MODEL PROFILE

styledistance

StyleDistance · styledistance

This repository contains the model introduced in StyleDistance: Stronger Content-Independent Style Embeddings with Synthetic Parallel Examples. StyleDistance is a **style embedding model** that aims to embed texts with similar writing styles closely and different styles far apart, regardless of content. You may find this model useful for stylistic analysis of text, clustering, authorship identfication and verification tasks, and automatic style transfer evaluation. StyleDistance was contrastively trained on SynthSTEL, a synthetically generated dataset of positive and negative examples of 40 style features being used in text. By utilizing this synthetic dataset, StyleDistance is able to achieve stronger content-independence than other style embeddding models currently available. 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. For a version that is purely trained on synthetic data, see this other version of StyleDistance.

Context windowNot disclosed
Input → OutputText → Embedding
Model weightsNot verified
Profile updated2026/10/30 22:43:10
OVERVIEW

Model overview

This repository contains the model introduced in StyleDistance: Stronger Content-Independent Style Embeddings with Synthetic Parallel Examples. StyleDistance is a **style embedding model** that aims to embed texts with similar writing styles closely and different styles far apart, regardless of content. You may find this model useful for stylistic analysis of text, clustering, authorship identfication and verification tasks, and automatic style transfer evaluation. StyleDistance was contrastively trained on SynthSTEL, a synthetically generated dataset of positive and negative examples of 40 style features being used in text. By utilizing this synthetic dataset, StyleDistance is able to achieve stronger content-independence than other style embeddding models currently available. 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. For a version that is purely trained on synthetic data, see this other version of StyleDistance.

Key features

特点摘要待依据来源补齐,不凭模型名称推断能力。

Limitations & considerations

Sources are insufficient to establish specific limitations. Verify task performance, latency, data policies, licensing and version lifecycle.

PRICING & COST

Source prices & cost analysis

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Estimate monthly base token costs

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INDEPENDENT BENCHMARKS

Public benchmarks & run configuration

暂无已采集的可核验公开评分。模型未上榜不代表能力较低。

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Frequently asked questions

Do open weights permit free commercial use?

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Sources & traceability

许可证:mit。模型介绍与第三方评测均以原来源的具体版本和方法为准。