msmarco-distilbert-base-v4
| Property | Value |
|---|---|
| Parameter Count | 66.4M |
| License | Apache 2.0 |
| Paper | Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks |
| Framework Support | PyTorch, TensorFlow, ONNX, OpenVINO |
What is msmarco-distilbert-base-v4?
msmarco-distilbert-base-v4 is a sophisticated sentence embedding model built on the DistilBERT architecture. It's designed to convert sentences and paragraphs into 768-dimensional dense vector representations, making it particularly effective for semantic search, clustering, and similarity comparison tasks. Developed by the sentence-transformers team, this model represents a careful balance between computational efficiency and performance.
Implementation Details
The model implements a two-stage architecture combining a DistilBERT transformer with a specialized pooling layer. It processes text with a maximum sequence length of 512 tokens and utilizes mean pooling to generate fixed-size sentence embeddings.
- Transformer base: DistilBERT architecture optimized for efficiency
- Output dimension: 768-dimensional dense vectors
- Pooling strategy: Mean pooling over token embeddings
- Maximum sequence length: 512 tokens
Core Capabilities
- Semantic text similarity computation
- Document clustering and organization
- Information retrieval and search
- Cross-lingual text matching
- Content-based recommendation systems
Frequently Asked Questions
Q: What makes this model unique?
This model stands out for its optimal balance between performance and efficiency, using DistilBERT's architecture while maintaining high-quality sentence embeddings. It's specifically optimized for the MS MARCO dataset, making it particularly effective for search-related tasks.
Q: What are the recommended use cases?
The model excels in semantic search applications, document similarity comparison, and clustering tasks. It's particularly well-suited for applications requiring fast and accurate text similarity measurements, such as search engines, content recommendation systems, and document classification tools.
