rubert-base-cased-sentiment-new
| Property | Value |
|---|---|
| Parameter Count | 178M |
| Model Type | Text Classification |
| Dataset | Tatyana/ru_sentiment_dataset |
| Developer | Tatyana Voloshina |
What is rubert-base-cased-sentiment-new?
rubert-base-cased-sentiment-new is a specialized BERT-based model designed for sentiment analysis of Russian text. Built upon the BERT architecture, this model has been fine-tuned to classify text into three sentiment categories: neutral (0), positive (1), and negative (2). With 178M parameters, it represents a robust solution for Russian language sentiment processing.
Implementation Details
The model is implemented using PyTorch and leverages the Transformers architecture. It utilizes safetensors for efficient parameter storage and has been specifically trained on the Tatyana/ru_sentiment_dataset. The model is optimized for inference endpoints, making it suitable for production deployments.
- Built on RuBERT base architecture
- Supports three-way sentiment classification
- Includes safetensors implementation
- Optimized for production deployment
Core Capabilities
- Russian text sentiment analysis
- Multi-class sentiment classification
- Batch processing of text inputs
- Production-ready inference
Frequently Asked Questions
Q: What makes this model unique?
This model is specifically optimized for Russian language sentiment analysis, featuring a three-way classification system and utilizing modern safetensors technology for efficient parameter handling.
Q: What are the recommended use cases?
The model is ideal for analyzing sentiment in Russian text data, particularly useful for social media monitoring, customer feedback analysis, and general Russian language sentiment processing tasks. It should not be used to create hostile or alienating environments.
