gtr-t5-base
| Property | Value |
|---|---|
| Parameter Count | 110M |
| Model Type | Sentence Transformer |
| Architecture | T5-base Encoder |
| License | Apache 2.0 |
| Paper | Large Dual Encoders Are Generalizable Retrievers |
What is gtr-t5-base?
gtr-t5-base is a specialized sentence transformer model designed for semantic search applications. It's a PyTorch conversion of the original TensorFlow gtr-base-1 model, maintaining equivalent performance while offering better framework compatibility. The model maps sentences and paragraphs to 768-dimensional dense vector spaces, enabling efficient semantic similarity comparisons.
Implementation Details
The model is built on the T5-base architecture, utilizing only the encoder component. It's optimized with FP16 precision for efficient memory usage and faster inference. Implementation requires the sentence-transformers library (version 2.2.0 or newer) and can be easily integrated into existing Python workflows.
- 768-dimensional output vectors
- FP16 weight storage for efficiency
- Compatible with sentence-transformers framework
- PyTorch-based implementation
Core Capabilities
- Sentence and paragraph embedding generation
- Semantic similarity computation
- Efficient text retrieval
- Cross-lingual text matching
- Document similarity analysis
Frequently Asked Questions
Q: What makes this model unique?
This model stands out for its efficient implementation of the GTR architecture in PyTorch, offering state-of-the-art performance in semantic search while maintaining a relatively small parameter count of 110M. The FP16 precision makes it particularly suitable for production deployments.
Q: What are the recommended use cases?
The model excels in semantic search applications, document similarity analysis, and information retrieval tasks. It's particularly well-suited for applications requiring efficient text embedding generation and similarity computations at scale.
