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)

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