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| Path | Description |
|---|---|
deberta_contradiction_tuned/ | Tool-aware DeBERTa fine-tuned on mixed train (contradiction oversample ×3) — best run |
deberta_mixed/ | Earlier/alternate DeBERTa mixed checkpoint (no contradiction oversampling) |
predictions/ | mixed_test span predictions (DeBERTa, LookBack, Lettuce) |
lookback/lookback_mixed_classifier.joblib | Sklearn head for LookBackLens (TinyLlama features) |
lookback/lookback_mixed_train_features.npz | Cached train attention features (~1.1 GB) |
lookback/lookback_mixed_val_features.npz | Cached validation attention features (~164 MB) |
1from transformers import AutoModelForTokenClassification, AutoTokenizer
2
3model_dir = "drond0174/hallucination_detection"
4tokenizer = AutoTokenizer.from_pretrained(f"{model_dir}/deberta_contradiction_tuned")
5model = AutoModelForTokenClassification.from_pretrained(
6 f"{model_dir}/deberta_contradiction_tuned"
7)deberta_contradiction_tuned/run_meta.json for threshold, best epoch, and validation F1.lookback/*_features.npz to skip re-running TinyLlama feature extraction. Point train_cache_path / val_cache_path in lookback_baseline.py to the downloaded files.