---
base_model: aubmindlab/bert-base-arabert
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: results
  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. -->

# results

This model is a fine-tuned version of [aubmindlab/bert-base-arabert](https://huggingface.co/aubmindlab/bert-base-arabert) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.4485
- Accuracy: 0.7656
- Precision: 0.7688
- Recall: 0.7656
- F1: 0.7650
- Mrr: 0.8440

## 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: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 320
- num_epochs: 12

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Accuracy | Precision | Recall | F1     | Mrr    |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:---------:|:------:|:------:|:------:|
| 0.9496        | 1.0   | 2250  | 0.9448          | 0.69     | 0.7197    | 0.69   | 0.6896 | 0.8003 |
| 0.7839        | 2.0   | 4500  | 0.8385          | 0.7      | 0.7302    | 0.7    | 0.7032 | 0.8101 |
| 0.4602        | 3.0   | 6750  | 0.9599          | 0.745    | 0.7524    | 0.745  | 0.7421 | 0.8346 |
| 0.4453        | 4.0   | 9000  | 0.9992          | 0.7325   | 0.7474    | 0.7325 | 0.7353 | 0.8342 |
| 0.3919        | 5.0   | 11250 | 1.2636          | 0.7425   | 0.7551    | 0.7425 | 0.7413 | 0.8312 |
| 0.313         | 6.0   | 13500 | 1.3639          | 0.7625   | 0.7679    | 0.7625 | 0.7628 | 0.8442 |
| 0.2186        | 7.0   | 15750 | 1.6281          | 0.745    | 0.7566    | 0.745  | 0.7461 | 0.8369 |
| 0.1942        | 8.0   | 18000 | 1.5611          | 0.775    | 0.7822    | 0.775  | 0.7752 | 0.8486 |
| 0.128         | 9.0   | 20250 | 1.7601          | 0.74     | 0.7504    | 0.74   | 0.7412 | 0.8341 |
| 0.0598        | 10.0  | 22500 | 1.6894          | 0.7725   | 0.7761    | 0.7725 | 0.7725 | 0.8548 |
| 0.0699        | 11.0  | 24750 | 1.8025          | 0.765    | 0.7698    | 0.765  | 0.7645 | 0.8460 |
| 0.0292        | 12.0  | 27000 | 1.8754          | 0.76     | 0.7621    | 0.76   | 0.7592 | 0.8451 |


### Framework versions

- Transformers 4.38.2
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2