paraphrase-distilroberta-base-v2
| Property | Value |
|---|---|
| Parameter Count | 82.1M |
| License | Apache 2.0 |
| Framework Support | PyTorch, TensorFlow, JAX, ONNX |
| Paper | Sentence-BERT Paper |
What is paraphrase-distilroberta-base-v2?
This is a sophisticated sentence transformer model that maps sentences and paragraphs into a 768-dimensional dense vector space. Built on the DistilRoBERTa architecture, it's specifically designed for semantic similarity tasks, clustering, and information retrieval applications.
Implementation Details
The model implements a two-component architecture: a transformer-based encoding layer followed by a pooling layer. It processes input text with a maximum sequence length of 128 tokens and employs mean pooling for generating sentence embeddings.
- Built on DistilRoBERTa base architecture
- 768-dimensional output embeddings
- Supports multiple deep learning frameworks
- Optimized for sentence-level semantic representations
Core Capabilities
- Sentence and paragraph embedding generation
- Semantic similarity computation
- Text clustering
- Information retrieval
- Cross-lingual text comparison
Frequently Asked Questions
Q: What makes this model unique?
This model stands out for its efficient architecture that balances performance and computational requirements. As a distilled version of RoBERTa, it maintains strong performance while reducing the model size, making it practical for production deployments.
Q: What are the recommended use cases?
The model excels in applications requiring semantic similarity assessment, including duplicate detection, semantic search, cluster analysis of text data, and information retrieval systems. It's particularly effective for tasks where understanding sentence-level meaning is crucial.
