---
tags:
- generated_from_trainer
metrics:
- precision
- recall
- accuracy
model-index:
- name: train
  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. -->

# train

This model is a fine-tuned version of [aubmindlab/bert-base-arabertv02](https://huggingface.co/aubmindlab/bert-base-arabertv02) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9723
- Macro F1: 0.7972
- Precision: 0.8006
- Recall: 0.8116
- Kappa: 0.7148
- Accuracy: 0.8116

## 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: 128
- seed: 25
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 15

### Training results

| Training Loss | Epoch | Step | Validation Loss | Macro F1 | Precision | Recall | Kappa  | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|:--------:|
| No log        | 1.0   | 101  | 1.2223          | 0.5901   | 0.5634    | 0.6884 | 0.4728 | 0.6884   |
| No log        | 2.0   | 203  | 1.0235          | 0.6570   | 0.6224    | 0.7303 | 0.5496 | 0.7303   |
| No log        | 3.0   | 304  | 0.9087          | 0.7101   | 0.7166    | 0.7623 | 0.6226 | 0.7623   |
| No log        | 4.0   | 406  | 0.8119          | 0.7500   | 0.7346    | 0.7808 | 0.6711 | 0.7808   |
| 0.9826        | 5.0   | 507  | 0.8113          | 0.7821   | 0.7832    | 0.8030 | 0.6989 | 0.8030   |
| 0.9826        | 6.0   | 609  | 0.8290          | 0.7765   | 0.7719    | 0.7943 | 0.6919 | 0.7943   |
| 0.9826        | 7.0   | 710  | 0.8481          | 0.7756   | 0.7696    | 0.7980 | 0.6907 | 0.7980   |
| 0.9826        | 8.0   | 812  | 0.8620          | 0.7820   | 0.7747    | 0.8030 | 0.6974 | 0.8030   |
| 0.9826        | 9.0   | 913  | 0.8717          | 0.7891   | 0.7878    | 0.8042 | 0.7055 | 0.8042   |
| 0.3153        | 10.0  | 1015 | 0.9027          | 0.7872   | 0.7879    | 0.8067 | 0.7070 | 0.8067   |
| 0.3153        | 11.0  | 1116 | 0.9492          | 0.7902   | 0.7915    | 0.8067 | 0.7066 | 0.8067   |
| 0.3153        | 12.0  | 1218 | 0.9462          | 0.7877   | 0.7850    | 0.8042 | 0.7037 | 0.8042   |
| 0.3153        | 13.0  | 1319 | 0.9696          | 0.7881   | 0.7892    | 0.8030 | 0.7022 | 0.8030   |
| 0.3153        | 14.0  | 1421 | 0.9658          | 0.7931   | 0.7975    | 0.8067 | 0.7090 | 0.8067   |
| 0.1362        | 14.93 | 1515 | 0.9723          | 0.7972   | 0.8006    | 0.8116 | 0.7148 | 0.8116   |


### Framework versions

- Transformers 4.30.2
- Pytorch 2.0.1+cu118
- Tokenizers 0.13.3