ruBert-base
Property | Value |
---|---|
Parameter Count | 178M |
Training Data | 30GB |
License | Apache 2.0 |
Dictionary Size | 120,138 |
Paper | arXiv:2309.10931 |
What is ruBert-base?
ruBert-base is a Russian language model developed by the SberDevices team, specifically designed for mask-filling tasks. It represents a significant advancement in Russian natural language processing, being part of a family of transformer models detailed in the paper "A Family of Pretrained Transformer Language Models for Russian".
Implementation Details
The model utilizes a BERT-based encoder architecture with BPE (Byte Pair Encoding) tokenization. With 178 million parameters and trained on 30GB of Russian text data, it offers robust language understanding capabilities while maintaining reasonable computational requirements.
- Architecture: Transformer-based encoder
- Tokenization: BPE with 120,138 vocabulary size
- Training Volume: 30GB of Russian text
- Task Specialization: Mask filling
Core Capabilities
- Masked language modeling for Russian text
- Contextual word embeddings
- Support for downstream NLP tasks
- Integration with PyTorch and Transformers library
Frequently Asked Questions
Q: What makes this model unique?
The model is specifically optimized for Russian language processing, trained on a substantial 30GB dataset, and offers state-of-the-art performance for mask-filling tasks while maintaining a moderate parameter count of 178M.
Q: What are the recommended use cases?
The model is particularly well-suited for tasks involving masked language modeling in Russian text, text completion, and can serve as a foundation for fine-tuning on specific downstream NLP tasks requiring Russian language understanding.