README.md

---
tags:
- generated_from_trainer
datasets:
- emotone_ar
metrics:
- accuracy
- f1
model-index:
- name: bert-base-arabic-finetuned-emotion
  results:
  - task:
      name: Text Classification
      type: text-classification
    dataset:
      name: emotone_ar
      type: emotone_ar
      config: default
      split: train[:90%]
      args: default
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.7415506958250497
    - name: F1
      type: f1
      value: 0.7406006078114171
---

<!-- 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. -->

# bert-base-arabic-finetuned-emotion

This model is a fine-tuned version of [asafaya/bert-base-arabic](https://huggingface.co/asafaya/bert-base-arabic) on the emotone_ar dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8965
- Accuracy: 0.7416
- F1: 0.7406

### Cite this model
```
-Noaman, H. (2023). Improved Emotion Detection Framework for Arabic Text using Transformer Models.
Advanced Engineering Technology and Application, 12(2), 1-11.

@article{noaman2023improved,
  title={Improved Emotion Detection Framework for Arabic Text using Transformer Models},
  author={Noaman, Hatem},
  journal={Advanced Engineering Technology and Application},
  volume={12},
  number={2},
  pages={1--11},
  year={2023},
  publisher={Fayoum University}
}
```

## Load Pretrained Model
You can use this model by

```python 
from transformers import AutoTokenizer, AutoModel

tokenizer = AutoTokenizer.from_pretrained("hatemnoaman/bert-base-arabic-finetuned-emotion")
model = AutoModel.from_pretrained("hatemnoaman/bert-base-arabic-finetuned-emotion")
```


## 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: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 6

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 1.3476        | 1.0   | 142  | 0.8911          | 0.7008   | 0.6812 |
| 0.8204        | 2.0   | 284  | 0.8175          | 0.7276   | 0.7212 |
| 0.6227        | 3.0   | 426  | 0.8392          | 0.7376   | 0.7302 |
| 0.4816        | 4.0   | 568  | 0.8531          | 0.7435   | 0.7404 |
| 0.378         | 5.0   | 710  | 0.8817          | 0.7396   | 0.7388 |
| 0.3134        | 6.0   | 852  | 0.8965          | 0.7416   | 0.7406 |


### Framework versions

- Transformers 4.25.1
- Pytorch 1.13.0+cu116
- Datasets 2.8.0
- Tokenizers 0.13.2