HammaLoRACAMeLBERT-v3

Hamma-16/HammaLoRACAMeLBERT-v3

HammaLoRACAMeLBERT-v3 is a machine learning model from Hamma-16. Use it for the machine learning task on the model card, and read the license before you ship it in a product. It is set up for peft. The card lists the license as apache-2.0.

Advanced Arabic Dialect Classification Model with Complete Training Metrics

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Hugging Face

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At a glance

Library: peft
License: apache-2.0
Access: Public
Created: May 8, 2025
Updated: May 8, 2025
SHA: f6358bef

Base models

CAMeL-Lab/bert-base-arabic-camelbert-mix

Model card

From the Hugging Face model README

HammaLoRACAMeLBert

Advanced Arabic Dialect Classification Model with Complete Training Metrics

Training Metrics

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:

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%})")

Library: peft
License: apache-2.0
Languages: ar
Created: May 8, 2025
Updated: May 8, 2025