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