zoro6u/marbert-arabic-dialect-id

MARBERTv2 — Arabic Dialect Identification (18 countries)

Fine-tuned MARBERTv2 for country-level Arabic dialect classification on the QADI tweet dataset.

Test macro-F1: 0.614 (baseline TF-IDF + LinearSVC: 0.582; original QADI paper: 0.606).

Labels: OM, SD, SA, KW, QA, LB, JO, SY, IQ, MA, EG, PL, YE, BH, DZ, AE, TN, LY.

Usage

from transformers import pipeline
clf = pipeline("text-classification", model="zoro6u/marbert-arabic-dialect-id")
clf("شنو الاخبار يا زول كيفك")
# [{'label': 'SD', 'score': 0.99}]

Preprocessing used in training (apply the same before inference): remove @mentions and URLs, strip #, remove diacritics, collapse whitespace.

Training

• Data: QADI, 440k train tweets, 18 balanced country labels (auto-labeled from author profiles, ~91.5% label accuracy per the paper)
• 2 epochs, lr 2e-5, batch 128, max_len 64, fp16, warmup 400 steps, weight decay 0.01
• ~1h40 on 2×T4 (Kaggle)

Per-dialect F1 (test)

OM	SD	SA	KW	QA	LB	JO	SY	IQ	MA	EG	PL	YE	BH	DZ	AE	TN	LY
.52	.72	.54	.65	.55	.73	.49	.51	.68	.71	.85	.63	.41	.46	.66	.51	.66	.78

Strongest: EG, LY, LB, SD, MA. Weakest: the Gulf cluster and YE, where dialects are close and tweets are short.

Limitations

• Labels are author-level, not text-level: noisy by construction.
• Trained on tweets; expect lower accuracy on formal text or other domains.
• Per-dialect scores have wide error bars (test set 181–1130 samples per class).

Code and baseline: github.com/zoro6u/arabic-dialect-id