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lettucedect-610m-eurobert-fr-v1

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 ---
+license: mit
+language:
+- fr
+base_model:
+- EuroBERT/EuroBERT-610m
+pipeline_tag: token-classification
+tags:
+- token classification
+- hallucination detection
+- transformers
+- question answer
+datasets:
+- KRLabsOrg/ragtruth-fr-translated
 ---
+# LettuceDetect: French Hallucination Detection Model
+<p align="center">
+  <img src="https://github.com/KRLabsOrg/LettuceDetect/blob/feature/cn_llm_eval/assets/lettuce_detective_multi.png?raw=true" alt="LettuceDetect Logo" width="400"/>
+</p>
+**Model Name:** KRLabsOrg/lettucedect-610m-eurobert-fr-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 multilingual Retrieval-Augmented Generation (RAG) applications. This model is built on **EuroBERT-610M**, which has been specifically chosen for its extended context support (up to **8192 tokens**) and strong multilingual capabilities. 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 French large model utilizing EuroBERT-610M architecture**
+## Model Details
+- **Architecture:** EuroBERT-610M with extended context support (up to 8192 tokens)
+- **Task:** Token Classification / Hallucination Detection
+- **Training Dataset:** RagTruth-FR (translated from the original RAGTruth dataset)
+- **Language:** French
+## How It Works
+The model is trained to identify tokens in the French 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
+# For a transformer-based approach:
+detector = HallucinationDetector(
+    method="transformer",
+    model_path="KRLabsOrg/lettucedect-610m-eurobert-fr-v1",
+    lang="fr",
+    trust_remote_code=True
+)
+contexts = ["La France est un pays d'Europe. La capitale de la France est Paris. La population de la France est de 67 millions."]
+question = "Quelle est la capitale de la France? Quelle est la population de la France?"
+answer = "La capitale de la France est Paris. La population de la France est de 69 millions."
+# Get span-level predictions indicating which parts of the answer are considered hallucinated.
+predictions = detector.predict(context=contexts, question=question, answer=answer, output_format="spans")
+print("Prédictions:", predictions)
+# Prédictions: [{'start': 36, 'end': 81, 'confidence': 0.9726481437683105, 'text': ' La population de la France est de 69 millions.'}]
+```
+## Performance
+**Results on Translated RAGTruth-FR**
+We evaluate our French models on translated versions of the [RAGTruth](https://aclanthology.org/2024.acl-long.585/) dataset. The EuroBERT-610M French model achieves an F1 score of 73.13%, significantly outperforming prompt-based methods like GPT-4.1-mini (62.37%) with a substantial improvement of +10.76 percentage points.
+For detailed performance metrics, see the table below:
+| Language | Model           | Precision (%) | Recall (%) | F1 (%) | GPT-4.1-mini F1 (%) | Δ F1 (%) |
+|----------|-----------------|---------------|------------|--------|---------------------|----------|
+| French  | EuroBERT-210M   | 58.86         | 74.34      | 65.70  | 62.37               | +3.33    |
+| French  | EuroBERT-610M   | **67.08**     | **80.38**  | **73.13**  | 62.37               | **+10.76**   |
+The 610M model offers the best performance with over 7 percentage points improvement in F1 score compared to the 210M model. It particularly excels in recall, detecting more hallucinations with an 80.38% recall rate.
+## 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},
+}
+```