ruElectra-small
| Property | Value |
|---|---|
| License | MIT |
| Language | Russian |
| Framework Support | PyTorch, TensorFlow |
| Research Paper | arXiv:2309.10931 |
What is ruElectra-small?
ruElectra-small is a compact Russian language model based on the ELECTRA architecture, developed by the SaluteDevices RnD Team. It's specifically designed for generating high-quality embeddings for Russian text, with optimized performance through mean token embeddings.
Implementation Details
The model implements the ELECTRA architecture in a small form factor, offering a balance between performance and resource efficiency. It can be easily integrated using either PyTorch or TensorFlow frameworks through the Hugging Face Transformers library.
- Supports variable length inputs with padding and truncation
- Implements mean pooling for optimal embedding generation
- Maximum sequence length of 24 tokens
- Provides both token-level and sentence-level embeddings
Core Capabilities
- Russian text embedding generation
- Sentence similarity tasks
- Text representation for downstream NLP tasks
- Efficient processing of short to medium-length texts
Frequently Asked Questions
Q: What makes this model unique?
This model is specifically optimized for Russian language processing and is part of a larger family of Russian transformer models. Its small size makes it suitable for deployment in resource-constrained environments while maintaining good performance through mean token embeddings.
Q: What are the recommended use cases?
The model is best suited for tasks requiring Russian text embeddings, including semantic similarity comparison, text classification, and as a feature extractor for downstream NLP tasks. It's particularly effective when using mean pooling for sentence embeddings.
