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---
license: mit
base_model: Davlan/xlm-roberta-base-finetuned-arabic
tags:
- generated_from_keras_callback
model-index:
- name: betteib/xlm-tn-20epochs-lr
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# betteib/xlm-tn-20epochs-lr
This model is a fine-tuned version of [Davlan/xlm-roberta-base-finetuned-arabic](https://huggingface.co/Davlan/xlm-roberta-base-finetuned-arabic) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 7.0724
- Train Accuracy: 0.0291
- Validation Loss: 6.9350
- Validation Accuracy: 0.0286
- Epoch: 5
## 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': 4464, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, '__passive_serialization__': True}, 'warmup_steps': 496, 'power': 1.0, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.03}
- training_precision: float32
### Training results
| Train Loss | Train Accuracy | Validation Loss | Validation Accuracy | Epoch |
|:----------:|:--------------:|:---------------:|:-------------------:|:-----:|
| 9.6176 | 0.0035 | 9.2319 | 0.0048 | 0 |
| 8.9356 | 0.0059 | 8.5303 | 0.0071 | 1 |
| 8.1494 | 0.0100 | 7.7161 | 0.0137 | 2 |
| 7.5554 | 0.0180 | 7.2709 | 0.0281 | 3 |
| 7.2561 | 0.0273 | 7.0588 | 0.0289 | 4 |
| 7.0724 | 0.0291 | 6.9350 | 0.0286 | 5 |
### Framework versions
- Transformers 4.31.0
- TensorFlow 2.12.0
- Datasets 2.19.1
- Tokenizers 0.13.3
|