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KRLabsOrg/tinylettuce-ettin-68m-en-bioasq
tinylettuce-ettin-68m-en-bioasq is a token classification model from KRLabsOrg. Use it when you need labels on individual words, such as names. It is set up for transformers. The card lists the license as mit.
<p align="center" <img src="https://github.com/KRLabsOrg/LettuceDetect/blob/dev/assets/tinytinylettuce.png?raw=true" alt="TinyLettuce" width="400"/ </p
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From the Hugging Face model README
Model Name: tinylettuce-ettin-68m-en-v1
Organization: KRLabsOrg
Github: https://github.com/KRLabsOrg/LettuceDetect
Ettin encoders: https://arxiv.org/pdf/2507.11412
The 68M Ettin variant provides the highest accuracy among TinyLettuce models while remaining CPU‑friendly. It detects unsupported spans in answers given context and is optimized for low‑cost deployment and fine‑tuning.
Trained on our synthetic dataset (mixed with RAGTruth), this 68M variant reaches 92.64% F1 on the held‑out synthetic test set, proving the effectiveness of our domain‑specific hallucination data generation pipeline.
Synthetic (domain‑specific):
| Model | Parameters | Precision (%) | Recall (%) | F1 (%) | Hardware |
|---|---|---|---|---|---|
| TinyLettuce-17M | 17M | 84.56 | 98.21 | 90.87 | CPU |
| TinyLettuce-32M | 32M | 80.36 | 99.10 | 88.76 | CPU |
| TinyLettuce-68M | 68M | 89.54 | 95.96 | 92.64 | CPU |
| GPT-5-mini | ~200B | 71.95 | 100.00 | 83.69 | API |
| GPT-OSS-120B | 120B | 72.21 | 98.64 | 83.38 | GPU |
| Qwen3-235B | 235B | 66.74 | 99.32 | 79.84 | GPU |
First install lettucedetect:
pip install lettucedetect
Then use it:
from lettucedetect.models.inference import HallucinationDetector
detector = HallucinationDetector(
method="transformer",
model_path="KRLabsOrg/tinylettuce-ettin-68m-en-v1",
)
spans = detector.predict(
context=[
"Ibuprofen is an NSAID that reduces inflammation and pain. The typical adult dose is 400-600mg every 6-8 hours, not exceeding 2400mg daily."
],
question="What is the maximum daily dose of ibuprofen?",
answer="The maximum daily dose of ibuprofen for adults is 3200mg.",
output_format="spans",
)
print(spans)
# Output: [{"start": 51, "end": 57, "text": "3200mg"}]
If you use the model or the tool, please cite the following paper:
@misc{Kovacs:2025,
title={LettuceDetect: A Hallucination Detection Framework for RAG Applications},
author={Ádám Kovács and Gábor Recski},
year={2025},
eprint={2502.17125},
archivePrefix={arXiv},
primaryClass={cs.CL},
url={https://arxiv.org/abs/2502.17125},
}