metadata
license: mit
base_model: Davlan/xlm-roberta-base-finetuned-arabic
tags:
- generated_from_keras_callback
model-index:
- name: betteib/xlm-tn-20epochs
results: []
betteib/xlm-tn-20epochs
This model is a fine-tuned version of Davlan/xlm-roberta-base-finetuned-arabic on an unknown dataset. It achieves the following results on the evaluation set:
- Train Loss: 6.7068
- Train Accuracy: 0.0290
- Validation Loss: 6.6661
- Validation Accuracy: 0.0290
- Epoch: 14
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:
- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'WarmUp', 'config': {'initial_learning_rate': 0.0001, 'decay_schedule_fn': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 0.0001, 'decay_steps': 18848, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'passive_serialization': True}, 'warmup_steps': 992, 'power': 1.0, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.001}
- training_precision: float32
Training results
| Train Loss | Train Accuracy | Validation Loss | Validation Accuracy | Epoch |
|---|---|---|---|---|
| 9.7281 | 0.0031 | 9.4281 | 0.0045 | 0 |
| 9.2184 | 0.0051 | 8.9814 | 0.0060 | 1 |
| 8.7336 | 0.0068 | 8.4005 | 0.0084 | 2 |
| 8.1287 | 0.0109 | 7.7969 | 0.0133 | 3 |
| 7.6665 | 0.0159 | 7.4295 | 0.0222 | 4 |
| 7.3938 | 0.0233 | 7.1783 | 0.0289 | 5 |
| 7.2079 | 0.0287 | 7.0257 | 0.0286 | 6 |
| 7.0785 | 0.0292 | 6.9028 | 0.0291 | 7 |
| 6.9777 | 0.0294 | 6.8739 | 0.0287 | 8 |
| 6.9034 | 0.0292 | 6.8083 | 0.0281 | 9 |
| 6.8549 | 0.0292 | 6.8099 | 0.0280 | 10 |
| 6.7978 | 0.0292 | 6.7450 | 0.0286 | 11 |
| 6.7555 | 0.0291 | 6.7420 | 0.0281 | 12 |
| 6.7267 | 0.0293 | 6.6804 | 0.0291 | 13 |
| 6.7068 | 0.0290 | 6.6661 | 0.0290 | 14 |
Framework versions
- Transformers 4.31.0
- TensorFlow 2.12.0
- Datasets 2.19.1
- Tokenizers 0.13.3