paraphrase-distilroberta-base-v1
| Property | Value |
|---|---|
| Parameters | 82.1M |
| License | Apache 2.0 |
| Framework | PyTorch, TensorFlow, JAX, ONNX |
| Paper | Sentence-BERT Paper |
What is paraphrase-distilroberta-base-v1?
This is a specialized sentence transformer model designed to convert sentences and paragraphs into 768-dimensional dense vector representations. Built on DistilRoBERTa architecture, it's optimized for semantic similarity tasks and can be effectively used for clustering and semantic search applications.
Implementation Details
The model implements a two-component architecture combining a DistilRoBERTa transformer with a pooling layer. It processes text with a maximum sequence length of 128 tokens and uses mean pooling to generate sentence embeddings.
- Transformer base: DistilRoBERTa with modern attention mechanisms
- Embedding dimension: 768
- Pooling strategy: Mean pooling over token embeddings
- Optimized for paraphrase detection and semantic similarity
Core Capabilities
- Sentence and paragraph embedding generation
- Semantic similarity computation
- Text clustering
- Semantic search operations
- Cross-lingual text comparison
Frequently Asked Questions
Q: What makes this model unique?
This model stands out for its efficient architecture that combines DistilRoBERTa's power with specialized training for paraphrase detection, making it particularly effective for semantic similarity tasks while maintaining computational efficiency.
Q: What are the recommended use cases?
The model is ideal for applications requiring semantic search, document clustering, paraphrase detection, and any task requiring comparison of text segments based on meaning rather than exact matching.
