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TrustHLT/Llama-3.1-8B-madon-arg-classification
Llama-3.1-8B-madon-arg-classification is a text classification model from TrustHLT. Use it when you need a label for a piece of text. It is set up for transformers.
This model is a fine-tuned version of Llama-3.1-8B for multi-label classification of legal arguments in Czech court decisions. It was introduced in the paper Mining Legal Arguments to Study Judicial Formalism.
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From the Hugging Face model README
This model is a fine-tuned version of Llama-3.1-8B for multi-label classification of legal arguments in Czech court decisions. It was introduced in the paper Mining Legal Arguments to Study Judicial Formalism.
The model is part of the MADON project, specifically focusing on Task 2: Argument type classification. It is trained to classify paragraphs from decisions of the Czech Supreme Courts into eight traditional types of legal arguments:
The model was adapted to the Czech legal domain through continued pretraining on a corpus of over 300,000 court decisions and fine-tuned using asymmetric loss and class weighting to address dataset imbalance.
If you want to use it for multi-label classification of Czech legal arguments, we suggest:
from transformers import AutoModelForSequenceClassification, AutoTokenizer, pipeline
model = AutoModelForSequenceClassification.from_pretrained("TrustHLT/Llama-3.1-8B-madon-arg-classification")
tokenizer = AutoTokenizer.from_pretrained("TrustHLT/Llama-3.1-8B-madon-arg-classification")
pipe = pipeline("text-classification", model=model, tokenizer=tokenizer, return_all_scores=True, function_to_apply="none")
text = "This is a legal paragraph"
print(pipe(text))
If you use this model or the MADON dataset in your research, please cite:
@article{madon2025mining,
title={Mining Legal Arguments to Study Judicial Formalism},
author={Anonymous},
journal={arXiv preprint arXiv:2512.11374},
year={2025}
}