Unggah-Ungguh-Javanese-GPT2-Classifier

Unggah-Ungguh-Javanese-GPT2-Classifier is part of the Unggah-Ungguh's model family, a classifier model for Javanese Honorific Classification task that was mentioned in "Do Language Models Understand Honorific Systems in Javanese?". Check out our paper for more information!

Model description

• Model type: A classifier model trained on a highly curated Unggah-Ungguh dataset that represent Javanese Honorific rules and systems.
• Language(s) NLP: Javanese
• License: CC-BY-NC 4.0
• Finetuned from model: w11wo/javanese-distilbert-small-imdb

Model Sources

• Project Page: https://javanesehonorifics.github.io/
• Repository: https://github.com/JavaneseHonorifics
• Paper: https://arxiv.org/abs/2502.20864

Using the model

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch
model_path = "JavaneseHonorifics/Unggah-Ungguh-Javanese-GPT2-Classifier"
tokenizer = AutoTokenizer.from_pretrained(model_path)
model = AutoModelForSequenceClassification.from_pretrained(model_path)
INPUT_TEXT = "Mbak Srini mangan pecel ajange pincuk"
tokenized_input = tokenizer([INPUT_TEXT], return_tensors="pt", truncation=True, padding=True)
with torch.no_grad():
    outputs = model(**tokenized_input)
    y_pred = outputs.logits.argmax(-1)
print("Predicted class:", y_pred.item())

License and Use

Unggah-Ungguh is licensed under the CC-BY-NC 4.0

Citation

@inproceedings{farhansyah-etal-2025-language,
    title = "Do Language Models Understand Honorific Systems in {J}avanese?",
    author = "Farhansyah, Mohammad Rifqi  and
      Darmawan, Iwan  and
      Kusumawardhana, Adryan  and
      Winata, Genta Indra  and
      Aji, Alham Fikri  and
      Wijaya, Derry Tanti",
    editor = "Che, Wanxiang  and
      Nabende, Joyce  and
      Shutova, Ekaterina  and
      Pilehvar, Mohammad Taher",
    booktitle = "Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2025",
    address = "Vienna, Austria",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2025.acl-long.1296/",
    doi = "10.18653/v1/2025.acl-long.1296",
    pages = "26732--26754",
    ISBN = "979-8-89176-251-0",
    abstract = "The Javanese language features a complex system of honorifics that vary according to the social status of the speaker, listener, and referent. Despite its cultural and linguistic significance, there has been limited progress in developing a comprehensive corpus to capture these variations for natural language processing (NLP) tasks. In this paper, we present Unggah-Ungguh, a carefully curated dataset designed to encapsulate the nuances of Unggah-Ungguh Basa, the Javanese speech etiquette framework that dictates the choice of words and phrases based on social hierarchy and context. Using Unggah-Ungguh, we assess the ability of language models (LMs) to process various levels of Javanese honorifics through classification and machine translation tasks. To further evaluate cross-lingual LMs, we conduct machine translation experiments between Javanese (at specific honorific levels) and Indonesian. Additionally, we explore whether LMs can generate contextually appropriate Javanese honorifics in conversation tasks, where the honorific usage should align with the social role and contextual cues. Our findings indicate that current LMs struggle with most honorific levels, exhibiting a bias toward certain honorific tiers."
}