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language: ar
license: apache-2.0
library_name: peft
base_model: UBC-NLP/MARBERT
tags:
- arabic
- dialect-classification
- lora
---
# HammaLoRAMarBert
Advanced Arabic Dialect Classification Model with Complete Training Metrics
## Full Training History
| epoch | train_loss | eval_loss | train_accuracy | eval_accuracy | f1 | precision | recall |
|--------:|-------------:|------------:|-----------------:|----------------:|---------:|------------:|---------:|
| 1 | 1.01756 | 1.0054 | 0.70748 | 0.717978 | 0.693725 | 0.706778 | 0.70748 |
| 2 | 0.762952 | 0.75223 | 0.771853 | 0.78764 | 0.771604 | 0.778861 | 0.771853 |
| 3 | 0.650689 | 0.648891 | 0.796329 | 0.803371 | 0.797666 | 0.801681 | 0.796329 |
| 4 | 0.622925 | 0.626332 | 0.801449 | 0.811798 | 0.801765 | 0.80837 | 0.801449 |
| 5 | 0.576898 | 0.588152 | 0.809815 | 0.812921 | 0.810793 | 0.814344 | 0.809815 |
| 6 | 0.567929 | 0.60128 | 0.814623 | 0.810674 | 0.816486 | 0.823517 | 0.814623 |
| 7 | 0.556496 | 0.58585 | 0.818244 | 0.820225 | 0.818915 | 0.822701 | 0.818244 |
| 8 | 0.54978 | 0.592384 | 0.821054 | 0.820225 | 0.82197 | 0.82844 | 0.821054 |
| 9 | 0.543711 | 0.587352 | 0.824301 | 0.816854 | 0.826151 | 0.83428 | 0.824301 |
| 10 | 0.51674 | 0.565089 | 0.830607 | 0.818539 | 0.831944 | 0.83726 | 0.830607 |
| 11 | 0.520477 | 0.580509 | 0.830669 | 0.819663 | 0.832265 | 0.837997 | 0.830669 |
| 12 | 0.507471 | 0.563466 | 0.833729 | 0.82809 | 0.834758 | 0.839029 | 0.833729 |
| 13 | 0.498436 | 0.557207 | 0.834603 | 0.825281 | 0.835891 | 0.840618 | 0.834603 |
| 14 | 0.496213 | 0.551106 | 0.836289 | 0.828652 | 0.837213 | 0.840592 | 0.836289 |
| 15 | 0.493182 | 0.549526 | 0.836414 | 0.826404 | 0.837405 | 0.840693 | 0.836414 |
## Label Mapping:
{0: 'Egypt', 1: 'Iraq', 2: 'Lebanon', 3: 'Morocco', 4: 'Saudi_Arabia', 5: 'Sudan', 6: 'Tunisia'}
## USAGE Example:
```python
from transformers import pipeline
classifier = pipeline(
"text-classification",
model="Hamma-16/HammaLoRAMarBert",
device="cuda" if torch.cuda.is_available() else "cpu"
)
sample_text = "شلونك اليوم؟"
result = classifier(sample_text)
print(f"Text: {sample_text}")
print(f"Predicted: {result[0]['label']} (confidence: {result[0]['score']:.1%})")
|