GTR-T5-Large Model
| Property | Value |
|---|---|
| Parameter Count | 335M |
| Model Type | Sentence Transformer |
| Architecture | T5-Large Encoder |
| License | Apache 2.0 |
| Paper | Large Dual Encoders Are Generalizable Retrievers |
What is gtr-t5-large?
GTR-T5-Large is a sophisticated sentence transformer model designed specifically for semantic search applications. It's a PyTorch-based implementation converted from the original TensorFlow gtr-large-1 model, capable of mapping sentences and paragraphs into 768-dimensional dense vector spaces. The model maintains FP16 precision and leverages the encoder portion of the T5-large architecture.
Implementation Details
The model is implemented using the sentence-transformers framework and requires version 2.2.0 or newer. It's optimized for efficiency with FP16 weight storage and can be easily deployed using PyTorch. The implementation maintains performance parity with the original TensorFlow model while offering the flexibility of the PyTorch ecosystem.
- 768-dimensional output embeddings
- FP16 weight storage for efficiency
- Compatible with sentence-transformers framework
- Converted from TensorFlow while maintaining accuracy
Core Capabilities
- Semantic sentence and paragraph embedding generation
- Optimized for retrieval tasks
- Efficient processing of text inputs
- State-of-the-art performance in semantic search applications
Frequently Asked Questions
Q: What makes this model unique?
This model stands out for its specific optimization for semantic search tasks while maintaining generalizable retrieval capabilities. It combines the power of the T5-large architecture with efficient FP16 storage, making it both powerful and practical for production deployments.
Q: What are the recommended use cases?
The model is ideal for semantic search applications, document similarity matching, and information retrieval tasks. It's particularly well-suited for applications requiring high-quality sentence embeddings for comparison and retrieval purposes.
