HammaLoRAMarBERT-v3

Hamma-16/HammaLoRAMarBERT-v3

HammaLoRAMarBERT-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: e655de32

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	1.01756	1.0054	0.70748	0.717978	0.693725	0.706778	0.70748
2	0.762952	0.75223	0.771853	0.78764	0.771604	0.778861	0.771853
3	0.650689	0.648891	0.796329	0.803371	0.797666	0.801681	0.796329
4	0.622925	0.626332	0.801449	0.811798	0.801765	0.80837	0.801449
5	0.576898	0.588152	0.809815	0.812921	0.810793	0.814344	0.809815
6	0.567929	0.60128	0.814623	0.810674	0.816486	0.823517	0.814623
7	0.556496	0.58585	0.818244	0.820225	0.818915	0.822701	0.818244
8	0.54978	0.592384	0.821054	0.820225	0.82197	0.82844	0.821054
9	0.543711	0.587352	0.824301	0.816854	0.826151	0.83428	0.824301
10	0.51674	0.565089	0.830607	0.818539	0.831944	0.83726	0.830607
11	0.520477	0.580509	0.830669	0.819663	0.832265	0.837997	0.830669
12	0.507471	0.563466	0.833729	0.82809	0.834758	0.839029	0.833729
13	0.498436	0.557207	0.834603	0.825281	0.835891	0.840618	0.834603
14	0.496213	0.551106	0.836289	0.828652	0.837213	0.840592	0.836289
15	0.493182	0.549526	0.836414	0.826404	0.837405	0.840693	0.836414

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