---
license: mit
datasets:
- newmindai/RAGTruth-TR
language:
- tr
- en
metrics:
- precision
- recall
- f1
- roc_auc
base_model:
- EuroBERT/EuroBERT-210m
pipeline_tag: token-classification
---

# lettucedect-210m-eurobert-tr-v1

## Model Description

**lettucedct-210m-eurobert-tr-v1** is a multilingual hallucination detection model based on the EuroBERT architecture, fine-tuned for Turkish hallucination detection tasks. This model is part of the Turk-LettuceDetect suite and demonstrates strong cross-lingual generalization capabilities for detecting hallucinations in Turkish Retrieval-Augmented Generation (RAG) applications.

## Model Details

- **Model Type:** Token-level binary classifier for hallucination detection
- **Base Architecture:** EuroBERT-base
- **Language:** Turkish (tr) with multilingual capabilities
- **Training Dataset:** Machine-translated RAGTruth dataset (17,790 training instances)
- **Context Length:** Up to 8,192 tokens
- **Model Size:** ~210M parameters

## Intended Use

### Primary Use Cases
- Hallucination detection in Turkish RAG systems
- Cross-lingual hallucination detection applications
- Data-to-text generation verification (strongest performance area)
- Multilingual NLP pipelines requiring Turkish support

### Supported Tasks
- Question Answering (QA) hallucination detection
- Data-to-text generation verification (**strongest performance**)
- Text summarization fact-checking

## Performance

### Overall Performance (F1-Score)
- **Whole Dataset:** 0.7777
- **Question Answering:** 0.7317
- **Data-to-text Generation:** 0.8030 (**best in suite**)
- **Summarization:** 0.6057

### Key Strengths
- **Best performance in data-to-text generation**
- Robust multilingual transfer learning capabilities

## Training Details

### Training Data
- **Dataset:** Machine-translated RAGTruth benchmark
- **Size:** 17,790 training instances, 2,700 test instances
- **Tasks:** Question answering (MS MARCO), data-to-text (Yelp), summarization (CNN/Daily Mail)
- **Translation Model:** Google Gemma-3-27b-it

### Training Configuration
- **Epochs:** 6
- **Learning Rate:** 1e-5
- **Batch Size:** 4
- **Hardware:** NVIDIA A100 40GB GPU
- **Training Time:** ~2 hours
- **Optimization:** Cross-entropy loss with token masking

### Multilingual Foundation
- Built on EuroBERT architecture supporting multiple European languages
- Demonstrates effective multilingual transfer learning
- No full in-language retraining required due to strong cross-lingual capabilities

## Technical Specifications

### Architecture Features
- **Base Model:** EuroBERT multilingual encoder
- **Maximum Sequence Length:** 8,192 tokens
- **Classification Head:** Binary token-level classifier
- **Multilingual Support:** European languages with strong Turkish adaptation
- **Parameter Count:** 210M parameters

### Input Format
```
Input: [CONTEXT] [QUESTION] [GENERATED_ANSWER]
Output: Token-level binary labels (0=supported, 1=hallucinated)
```

## Limitations and Biases

### Known Limitations
- Reduced effectiveness in summarization compared to structured tasks
- Performance dependent on translation quality of training data
- Optimized primarily for European language patterns

### Potential Biases
- Translation artifacts from machine-translated training data
- Multilingual transfer bias favoring European linguistic patterns
- May perform differently on Turkish dialects or informal text

## Usage

### Installation
```bash
pip install lettucedetect
```

### Basic Usage
```python
from lettucedetect.models.inference import HallucinationDetector

# Initialize the Turkish-specific hallucination detector
detector = HallucinationDetector(
    method="transformer", 
    model_path="newmindai/modernbert-tr-uncased-stsb-HD"
)

# Turkish context, question, and answer
context = "İstanbul Türkiye'nin en büyük şehridir. Şehir 15 milyonluk nüfusla Avrupa'nın en kalabalık şehridir."
question = "İstanbul'un nüfusu nedir? İstanbul Avrupa'nın en kalabalık şehri midir?"
answer = "İstanbul'un nüfusu yaklaşık 16 milyondur ve Avrupa'nın en kalabalık şehridir."

# Get span-level predictions (start/end indices, confidence scores)
predictions = detector.predict(
    context=context, 
    question=question, 
    answer=answer, 
    output_format="spans"
)

print("Tespit Edilen Hallusinasyonlar:", predictions)
# Örnek çıktı: 
# [{'start': 34, 'end': 57, 'confidence': 0.92, 'text': 'yaklaşık 16 milyondur'}]
```


## Evaluation

### Benchmark Results
Evaluated on machine-translated Turkish RAGTruth test set, demonstrating the effectiveness of multilingual transfer learning for Turkish hallucination detection, particularly excelling in data-to-text generation tasks.

**Example-level Results**

<img 
  src="https://cdn-uploads.huggingface.co/production/uploads/683d4880e639f8d647355997/RejTWu3JNjH8t0teV1Txf.png" 
  width="1000" 
  style="object-fit: contain; margin: auto; display: block;"
/>

**Token-level Results**

<img 
  src="https://cdn-uploads.huggingface.co/production/uploads/683d4880e639f8d647355997/ECyrfN5Jv8fZSM0svxLXq.png" 
  width="500" 
  style="object-fit: contain; margin: auto; display: block;"
/>

## Citation

```bibtex
@inproceedings{turklettucedetect2025,
  title={Turk-LettuceDetect: A Hallucination Detection Models for Turkish RAG Applications},
  author={NewMind AI Team},
  booktitle={9th International Artificial Intelligence and Data Processing Symposium (IDAP'25)},
  year={2025},
  address={Malatya, Turkey}
}
```

## Original LettuceDetect Framework

This model extends the LettuceDetect methodology:
```bibtex
@article{lettucedetect2025,
  title={LettuceDetect: a hallucination detection framework for RAG applications},
  author={Kovács, Á. and Ács, B. and Kovács, D. and Szendi, S. and Kadlecik, Z. and Dávid, S.},
  journal={arXiv preprint arXiv:2502.17125},
  year={2025}
}
```

## License

This model is released under an open-source license to support research and development in Turkish and multilingual NLP applications.

## Contact

For questions about this model or other Turkish hallucination detection models, please refer to the original paper or contact the authors.

---