Styledistance

🚀 StyleDistance - Sentence Similarity Model

This project presents StyleDistance, a sentence - similarity model that focuses on style embeddings. It can effectively embed texts with similar writing styles closely and different styles far apart, regardless of the content. This model is highly useful for tasks such as stylistic analysis, clustering, authorship identification, and automatic style - transfer evaluation.

📦 Model Information

🚀 Quick Start

This repository contains the model introduced in StyleDistance: Stronger Content - Independent Style Embeddings with Synthetic Parallel Examples.

StyleDistance is a style embedding model aiming to embed texts with similar writing styles closely and different styles far apart, regardless of content. It can be beneficial for stylistic analysis of text, clustering, authorship identification and verification tasks, and automatic style transfer evaluation.

✨ Features

Training Data and Variants of StyleDistance

StyleDistance was contrastively trained on SynthSTEL, a synthetically generated dataset of positive and negative examples of 40 style features being used in text. By using this synthetic dataset, StyleDistance can achieve stronger content - independence than other current style embedding models. This particular model was trained using a combination of the synthetic dataset and a [real dataset that makes use of authorship datasets from Reddit to train style embeddings](https://aclanthology.org/2022.repl4nlp - 1.26/). For a version purely trained on synthetic data, see this other version of StyleDistance.

💻 Usage Examples

Basic Usage

from sentence_transformers import SentenceTransformer
from sentence_transformers.util import cos_sim

model = SentenceTransformer('StyleDistance/styledistance') # Load model

input = model.encode("Did you hear about the Wales wing? He'll h8 2 withdraw due 2 injuries from future competitions.")
others = model.encode(["We're raising funds 2 improve our school's storage facilities and add new playground equipment!", "Did you hear about the Wales wing? He'll hate to withdraw due to injuries from future competitions."])
print(cos_sim(input, others))

Widget Examples

• Example 1
  • Source Sentence: Did you hear about the Wales wing? He'll h8 2 withdraw due 2 injuries from future competitions.
  • Comparison Sentences:
    • We're raising funds 2 improve our school's storage facilities and add new playground equipment!
    • Did you hear about the Wales wing? He'll hate to withdraw due to injuries from future competitions.
• Example 2
  • Source Sentence: You planned the DesignMeets Decades of Design event; you executed it perfectly.
  • Comparison Sentences:
    • We'll find it hard to prove the thief didn't face a real threat!
    • You orchestrated the DesignMeets Decades of Design gathering; you actualized it flawlessly.
• Example 3
  • Source Sentence: Did the William Barr maintain a commitment to allow Robert Mueller to finish the inquiry?
  • Comparison Sentences:
    • Will the artist be compiling a music album, or will there be a different focus in the future?
    • Did William Barr maintain commitment to allow Robert Mueller to finish inquiry?

📄 License

The model is released under the MIT license.

📚 Citation

@misc{patel2025styledistancestrongercontentindependentstyle,
      title={StyleDistance: Stronger Content - Independent Style Embeddings with Synthetic Parallel Examples}, 
      author={Ajay Patel and Jiacheng Zhu and Justin Qiu and Zachary Horvitz and Marianna Apidianaki and Kathleen McKeown and Chris Callison - Burch},
      year={2025},
      eprint={2410.12757},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2410.12757}, 
}

🔗 Trained with DataDreamer

This model was trained with a synthetic dataset with DataDreamer 🤖💤. The synthetic dataset card and model card can be found here. The training arguments can be found here.

💸 Funding Acknowledgements

This research is supported in part by the Office of the Director of National Intelligence (ODNI), Intelligence Advanced Research Projects Activity (IARPA), via the HIATUS Program contract #2022 - 22072200005. The views and conclusions contained herein are those of the authors and should not be interpreted as necessarily representing the official policies, either expressed or implied, of ODNI, IARPA, or the U.S. Government. The U.S. Government is authorized to reproduce and distribute reprints for governmental purposes notwithstanding any copyright annotation therein.