Arabic stance-detection classifier for StanceEval-2026, an ArabicNLP 2026 (@ EMNLP 2026, Budapest) shared task. Given an Arabic tweet and a target topic, the model predicts whether the tweet's author is in Favor of, Against, or has None (neutral/irrelevant) stance toward the target.

Track 1: Seen Targets

Trained and evaluated on targets that appear in the training data: COVID-19 Vaccine, Digital Transformation, Women Empowerment. Train 3,502 / Dev 619 tweets (Mawqif-v2).

Base model

google-bert/bert-base-multilingual-cased

Labels

ID	Label	Meaning
0	`Favor`	Tweet supports the target
1	`Against`	Tweet opposes the target
2	`None`	Neutral, irrelevant, or unclear stance

Dev set result

69.07 Favg2 (dev set), the shared task's primary metric (macro-F1 over Favor + Against, None excluded).

Usage

python

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

tokenizer = AutoTokenizer.from_pretrained("HassanB4/t1_s9_mbert_text_target")
model = AutoModelForSequenceClassification.from_pretrained("HassanB4/t1_s9_mbert_text_target")
model.eval()

id2label = {0: "Favor", 1: "Against", 2: "None"}

text = "..."
target = "..."
inputs = tokenizer(text, target, return_tensors="pt", truncation=True, max_length=512)

with torch.no_grad():
    logits = model(**inputs).logits

predicted_label = id2label[int(torch.argmax(logits, dim=-1)[0])]
print(predicted_label)

Status

Part of the NAMAA Community StanceEval-2026 submission (Track 1). A system description paper is in preparation; citation details will be added once available.