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---
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
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