.7719 | 44   | 0.6607          | 0.3453  | 0.6607 |
| No log        | 0.8070 | 46   | 0.7386          | 0.3013  | 0.7386 |
| No log        | 0.8421 | 48   | 0.8577          | 0.2819  | 0.8577 |
| No log        | 0.8772 | 50   | 0.9544          | 0.2404  | 0.9544 |
| No log        | 0.9123 | 52   | 0.9673          | 0.2440  | 0.9673 |
| No log        | 0.9474 | 54   | 0.9583          | 0.2440  | 0.9583 |
| No log        | 0.9825 | 56   | 0.9484          | 0.2440  | 0.9484 |


### Framework versions

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