---
library_name: transformers
base_model: aubmindlab/bert-base-arabertv2
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: bert-base-arabertv2_D3Lex_CE_19levels
  results: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# bert-base-arabertv2_D3Lex_CE_19levels

This model is a fine-tuned version of [aubmindlab/bert-base-arabertv2](https://huggingface.co/aubmindlab/bert-base-arabertv2) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.8342
- Macro F1: 0.4364
- Macro Precision: 0.4424
- Macro Recall: 0.4454
- Accuracy: 0.5140
- Accuracy With Margin: 0.6744
- Distance: 1.2527
- Quadratic weighted kappa: 0.7889
- Accuracy 7: 0.6179
- Accuracy 5: 0.6653
- Accuracy 3: 0.7363

## 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: 5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 6
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Macro F1 | Macro Precision | Macro Recall | Accuracy | Accuracy With Margin | Distance | Quadratic weighted kappa | Accuracy 7 | Accuracy 5 | Accuracy 3 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------------:|:------------:|:--------:|:--------------------:|:--------:|:------------------------:|:----------:|:----------:|:----------:|
| 1.8254        | 1.0   | 857  | 1.4573          | 0.3478   | 0.3741          | 0.3804       | 0.5044   | 0.6513               | 1.3171   | 0.7790                   | 0.6223     | 0.6773     | 0.7415     |
| 1.2699        | 2.0   | 1714 | 1.4409          | 0.4133   | 0.4330          | 0.4316       | 0.5157   | 0.6688               | 1.2666   | 0.7825                   | 0.6230     | 0.6802     | 0.7497     |
| 1.0213        | 3.0   | 2571 | 1.4714          | 0.4157   | 0.4210          | 0.4276       | 0.5272   | 0.6711               | 1.2473   | 0.7889                   | 0.6308     | 0.6780     | 0.7458     |
| 0.8308        | 4.0   | 3428 | 1.6169          | 0.4074   | 0.4422          | 0.4110       | 0.5171   | 0.6724               | 1.2573   | 0.7848                   | 0.6194     | 0.6663     | 0.7397     |
| 0.6128        | 5.0   | 4285 | 1.7657          | 0.4222   | 0.4375          | 0.4349       | 0.5164   | 0.6737               | 1.2575   | 0.7861                   | 0.6208     | 0.6703     | 0.7391     |
| 0.4786        | 6.0   | 5142 | 1.8342          | 0.4364   | 0.4424          | 0.4454       | 0.5140   | 0.6744               | 1.2527   | 0.7889                   | 0.6179     | 0.6653     | 0.7363     |


### Framework versions

- Transformers 4.53.2
- Pytorch 2.6.0+cu124
- Datasets 4.0.0
- Tokenizers 0.21.2