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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
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
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: 6.8549
- Train Accuracy: 0.0292
- Validation Loss: 6.8099
- Validation Accuracy: 0.0280
- Epoch: 10
## 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 |
### Framework versions
- Transformers 4.31.0
- TensorFlow 2.12.0
- Datasets 2.19.1
- Tokenizers 0.13.3
|