sentence-t5-base
| Property | Value |
|---|---|
| Parameter Count | 110M |
| Tensor Type | FP16 |
| License | Apache 2.0 |
| Paper | Sentence-T5: Scalable sentence encoders |
What is sentence-t5-base?
sentence-t5-base is a specialized sentence transformer model that maps sentences and paragraphs to 768-dimensional dense vector representations. Converted from the original TensorFlow model st5-base-1, it's optimized for sentence similarity tasks while maintaining high performance and efficiency through FP16 weight storage.
Implementation Details
The model leverages only the encoder portion of a T5-base architecture and has been specifically adapted for sentence embedding tasks. It requires the sentence-transformers library (version 2.2.0 or newer) for implementation and can be easily integrated into existing NLP pipelines.
- 768-dimensional output vectors
- FP16 weight storage for efficiency
- PyTorch-based implementation
- Compatible with sentence-transformers framework
Core Capabilities
- Sentence and paragraph embedding generation
- Sentence similarity computation
- Text representation for downstream tasks
- Cross-sentence semantic analysis
Frequently Asked Questions
Q: What makes this model unique?
This model stands out for its efficient implementation of the T5 architecture specifically for sentence embedding tasks, offering a good balance between performance and resource usage with its 110M parameter size and FP16 weight storage.
Q: What are the recommended use cases?
The model excels at sentence similarity tasks and general text embedding generation, though it's noted to perform less optimally for semantic search tasks. It's ideal for applications requiring sentence-level comparison and analysis.
