rubert-base-cased
| Property | Value |
|---|---|
| Parameter Count | 180M |
| Model Type | BERT (Russian) |
| Architecture | 12-layer, 768-hidden, 12-heads |
| Research Paper | arXiv:1905.07213 |
| Author | DeepPavlov |
What is rubert-base-cased?
rubert-base-cased is a Russian language model based on BERT architecture, specifically adapted for Russian language processing. Developed by DeepPavlov, it's a powerful transformer-based model trained on Russian Wikipedia and news data, maintaining case sensitivity for better language understanding.
Implementation Details
The model is built upon a multilingual BERT-base architecture and fine-tuned specifically for Russian language processing. As of November 2021, it includes both Masked Language Modeling (MLM) and Next Sentence Prediction (NSP) heads, making it suitable for various NLP tasks.
- 180 million parameters
- 12 transformer layers
- 768 hidden dimensions
- 12 attention heads
- Case-sensitive processing
Core Capabilities
- Russian text understanding and processing
- Masked Language Modeling (MLM)
- Next Sentence Prediction (NSP)
- Support for case-sensitive applications
- Adaptable for various downstream NLP tasks
Frequently Asked Questions
Q: What makes this model unique?
This model is specifically optimized for Russian language processing, using a custom vocabulary of Russian subtokens and trained on Russian-specific data, making it more effective than general multilingual models for Russian language tasks.
Q: What are the recommended use cases?
The model is ideal for Russian language processing tasks including text classification, named entity recognition, question answering, and other NLP applications requiring deep understanding of Russian language context.
