msmarco-distilbert-cos-v5
| Property | Value |
|---|---|
| Parameter Count | 66.4M |
| Embedding Dimensions | 768 |
| Framework | PyTorch, TensorFlow, ONNX |
| Research Paper | Sentence-BERT Paper |
What is msmarco-distilbert-cos-v5?
msmarco-distilbert-cos-v5 is a specialized sentence transformer model designed for semantic search applications. Built on the DistilBERT architecture, it maps sentences and paragraphs to 768-dimensional dense vector spaces, trained on 500,000 query-answer pairs from the MS MARCO Passages dataset.
Implementation Details
The model utilizes mean pooling for embedding generation and produces normalized embeddings with length 1. It supports multiple frameworks including PyTorch and TensorFlow, making it versatile for different deployment scenarios.
- Normalized embeddings enable efficient similarity computations
- Supports dot-product, cosine-similarity, and euclidean distance scoring
- Compatible with sentence-transformers library for easy implementation
Core Capabilities
- Semantic text similarity computation
- Query-document matching
- Dense passage retrieval
- Cross-encoder scoring optimization
Frequently Asked Questions
Q: What makes this model unique?
This model's strength lies in its optimization for semantic search tasks, leveraging the efficient DistilBERT architecture while maintaining high-quality embeddings. Its training on MS MARCO makes it particularly effective for query-document matching scenarios.
Q: What are the recommended use cases?
The model excels in information retrieval tasks, semantic search applications, and document similarity comparisons. It's particularly well-suited for applications requiring fast and accurate text similarity measurements.
