README.md

metadata

license: apache-2.0
language:
  - ar
  - arz
  - ary
base_model:
  - UBC-NLP/MARBERTv2
pipeline_tag: text-classification
library_name: transformers

🏷️ Model Card: marbertv2-arabic-written-dialect-classifier

Model Name: marbertv2-arabic-written-dialect-classifier
Base Model: UBC-NLP/MARBERTv2
Languages: Arabic (dialectal + MSA)
Author: IbrahimAmin

---

📌 Model Description

This model is fine-tuned from MARBERTv2, a transformer-based language model optimized for Arabic, on a multi-dialect classification task. It distinguishes among five major written Arabic dialect regions:

• MAGHREB (North African dialects)
• LEV (Levantine dialects)
• MSA (Modern Standard Arabic)
• GLF (Gulf dialects)
• EGY (Egyptian Arabic)

It is intended for dialect identification in short Arabic text snippets from various sources including social media, forums, and informal writing.

---

📊 Labels

The model predicts one of the following five classes:

"id2label": {
  "0": "MAGHREB",
  "1": "LEV",
  "2": "MSA",
  "3": "GLF",
  "4": "EGY"
}

---

📚 Training Data

The model was trained about 850,000+ Arabic sentences from 9 different publicly available datasets, covering a wide variety of written Arabic dialects.

Distribution by Dialect:

Dialect	Count
GLF	253,553
LEV	243,025
MAGHREB	140,887
EGY	105,226
MSA	83,231

---

⚙️ Training Details

• Architecture: MARBERTv2 (BERT-based)
• Task: Text Classification (Dialect Identification)
• Objective: Multi-class classification with softmax over 5 dialect classes
• Tokenizer: UBC-NLP/MARBERTv2

---

📂 Datasets Used

Below is a detailed overview of the datasets used in training and/or considered during development:

Dataset	Brief Description	Annotation strategy	Provided Labels	Current SOTA Performance	Notes
MADAR Subtask-1 (MADAR-6)	A Collection of parallel sentences (BTEC) covering the dialects of 5 cities from the Arab World and MSA in the travel domain (10,000 sentences per city)	Manual	5 Arab Cities + MSA	92.5% Accuracy	ADDED
MADAR Subtask-1 (MADAR-26)	A Collection of parallel sentences (BTEC) covering the dialects of 25 cities from the Arab World and MSA in the travel domain (2,000 sentences per city)	Manual	25 Arab Cities + MSA	67.32% F1-Score	ADDED
DART	25K tweets that are annotated via crowdsourcing and it is well-balanced over five main groups of Arabic dialects	Manual	5 Arab Regions	UNK	ADDED
ArSarcasm v1	10,547 tweets from ASTD and SemEval datasets for Sarcasm detection with the dilaect information added in	Manual	4 Arab Regions + MSA	UNK	ADDED
ArSarcasm v2	ArSarcasm-v2 dataset contains 15,548 Tweets and is an extension of the original ArSarcasm dataset (Consists of ArScarcasm v1 along with portions of DAICT corpus and some new tweets)	Manual	4 Arab Regions + MSA	UNK	ADDED
IADD	Five publicly available corpora were identified, analyzed and filtered to build IADD (AOC, DART, PADIC, SHAMI and TSAC)	________	5 Regions and 9 Countries	UNK	ADDED
QADI	540k tweets (30k per country on average) with a total of 8.8M words	Automatic	18 Arab Countries	60.6%	ADDED
AOC	The Arabic Online Commentary dataset is based on reader commentary from the online versions of three Arabic newspapers:AlGhad from JOR, Al-Riyadh from KSA, and Al-Youm Al-Sabe’ from EGY	Manual	3 Arab Regions + MSA	UNK	ADDED
NADI-2020	25,957 Tweets from 100 Arab provinces and 21 Arab countries	Automatic	100 Prov. and 21 Coun.	6.39%, 26.78%	ADDED

---

💡 Usage

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

model_name = "IbrahimAmin/marbertv2-arabic-written-dialect-classifier"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)

text = "الدنيا مش مستاهلة تجري كده، خد وقتك واستمتع بالحاجة البسيطة"
inputs = tokenizer(text, return_tensors="pt")
outputs = model(**inputs)
pred = torch.argmax(outputs.logits, dim=1).item()

print(f"Predicted Dialect: {model.config.id2label[pred]}")

---

✨ Acknowledgements

• MARBERTv2 team at UBC-NLP
• Contributors of the Arabic dialect datasets used in training