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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 | 1.23777 | 1.22883 | 0.696741 | 0.706742 | 0.679064 | 0.704547 | 0.696741 |
| 2 | 0.740169 | 0.733708 | 0.790397 | 0.800562 | 0.791608 | 0.797789 | 0.790397 |
| 3 | 0.601572 | 0.617834 | 0.818182 | 0.821348 | 0.819564 | 0.824729 | 0.818182 |
| 4 | 0.562756 | 0.585901 | 0.824363 | 0.816292 | 0.825464 | 0.835567 | 0.824363 |
| 5 | 0.497183 | 0.534541 | 0.839411 | 0.832022 | 0.839956 | 0.842141 | 0.839411 |
| 6 | 0.467484 | 0.529349 | 0.848964 | 0.830899 | 0.850348 | 0.855113 | 0.848964 |
| 7 | 0.447877 | 0.52692 | 0.851773 | 0.832022 | 0.852826 | 0.857268 | 0.851773 |
| 8 | 0.44038 | 0.525875 | 0.854021 | 0.830337 | 0.855092 | 0.860913 | 0.854021 |
| 9 | 0.416875 | 0.513681 | 0.863886 | 0.835955 | 0.865207 | 0.870201 | 0.863886 |
| 10 | 0.397198 | 0.498091 | 0.868506 | 0.839888 | 0.869502 | 0.872867 | 0.868506 |
| 11 | 0.396181 | 0.509205 | 0.86757 | 0.835955 | 0.869238 | 0.875968 | 0.86757 |
| 12 | 0.38368 | 0.494237 | 0.873064 | 0.838764 | 0.87361 | 0.875448 | 0.873064 |
| 13 | 0.377543 | 0.496908 | 0.874001 | 0.83764 | 0.874749 | 0.877947 | 0.874001 |
| 14 | 0.371016 | 0.491708 | 0.877435 | 0.841573 | 0.878057 | 0.880101 | 0.877435 |
| 15 | 0.370049 | 0.493832 | 0.877872 | 0.840449 | 0.878651 | 0.881198 | 0.877872 |
## 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%})")
|