msmarco-distilbert-base-tas-b
| Property | Value |
|---|---|
| Parameter Count | 66.4M |
| Architecture | DistilBERT |
| Embedding Dimension | 768 |
| License | Apache 2.0 |
| Framework Support | PyTorch, TensorFlow, ONNX |
What is msmarco-distilbert-base-tas-b?
This model is a specialized sentence transformer built on the DistilBERT architecture, designed specifically for semantic search applications. It converts sentences and paragraphs into 768-dimensional dense vector representations, making it highly effective for text similarity tasks and information retrieval.
Implementation Details
The model utilizes a two-stage architecture combining a DistilBERT transformer with a specialized pooling layer. It processes input text through the transformer and applies CLS token pooling to generate the final embeddings. The model supports both sentence-transformers and HuggingFace Transformers implementations.
- Efficient architecture with 66.4M parameters
- 768-dimensional output embeddings
- CLS token pooling strategy
- Maximum sequence length of 512 tokens
Core Capabilities
- Semantic similarity computation
- Dense passage retrieval
- Text embeddings generation
- Cross-encoder scoring
Frequently Asked Questions
Q: What makes this model unique?
This model stands out for its optimization on the MS MARCO dataset and its efficient architecture that balances performance with computational requirements. The TAS-B variant specifically focuses on providing strong semantic search capabilities while maintaining reasonable resource usage.
Q: What are the recommended use cases?
The model is particularly well-suited for semantic search applications, document similarity matching, and information retrieval tasks. It excels in scenarios requiring efficient comparison of text passages or query-document matching.
