stsb-roberta-base-v2
| Property | Value |
|---|---|
| Parameter Count | 125M |
| Output Dimensions | 768 |
| License | Apache 2.0 |
| Paper | Sentence-BERT Paper |
What is stsb-roberta-base-v2?
stsb-roberta-base-v2 is a specialized sentence embedding model based on RoBERTa architecture, designed to convert sentences and paragraphs into fixed-size dense vector representations. It's particularly optimized for semantic similarity tasks and can map text to 768-dimensional vector space.
Implementation Details
The model implements a two-component architecture: a RoBERTa transformer followed by a pooling layer. It processes input text with a maximum sequence length of 75 tokens and uses mean pooling to generate sentence embeddings.
- Built on RoBERTa base architecture
- Implements mean pooling strategy
- Supports multiple framework implementations (PyTorch, TensorFlow, JAX)
- Optimized for sentence-level semantic tasks
Core Capabilities
- Sentence and paragraph embedding generation
- Semantic similarity computation
- Clustering and semantic search applications
- Cross-sentence relationship modeling
Frequently Asked Questions
Q: What makes this model unique?
This model uniquely combines RoBERTa's robust language understanding capabilities with specialized training for semantic similarity tasks. It offers a balance between performance and efficiency with its 125M parameter size.
Q: What are the recommended use cases?
The model excels in semantic search, document clustering, and similarity comparison tasks. It's particularly useful for applications requiring semantic understanding of sentences and paragraphs in a computationally efficient manner.
