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Upload fine-tuned DeBERTa-v3-base hallucination model
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metadata
license: agpl-3.0
language: en
tags:
  - hallucination-detection
  - nli
  - deberta-v3
  - director-ai
datasets:
  - pminervini/HaluEval
  - pietrolesci/nli_fever
  - tals/vitaminc
  - anli
base_model: MoritzLaurer/DeBERTa-v3-base-mnli-fever-anli-ling-wanli
pipeline_tag: text-classification

DeBERTa-v3-base -- Hallucination Detection

Fine-tuned for hallucination detection as part of Director-AI.

Smaller variant (184M params) of the large model. Lower accuracy but faster inference — suitable for CPU deployments.

Training

  • Base: MoritzLaurer/DeBERTa-v3-base-mnli-fever-anli-ling-wanli
  • Data: ~100K examples from HaluEval, FEVER, VitaminC, ANLI R3
  • Epochs: 3, lr 2e-5, batch 32 (effective), class-weighted CE loss
  • Labels: 0 = entailment, 1 = neutral, 2 = contradiction

Usage

from director_ai.core import NLIScorer

scorer = NLIScorer(model_name="anulum/deberta-v3-base-hallucination")
score = scorer.score("The capital of France is Paris.", "Paris is in Germany.")

License

AGPL-3.0 | Commercial licensing: anulum.li