halluscoring-marbert-nli
MARBERTv2 (UBC-NLP/MARBERTv2) fine-tuned on HalluScoring 2026 Task 1.1 using the NLI framing ([CLS] gold_answer [SEP] model_answer [SEP]). Internally this is run S03 — confirms the NLI-framing gain first found on CAMeLBERT (halluscoring-camelbert-nli) is architecture-independent; scores essentially tie (clean-dev AUC-ROC 0.9266 vs. 0.9272, −0.06pp).
Not submitted to the competition — kept as an internal experiment and as a component of the S23/S24v/S25v ensembles (dropped from the final best ensemble, S25v2, whose predictions turned out to be redundant with the ARBERT model since both are UBC-NLP architectures). See SYSTEM_WRITEUP.md for the officially-submitted models.
How to Use
import torch
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_id = "HassanB4/halluscoring-marbert-nli"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForSequenceClassification.from_pretrained(model_id)
model.eval()
gold_answer = "..."
model_answer = "..."
inputs = tokenizer(gold_answer, model_answer, truncation=True, max_length=512, return_tensors="pt")
with torch.no_grad():
logits = model(**inputs).logits
prob_hallucinated = torch.softmax(logits, dim=-1)[0, 1].item()
print(f"hallucinated={int(prob_hallucinated > 0.5)}, score={prob_hallucinated:.4f}")
Training
| Parameter | Value |
|---|---|
| Base model | UBC-NLP/MARBERTv2 |
| Input format | nli (gold_answer + model_answer) |
| Max sequence length | 512 |
| Batch size | 16 |
| Epochs | 5 |
| Learning rate | 2e-5 |
| Warmup ratio | 0.1 |
| Weight decay | 0.01 |
| Loss | cross-entropy |
| Seed | 42 |
Evaluation
| Split | AUC-ROC | F1-Macro |
|---|---|---|
| Dev (official, n=1300) | 0.9562 | 0.8989 |
| Dev (clean, unseen-question subset, n=800) | 0.9266 | — |
Limitations
Not evaluated on the hidden test set — internal dev-only experiment. Predictions were found to be redundant with halluscoring-arbert-nli (both UBC-NLP architectures), which is why it was excluded from the final S25v2 ensemble in favor of a more architecturally diverse model.
Citation
@inproceedings{namaa2026halluscoring,
title={{NAMAA at HalluScoring 2026: NLI-Framed BERT Classifiers and Ensembling for Model-Agnostic Arabic Hallucination Detection}},
author={[AUTHOR NAMES TBD]},
year={2026},
booktitle={Proceedings of ArabicNLP 2026},
note={HalluScoring 2026 Shared Task, Track 1}
}