| --- |
| 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." |
| } |
| ``` |
|
|
