metadata

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

bert-base-arabic-finetuned-emotion

This model is a fine-tuned version of 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

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