StanceEval-2026 — Track 1 (Seen Targets): QARiB
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
ahmedabdelali/bert-base-qarib
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
81.84 Favg2 (dev set), the shared task's primary metric (macro-F1 over Favor + Against, None excluded).
Usage
from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch
tokenizer = AutoTokenizer.from_pretrained("HassanB4/t1_s5_qarib_text_target")
model = AutoModelForSequenceClassification.from_pretrained("HassanB4/t1_s5_qarib_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.