rut5-small
| Property | Value |
|---|---|
| Parameter Count | 64.6M |
| License | MIT |
| Tensor Type | F32 |
| Model Size | 246MB |
What is rut5-small?
rut5-small is a compact Russian language paraphrasing model derived from Google's mt5-small architecture. This model represents a significant optimization over its predecessor, reducing the parameter count from 300M to 64.6M while maintaining functionality for Russian language tasks.
Implementation Details
The model achieves its efficiency through intelligent vocabulary pruning, reducing the original sentencepiece vocabulary from 250K to 20K tokens, specifically optimized for Russian language processing. The vocabulary consists of 5K tokens from the original mt5-small model and 15K tokens derived from the Leipzig corpora collection's Russian web corpus.
- Optimized vocabulary focused on Russian language
- Reduced model size from 1.1GB to 246MB
- Efficient parameter distribution with restructured embeddings
- PyTorch-based implementation with Transformers library support
Core Capabilities
- Russian text paraphrasing
- Potential for fine-tuning on specific tasks
- Efficient inference with reduced memory footprint
- Support for batch processing and generation control parameters
Frequently Asked Questions
Q: What makes this model unique?
The model's uniqueness lies in its optimized architecture that maintains Russian language capabilities while significantly reducing model size through intelligent vocabulary pruning, making it more accessible for deployment in resource-constrained environments.
Q: What are the recommended use cases?
The model is primarily designed for Russian language paraphrasing tasks, though it can be fine-tuned for other Russian NLP tasks. It's particularly suitable for applications where model size and efficiency are crucial considerations.
