README.md

---
library_name: transformers
pipeline_tag: text-classification
language:
- cs
tags:
- legal
- modernbert
- czech
- legal-nlp
---

# ModernBERT-large-madon-arg-detection

This model is a fine-tuned version of ModernBERT-large for **Czech legal argument detection**. It was introduced in the paper [Mining Legal Arguments to Study Judicial Formalism](https://huggingface.co/papers/2512.11374).

The model is part of the [MADON project](https://github.com/trusthlt/madon/), which focuses on detecting and classifying judicial reasoning in Czech court decisions. This specific model corresponds to **Task 1** in the paper: detecting whether a paragraph in a legal decision is argumentative or non-argumentative.

## Model Description

The model was adapted to the Czech legal domain through continued pretraining on a corpus of over 300,000 court decisions and fine-tuned on the MADON dataset. In the paper's evaluation, this model achieved a **Balanced F1 score of 82.6%** for argument detection.

- **Paper:** [Mining Legal Arguments to Study Judicial Formalism](https://huggingface.co/papers/2512.11374)
- **Repository:** [TrustHLT/MADON](https://github.com/trusthlt/madon/)
- **Task:** Binary text classification (argumentative vs. non-argumentative)
- **Language:** Czech

## Usage

You can use this model for presence classification of Czech legal arguments using the `transformers` library:

```python
from transformers import AutoModelForSequenceClassification, AutoTokenizer, pipeline

model = AutoModelForSequenceClassification.from_pretrained("TrustHLT/ModernBERT-large-madon-arg-detection")
tokenizer = AutoTokenizer.from_pretrained("TrustHLT/ModernBERT-large-madon-arg-detection")

pipe = pipeline("text-classification", model=model, tokenizer=tokenizer)

text = "This is a legal paragraph" # Replace with Czech legal text

print(pipe(text))
```

## Citation

If you find this model useful, please cite:

```bibtex
@article{madon2025,
  title={Mining Legal Arguments to Study Judicial Formalism},
  author={Anonymous},
  journal={arXiv preprint arXiv:2512.11374},
  year={2025}
}
```