HammaLoRAMarBERT-v2

Hamma-16/HammaLoRAMarBERT-v2

HammaLoRAMarBERT-v2 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 7, 2025
Updated: May 7, 2025
SHA: 65ce6237

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	1.51721	1.50726	0.670392	0.685955	0.647908	0.695828	0.670392
2	0.827407	0.804686	0.779283	0.790449	0.779526	0.787574	0.779283
3	0.624589	0.617633	0.815747	0.823596	0.815815	0.818754	0.815747
4	0.577044	0.593563	0.822927	0.821348	0.824907	0.835161	0.822927
5	0.504094	0.535676	0.839036	0.834831	0.839583	0.842469	0.839036
6	0.46799	0.520281	0.849213	0.835955	0.850536	0.855303	0.849213
7	0.445317	0.510596	0.854708	0.840449	0.855552	0.858046	0.854708
8	0.428012	0.501261	0.858142	0.842135	0.859003	0.862191	0.858142
9	0.412287	0.491676	0.864635	0.848315	0.865268	0.868648	0.864635
10	0.400929	0.497091	0.868194	0.847753	0.8693	0.872337	0.868194
11	0.395328	0.506237	0.868319	0.840449	0.870433	0.87781	0.868319
12	0.378038	0.483877	0.874813	0.847191	0.875232	0.877259	0.874813
13	0.3727	0.488525	0.874313	0.841573	0.875207	0.878724	0.874313
14	0.366197	0.482607	0.878059	0.85	0.878635	0.880364	0.878059
15	0.365844	0.485294	0.878247	0.851124	0.879	0.88123	0.878247

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 7, 2025
Updated: May 7, 2025