Style-Embedding
| Property | Value |
|---|---|
| Author | Anna Wegmann |
| Architecture | RoBERTa-based Sentence Transformer |
| Embedding Dimension | 768 |
| Paper | ACL Anthology |
What is Style-Embedding?
Style-Embedding is a sophisticated sentence transformer model designed to capture the nuances of writing style while being independent of content. It maps sentences and paragraphs to a 768-dimensional vector space, making it particularly effective for tasks like clustering and semantic search focused on writing style analysis.
Implementation Details
The model utilizes a RoBERTa-based architecture with mean pooling and was trained using a TripletLoss function with cosine distance metric. It was trained for 4 epochs with a learning rate of 2e-05 and includes 10,500 warmup steps. The model implements content control through conversation or domain labels to ensure style representations are truly independent of topic content.
- Maximum sequence length: 512 tokens
- Trained with batch size of 8
- Uses AdamW optimizer with weight decay of 0.01
- Implements mean pooling strategy for sentence embeddings
Core Capabilities
- Style-focused sentence embedding generation
- Content-independent style analysis
- Authorship verification tasks
- Clustering of stylistically similar texts
- Semantic search based on writing style
Frequently Asked Questions
Q: What makes this model unique?
This model's uniqueness lies in its ability to separate writing style from content, achieved through a novel training approach that controls for content using conversation or domain labels. This makes it particularly effective for pure style analysis tasks.
Q: What are the recommended use cases?
The model is ideal for authorship verification, style-based text clustering, stylometric analysis, and any application requiring the comparison of writing styles independent of the actual content being discussed.
