rubert-base-cased-sentence
| Property | Value |
|---|---|
| Parameters | 180M |
| Author | DeepPavlov |
| Downloads | 50,435 |
| Primary Paper | Sentence-BERT Paper |
What is rubert-base-cased-sentence?
rubert-base-cased-sentence is a specialized Russian language model based on BERT architecture, designed specifically for generating sentence embeddings. It's a 12-layer, 768-hidden, 12-heads transformer model that has been initialized with RuBERT and fine-tuned on translated SNLI dataset and Russian XNLI dev set.
Implementation Details
The model implements a representation-based sentence encoding approach, utilizing mean pooled token embeddings similar to Sentence-BERT methodology. It's built on the RuBERT foundation with careful optimization for Russian language processing.
- 12-layer transformer architecture
- 768 hidden dimensions
- 12 attention heads
- Case-sensitive processing
- Specialized for Russian language
Core Capabilities
- High-quality Russian sentence embeddings generation
- Cross-lingual inference capabilities through XNLI training
- Effective semantic similarity analysis for Russian text
- Support for both PyTorch and JAX frameworks
Frequently Asked Questions
Q: What makes this model unique?
The model's uniqueness lies in its specialized fine-tuning for Russian language sentence embeddings, combining the power of BERT architecture with specific optimizations for Russian text processing and semantic understanding.
Q: What are the recommended use cases?
This model is ideal for tasks requiring semantic sentence comparison in Russian, including text similarity analysis, document clustering, and semantic search applications. It's particularly effective for applications requiring deep understanding of Russian language semantics.
