--- language: ar license: apache-2.0 library_name: peft base_model: CAMeL-Lab/bert-base-arabic-camelbert-mix tags: - arabic - dialect-classification - lora --- # HammaLoRACAMeLBert Advanced Arabic Dialect Classification Model with Complete Training Metrics ![Training Metrics](training_metrics.png) ## Full Training History: | epoch | train_loss | eval_loss | train_accuracy | eval_accuracy | f1 | precision | recall | |--------:|-------------:|------------:|-----------------:|----------------:|---------:|------------:|---------:| | 1 | 1.04802 | 1.05081 | 0.637238 | 0.63764 | 0.6275 | 0.63901 | 0.637238 | | 2 | 0.77121 | 0.781513 | 0.736139 | 0.735955 | 0.613924 | 0.630747 | 0.624063 | | 3 | 0.620521 | 0.659047 | 0.788899 | 0.779775 | 0.791235 | 0.799805 | 0.788899 | | 4 | 0.573822 | 0.644417 | 0.800762 | 0.776404 | 0.67889 | 0.688134 | 0.680569 | | 5 | 0.49636 | 0.587736 | 0.824675 | 0.791573 | 0.826167 | 0.833785 | 0.824675 | | 6 | 0.476073 | 0.589205 | 0.833292 | 0.792697 | 0.709791 | 0.724241 | 0.708417 | | 7 | 0.429335 | 0.547112 | 0.849525 | 0.808427 | 0.850117 | 0.853328 | 0.849525 | | 8 | 0.412983 | 0.544361 | 0.855519 | 0.805056 | 0.732394 | 0.747566 | 0.729645 | | 9 | 0.388498 | 0.551354 | 0.863511 | 0.808989 | 0.863979 | 0.869072 | 0.863511 | | 10 | 0.359349 | 0.52705 | 0.877185 | 0.81573 | 0.74493 | 0.757555 | 0.743194 | | 11 | 0.34684 | 0.538555 | 0.879683 | 0.810674 | 0.880776 | 0.884118 | 0.879683 | | 12 | 0.337791 | 0.538365 | 0.883304 | 0.807865 | 0.749058 | 0.760792 | 0.747378 | | 13 | 0.328992 | 0.53789 | 0.886301 | 0.81236 | 0.887004 | 0.889841 | 0.886301 | | 14 | 0.322374 | 0.536085 | 0.889423 | 0.81573 | 0.758437 | 0.767501 | 0.756868 | | 15 | 0.320124 | 0.53761 | 0.890485 | 0.814607 | 0.891161 | 0.89363 | 0.890485 | ## 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/HammaLoRACAMeLBert", 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%})")