sentence-t5-xxl
| Property | Value |
|---|---|
| Parameter Count | 4.86B |
| Model Type | Sentence Transformer |
| Output Dimensions | 768 |
| License | Apache 2.0 |
| Paper | Sentence-T5: Scalable sentence encoders |
What is sentence-t5-xxl?
sentence-t5-xxl is a powerful sentence embedding model that converts sentences and paragraphs into 768-dimensional dense vector representations. Based on the T5-11B architecture, this model has been specifically optimized for sentence similarity tasks and represents a PyTorch conversion of the original TensorFlow st5-11b-1 model.
Implementation Details
The model utilizes only the encoder portion of a T5-11B architecture and stores weights in FP16 format for efficiency. It's implemented using the sentence-transformers framework, making it easily accessible for Python developers.
- Optimized for sentence similarity tasks
- 768-dimensional output embeddings
- FP16 weight storage for reduced memory footprint
- Requires sentence-transformers version 2.2.0 or newer
Core Capabilities
- High-quality sentence and paragraph embedding generation
- Excellent performance on sentence similarity tasks
- Efficient processing with FP16 precision
- Simple integration with Python applications
Frequently Asked Questions
Q: What makes this model unique?
This model stands out due to its large parameter count (4.86B) and specialized optimization for sentence similarity tasks. It's a direct PyTorch port of the Google st5-11b-1 model, maintaining comparable performance while offering better framework compatibility.
Q: What are the recommended use cases?
The model excels at sentence similarity tasks and can be effectively used for tasks requiring semantic sentence comparison. However, it's worth noting that it may not perform optimally for semantic search tasks.
