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| Metric | Value |
|---|---|
| ROUGE-1 | 0.27 |
| ROUGE-2 | 0.17 |
| ROUGE-L | 0.22 |
| BLEU | 0.10 |
| Perplexity | 1.86 |
1from transformers import AutoTokenizer, AutoModelForCausalLM
2
3tokenizer = AutoTokenizer.from_pretrained("biankasimkova/mistral-sk-7b-8b-finetuned-court-decisions-sk")
4model = AutoModelForCausalLM.from_pretrained("biankasimkova/mistral-sk-7b-8b-finetuned-court-decisions-sk")
5
6reasoning = "Súd zistil, že žalobca ..." # legal reasoning text
7inputs = tokenizer(reasoning, return_tensors="pt")
8outputs = model.generate(**inputs, max_new_tokens=512)
9print(tokenizer.decode(outputs[0], skip_special_tokens=True))@mastersthesis{Simkova2026,
author = {Bianka Šimková},
title = {Using large language models for analyzing decisions of Slovak courts},
school = {Institute of Artificial Intelligence,
Faculty of Electrical Engineering and Informatics,
Technical University of Košice},
year = {2026}
}