---
license: mit
datasets:
- ragtruth
language:
- en
base_model: answerdotai/ModernBERT-large
pipeline_tag: token-classification
tags:
- hallucination-detection
- rag
- token-classification
- modernbert
- lettucedetect
---

# modernbert-large-ragtruth-token-level-binary

Fine-tuned `answerdotai/ModernBERT-large`, binary **token**-classification model for
**span-level** RAG hallucination detection — the same recipe as
[`hugoomezz/modernbert-ragtruth-token-level-binary`](https://huggingface.co/hugoomezz/modernbert-ragtruth-token-level-binary)
(Track B / arm b), scaled to the `-large` backbone, seed 42.

**This is the REPORTING model, not the deployed one.** Per
[ADR-021](https://github.com/hugoomez/rag-hallucination-detector/blob/main/docs/decisions.md#adr-021-3-seed-matched-scaling-comparison----modernbert-large-adopted-for-reporting),
the project deliberately keeps ModernBERT-**base** (arm b) as the model running in the
live demo — a latency/simplicity tradeoff — while adopting this large-backbone
checkpoint as the best-documented model for reporting the base→large scaling
comparison. The two roles are recorded separately on purpose: the headline number and
the running system are not required to be the same artifact, as long as which is which
is stated.

## Intended use

Research artifact for the base→large scaling comparison in
[ADR-021](https://github.com/hugoomez/rag-hallucination-detector/blob/main/docs/decisions.md)
and the paper's Appendix A. Not the model behind the live demo (see
`hugoomezz/modernbert-ragtruth-token-level-binary` for that). Not validated outside
RAGTruth's three task types (QA, Summary, Data2txt), and not intended for production
moderation decisions.

## Training data

[RAGTruth](https://github.com/ParticleMedia/RAGTruth) (Niu et al., 2024, ACL,
[arXiv:2401.00396](https://arxiv.org/abs/2401.00396)) — MIT-licensed. 13,578 train /
1,512 val / 2,700 test rows. Identical recipe to arm b (lr=1e-5, effective batch 8,
checkpoint selection on token-level F1), differing only in backbone (`ModernBERT-large`
vs `-base`) and seed count (this repo is seed 42 of a matched 3-seed sweep — see ADR-021
for the full 3-seed mean/range; this card reports seed 42's own numbers, not the mean).

## Metrics (RAGTruth test set, n=2700, seed 42, checkpoint selected at epoch 2/step 3396 on token-F1)

| Metric | Value |
|---|---|
| Response-level Precision | 0.8530 |
| Response-level Recall | 0.7444 |
| Response-level F1 | 0.7950 |
| Response-level Accuracy | 0.8659 |
| Span-level (char-overlap) Precision | 0.6954 |
| Span-level (char-overlap) Recall | 0.4927 |
| Span-level (char-overlap) F1 | **0.5767** |

Per task_type (response-level derived):

| Task | F1 | Recall |
|---|---|---|
| Data2txt | 0.8803 | 0.8636 |
| QA | 0.7290 | 0.7063 |
| Summary | 0.5563 | 0.4363 |

vs. arm b (ModernBERT-base, seed 42): response F1 0.7631, span F1 0.5321 — this
checkpoint improves response F1 by +0.0319 and span F1 by +0.0446 at this seed,
recall-led rather than precision-led (the same pattern ADR-012 found moving from
DeBERTa to ModernBERT-base). See ADR-021 for the full 3-seed picture (mean +0.0311 /
+0.0408) and its caveats — the epoch cap was not uniform across all seeds, and one
large-backbone seed (123) has unrecorded training configuration.

## Limitations

Same structural limitations as arm b (Summary is the weakest task; "Subtle"
hallucinations are the hardest case; checkpoint selection uses token-F1, not the
clean-span metric the paper's hypothesis is actually about — see the paper's §8
Limitations). Specific to this checkpoint:

- **Single-seed card.** This card characterizes seed 42 only. ADR-021's 3-seed mean is
  the number the paper actually reports as the headline scaling result; treat this
  card's numbers as one seed's contribution to that mean, not as the finding itself.
- **Not the deployed model.** If you want the model behind this project's live demo,
  use `hugoomezz/modernbert-ragtruth-token-level-binary` (arm b, ModernBERT-base)
  instead.
- **Resource-constrained scaling replication is methodologically separate from this
  project's main argument.** Per the paper's Appendix A: "tests nothing about
  `implicit_true`, contributes no evidence for or against §4, §5, or §6, and would be
  removed without affecting any claim the paper makes."

## How to use

```python
import torch
from transformers import AutoModelForTokenClassification, AutoTokenizer

# The tokenizer is loaded from the base ModernBERT-large repo, not the fine-tuned one,
# mirroring the same substitution used by hugoomezz/modernbert-ragtruth-token-level-binary
# (arm b) -- training data was built with this exact base tokenizer, so it's exact, not
# an approximation.
TOKENIZER_ID = "answerdotai/ModernBERT-large"
MODEL_ID = "hugoomezz/modernbert-large-ragtruth-token-level-binary"
SUPPORTED, HALLUCINATED = 0, 1

tokenizer = AutoTokenizer.from_pretrained(TOKENIZER_ID)
model = AutoModelForTokenClassification.from_pretrained(MODEL_ID, attn_implementation="sdpa").eval()

context = "The Eiffel Tower was completed in 1889 for the World's Fair in Paris."
response = "The Eiffel Tower was completed in 1889 and is located in Berlin, Germany."

encoding = tokenizer(
    context, response, max_length=4096, truncation="only_first",
    return_offsets_mapping=True, return_token_type_ids=False, return_tensors="pt",
)
sequence_ids = encoding.sequence_ids(0)
encoding.pop("offset_mapping")

with torch.no_grad():
    logits = model(**encoding).logits[0]
probs_hallucinated = torch.softmax(logits, dim=-1)[:, HALLUCINATED].tolist()

response_probs = [p for p, sid in zip(probs_hallucinated, sequence_ids) if sid == 1]
score = max(response_probs) if response_probs else 0.0
print(f"hallucination score: {score:.4f}  ({'FLAGGED' if score >= 0.5 else 'clean'})")
```

## Citation

```bibtex
@inproceedings{niu2024ragtruth,
  title     = {RAGTruth: A Hallucination Corpus for Developing and Evaluating RAG Systems},
  author    = {Niu, Cheng and Wu, Yuanhao and Zhu, Juno and Xu, Siliang and Shum, Kashun and Zhong, Randy and Song, Juntong and Zhang, Tong},
  booktitle = {Proceedings of ACL 2024},
  year      = {2024},
  eprint    = {2401.00396}
}
@article{kovacs2025lettucedetect,
  title   = {LettuceDetect: A Hallucination Detection Framework for RAG Applications},
  author  = {Kov{\'a}cs, {\'A}d{\'a}m and Bakos, Zsolt},
  journal = {arXiv preprint arXiv:2502.17125},
  year    = {2025}
}
```