---
license: mit
language: en
base_model: google/t5-v1_1-large
library_name: transformers
tags:
  - style-transfer
  - authorship
---

# HyperStyler (Reddit)

> **Warning:** This model was trained on Reddit posts. Generated text may contain profanity, offensive, or discriminatory language.

HyperStyler model trained on the Reddit (MUD) dataset, from **HyperStyler: Low-resource Authorship Style Transfer via Context-aware Style Navigation and Hypernetworks** (EMNLP 2026).

Code: https://github.com/JK-SHIN-PG/HyperStyler

## Usage

```python
from transformers import AutoModel

model = AutoModel.from_pretrained("JKSHINPG/HyperStyler", trust_remote_code=True).to("cuda")

source = "I really think we should leave early tomorrow to avoid traffic."
target_texts = [  # a few texts by the target author
    "lol yeah that's what i said, nobody listens tho",
    "honestly idk why ppl still use that app",
    "ok fair point but its still overpriced imo",
]
print(model.transfer(source, target_texts))
```

## Citation

```bibtex
@inproceedings{shin-etal-2026-hyperstyler,
    title = "{H}yper{S}tyler: Low-resource Authorship Style Transfer via Context-aware Style Navigation and Hypernetworks",
    author = "Shin, Jongkyung  and
      Jeon, Minguk  and
      Park, Chan Woo  and
      Lim, Chiehyeon",
    editor = "Lee, Hung-Yi  and
      Dev, Sunipa  and
      Li, Junyi Jessy  and
      Elliott, Desmond",
    booktitle = "Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing",
    month = oct,
    year = "2026",
    address = "Budapest, Hungary",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2026.emnlp-main.1045/",
    doi = "10.18653/2026.emnlp-main.1045",
    pages = "24006--24028",
    ISBN = "979-8-89176-459-0",
    abstract = "Low-resource authorship style transfer (LAST) aims to rewrite text into the style of an arbitrary target author using only a few reference examples while preserving the original meaning. Existing methods often struggle to achieve both high style fidelity and semantic preservation because they compress diverse references into a single static author embedding, which averages out context-dependent stylistic variation, and rely on hidden representations for style control, which entangle style with content. We propose HyperStyler, a novel architecture that decouples LAST into style selection and style realization. Stylo-navigator predicts style coordinates by jointly modeling the source context and target-author references, and Stylo-hypernet realizes them via dynamic parameter modulation instead of hidden-state injection. Our experiments on Reddit, Blog, and News datasets demonstrate that HyperStyler consistently outperforms prior methods including LLM-based approaches and generalizes robustly across domains. Notably, HyperStyler achieves superior performance with as few as 2.4{\%} additional parameters over T5-large, while being over 1.8{\texttimes} faster than LLMs at inference."
}
```