Instructions to use TrustHLT/ModernBERT-large-madon-formalism with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use TrustHLT/ModernBERT-large-madon-formalism with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="TrustHLT/ModernBERT-large-madon-formalism")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("TrustHLT/ModernBERT-large-madon-formalism") model = AutoModelForSequenceClassification.from_pretrained("TrustHLT/ModernBERT-large-madon-formalism") - Notebooks
- Google Colab
- Kaggle
ModernBERT-large-madon-formalism
This model is a companion for the paper Mining Legal Arguments to Study Judicial Formalism. It is part of the MADON project, which aims to study judicial reasoning in the decisions of Czech Supreme Courts.
Model Description
This model is based on the ModernBERT architecture and was adapted to the Czech legal domain through continued pretraining on a corpus of over 300,000 Czech court decisions. It is specifically fine-tuned for the holistic formalism classification of full legal documents, identifying whether a judicial decision is formalistic or non-formalistic.
- Task: Holistic formalism classification
- Language: Czech
- Dataset: MADON (Czech Supreme Court decisions)
- Repository: trusthlt/madon
Usage
If you want to use it for holistic formalism classification of Czech legal court documents, we suggest:
from transformers import AutoModelForSequenceClassification, AutoTokenizer, pipeline
model = AutoModelForSequenceClassification.from_pretrained("TrustHLT/ModernBERT-large-madon-formalism")
tokenizer = AutoTokenizer.from_pretrained("TrustHLT/ModernBERT-large-madon-formalism")
pipe = pipeline("text-classification", model=model, tokenizer=tokenizer)
# The expected input is a full Czech legal court document
text = "Dovolání se zamítá..." # Example Czech legal text
print(pipe(text))
Citation
If you use this model or the MADON dataset in your research, please cite the following paper:
@article{madon2025mining,
title={Mining Legal Arguments to Study Judicial Formalism},
author={Kocián, Michal and Šavelka, Jaromír and Moravčík, Jakub and Gavenčiak, Tomáš and Harašta, Jakub and Štefánik, Michal},
journal={arXiv preprint arXiv:2512.11374},
year={2025}
}
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