msmarco-bert-base-dot-v5
| Property | Value |
|---|---|
| Parameter Count | 109M |
| Embedding Dimensions | 768 |
| Max Sequence Length | 512 |
| Paper | Sentence-BERT Paper |
| Downloads | 123,187 |
What is msmarco-bert-base-dot-v5?
msmarco-bert-base-dot-v5 is a specialized sentence transformer model designed for semantic search applications. Built on BERT architecture, it transforms sentences and paragraphs into 768-dimensional dense vector representations, enabling efficient semantic similarity comparisons using dot-product scoring. The model was trained on 500,000 query-answer pairs from the Microsoft MARCO dataset, making it particularly effective for information retrieval tasks.
Implementation Details
The model employs mean pooling on BERT's contextual embeddings and does not produce normalized embeddings. It's implemented using the sentence-transformers framework and can process sequences up to 512 tokens. The model was trained using AdamW optimizer with a learning rate of 1e-05 and includes 10,000 warmup steps.
- Utilizes BERT-base-uncased as the foundation model
- Implements mean pooling strategy for sentence embeddings
- Optimized for dot-product similarity scoring
- Trained with MarginMSELoss for 30 epochs
Core Capabilities
- Semantic search and information retrieval
- Query-document similarity scoring
- Dense passage retrieval
- Text embedding generation for downstream tasks
Frequently Asked Questions
Q: What makes this model unique?
This model is specifically optimized for semantic search using dot-product similarity, trained on MS MARCO dataset, making it highly effective for query-document matching tasks. Its architecture is designed for efficient retrieval while maintaining strong semantic understanding.
Q: What are the recommended use cases?
The model excels in semantic search applications, document retrieval systems, and question-answering tasks. It's particularly well-suited for applications requiring fast similarity computation between queries and documents using dot-product scoring.
