HammaLoRAMarBERT-v1

Hamma-16/HammaLoRAMarBERT-v1

HammaLoRAMarBERT-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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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: bd4fc3cb

Base models

UBC-NLP/MARBERT

Model card

From the Hugging Face model README

HammaLoRAMarBert

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	1.23777	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:

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%})")

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