KRLabsOrg
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lettucedect-mmbert-base-hu-v1

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 ---
 library_name: transformers
+license: mit
+language:
+- hu
+base_model:
+- jhu-clsp/mmBERT-small
+pipeline_tag: token-classification
+tags:
+- token classification
+- hallucination detection
+- transformers
+- question answer
+datasets:
+- KRLabsOrg/ragtruth-hu-translated
 ---
+# LettuceDetect: Hungarian Hallucination Detection Model
+<p align="center">
+  <img src="https://github.com/KRLabsOrg/LettuceDetect/blob/main/assets/lettuce_detective.png?raw=true" alt="LettuceDetect Logo" width="400"/>
+</p>
+**Model Name:** lettucedect-mmbert-base-hu-v1
+**Organization:** KRLabsOrg
+**Github:** https://github.com/KRLabsOrg/LettuceDetect
+## Overview
+LettuceDetect is a transformer-based model for hallucination detection on context and answer pairs, designed for Retrieval-Augmented Generation (RAG) applications. This model is built on **ModernBERT**, which has been specifically chosen and trained becasue of its extended context support (up to **8192 tokens**). This long-context capability is critical for tasks where detailed and extensive documents need to be processed to accurately determine if an answer is supported by the provided context.
+**This is our Large model based on ModernBERT-large**
+## Model Details
+- **Architecture:** mmBERT-base with extended context support (up to 8192 tokens)
+- **Task:** Token Classification / Hallucination Detection
+- **Training Dataset:** RagTruth-HU
+- **Language:** Hungarian
+## How It Works
+The model is trained to identify tokens in the answer text that are not supported by the given context. During inference, the model returns token-level predictions which are then aggregated into spans. This allows users to see exactly which parts of the answer are considered hallucinated.
+## Usage
+### Installation
+Install the 'lettucedetect' repository
+```bash
+pip install lettucedetect
+```
+### Using the model
+```python
+from lettucedetect.models.inference import HallucinationDetector
+detector = HallucinationDetector(
+    method="transformer",
+    model_path="KRLabsOrg/lettucedect-mmbert-base-hu-v1",
+    lang="hu",
+    trust_remote_code=True
+)
+contexts = [
+    "Franciaország fővárosa Párizs. Franciaország népessége 67 millió fő. Franciaország területe 551 695 km²."
+]
+question = "Mennyi Franciaország népessége?"
+answer = "Franciaország népessége 125 millió fő."
+predictions = detector.predict(context=contexts, question=question, answer=answer, output_format="spans")
+print("Predictions:", predictions)
+# Predictions: [{'start': 0, 'end': 38, 'confidence': 0.9475189447402954, 'text': 'Franciaország népessége 125 millió fő.'}]
+```
+## Performance
+**Results on Translated RAGTruth-HU (Class 1: Hallucination)**
+We evaluate our Hungarian models on the translated [RAGTruth](https://aclanthology.org/2024.acl-long.585/) dataset. As a prompt baseline we include **meta-llama/Llama-4-Maverick-17B-128E-Instruct**.
+| Language | Model                                   | Precision (%) | Recall (%) | F1 (%) | Maverick F1 (%) | Δ F1 (%) |
+|----------|-----------------------------------------|---------------|------------|--------|-----------------|----------|
+| Hungarian | meta-llama/Llama-4-Maverick-17B-128E-Instruct | 38.70        | **96.82** | 55.30 | 55.30          | +0.00    |
+| Hungarian | lettucedect-mmBERT-small (ours)                    | 70.20         | 72.51      | 71.33 | 55.30          | **+16.03** |
+| Hungarian | lettucedect-mmBERT-base (ours)                     | **76.62**     | 69.21      | **72.73** | 55.30          | **+17.43** |
+*Note:* Percentages are reported for the hallucination class (Class 1). Δ F1 is measured in percentage points vs. the Maverick baseline.
+## Citing
+If you use the model or the tool, please cite the following paper:
+```bibtex
+@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},
+}
+```