HammaLoRACAMeLBERT-v1

Hamma-16/HammaLoRACAMeLBERT-v1

HammaLoRACAMeLBERT-v1 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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At a glance

Library: peft
License: apache-2.0
Access: Public
Created: May 8, 2025
Updated: May 8, 2025
SHA: 5fab75d9

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	1.37716	1.3692	0.53022	0.531461	0.473072	0.541513	0.53022
2	1.07614	1.06994	0.624063	0.636517	0.613924	0.630747	0.624063
3	0.968308	0.956658	0.662962	0.675281	0.661112	0.674352	0.662962
4	0.908012	0.900243	0.680569	0.692697	0.67889	0.688134	0.680569
5	0.858731	0.850955	0.699051	0.711236	0.70055	0.711083	0.699051
6	0.83265	0.825597	0.708417	0.716854	0.709791	0.724241	0.708417
7	0.796107	0.792507	0.722777	0.727528	0.723871	0.733254	0.722777
8	0.774045	0.773437	0.729645	0.732584	0.732394	0.747566	0.729645
9	0.758434	0.762295	0.736763	0.737079	0.739592	0.753967	0.736763
10	0.74339	0.7477	0.743194	0.744382	0.74493	0.757555	0.743194
11	0.730814	0.737005	0.74975	0.748876	0.751424	0.760475	0.74975
12	0.731264	0.74201	0.747378	0.748315	0.749058	0.760792	0.747378
13	0.719465	0.729863	0.753184	0.750562	0.754813	0.765065	0.753184
14	0.71225	0.722574	0.756868	0.754494	0.758437	0.767501	0.756868
15	0.712998	0.724446	0.756244	0.755618	0.757984	0.767426	0.756244

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