---
library_name: transformers
base_model: aubmindlab/bert-base-arabertv2
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: bert-base-arabertv2_Word_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_Word_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: 1.8378
- Macro F1: 0.4341
- Macro Precision: 0.4818
- Macro Recall: 0.4366
- Accuracy: 0.5124
- Accuracy With Margin: 0.6620
- Distance: 1.3402
- Quadratic weighted kappa: 0.7507
- Accuracy 7: 0.6048
- Accuracy 5: 0.6461
- Accuracy 3: 0.7196

## 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: 64
- eval_batch_size: 16
- seed: 42
- 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

### 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------------:|:------------:|:--------:|:--------------------:|:--------:|:------------------------:|:----------:|:----------:|:----------:|
| 1.821         | 1.0   | 857  | 1.5119          | 0.3566   | 0.3707          | 0.3839       | 0.4959   | 0.6328               | 1.4432   | 0.7348                   | 0.5997     | 0.6486     | 0.7185     |
| 1.2796        | 2.0   | 1714 | 1.4789          | 0.3907   | 0.4085          | 0.4181       | 0.5166   | 0.6643               | 1.3157   | 0.7614                   | 0.6148     | 0.6614     | 0.7363     |
| 1.0458        | 3.0   | 2571 | 1.4990          | 0.4274   | 0.4328          | 0.4307       | 0.5224   | 0.6618               | 1.3138   | 0.7608                   | 0.6141     | 0.6565     | 0.7272     |
| 0.8524        | 4.0   | 3428 | 1.6133          | 0.4380   | 0.4963          | 0.4362       | 0.5215   | 0.6625               | 1.3294   | 0.7523                   | 0.6107     | 0.6536     | 0.7244     |
| 0.6475        | 5.0   | 4285 | 1.7562          | 0.4295   | 0.4317          | 0.4342       | 0.5166   | 0.6618               | 1.3260   | 0.7545                   | 0.6068     | 0.6483     | 0.7237     |
| 0.5186        | 6.0   | 5142 | 1.8378          | 0.4341   | 0.4818          | 0.4366       | 0.5124   | 0.6620               | 1.3402   | 0.7507                   | 0.6048     | 0.6461     | 0.7196     |


### Framework versions

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