ruRoPEBert-e5-base-2k
| Property | Value |
|---|---|
| Parameter Count | 139M |
| Model Type | Feature Extraction |
| Architecture | RoPEBert |
| Context Window | 2048 tokens |
| Paper | CulturaX Paper |
What is ruRoPEBert-e5-base-2k?
ruRoPEBert-e5-base-2k is an advanced Russian language encoder model developed by Tochka AI. Built on the RoPEBert architecture, it's specifically designed for generating high-quality text embeddings and feature extraction. The model was trained on the comprehensive CulturaX dataset and surpasses previous models in quality according to the S+W score of the encodechka benchmark.
Implementation Details
The model implements sophisticated features including efficient attention mechanisms through SDPA and flexible RoPE scaling options. It requires transformers version 4.37.2 or higher and must be loaded with trust_remote_code=True to ensure proper functionality. The architecture includes built-in pooling mechanisms with options for mean pooling or first token transformation.
- Supports context lengths up to 2048 tokens with extensibility options
- Implements both eager and SDPA attention mechanisms
- Features built-in mean pooling and first token transformation options
- Supports dynamic and linear RoPE scaling for context extension
Core Capabilities
- High-quality Russian language text embeddings generation
- Efficient feature extraction for downstream tasks
- Flexible context window scaling through RoPE mechanisms
- Support for classification tasks with trainable classification head
- Batch processing with cosine similarity calculations
Frequently Asked Questions
Q: What makes this model unique?
The model's unique strength lies in its superior performance on the encodechka benchmark, particularly in Russian language tasks. It combines the benefits of the RoPEBert architecture with extensive training on the CulturaX dataset, resulting in state-of-the-art embedding quality for Russian language processing.
Q: What are the recommended use cases?
The model is ideal for tasks requiring high-quality Russian text embeddings, including semantic similarity analysis, text classification, and feature extraction for downstream NLP tasks. It's particularly effective for applications requiring processing of longer text sequences up to 2048 tokens.
