| Property | Value |
|---|---|
| Parameter Count | 178M |
| Downloads | 623,524 |
| Language | Russian |
| Framework | PyTorch |
What is rubert-base-cased-sentiment-rusentiment?
This is a specialized Russian language model based on DeepPavlov's BERT architecture, fine-tuned specifically for sentiment analysis tasks. The model is trained on the RuSentiment dataset and can classify text into three sentiment categories: neutral, positive, and negative.
Implementation Details
The model is built upon the DeepPavlov/rubert-base-cased-conversational architecture and implements a sequence classification approach. It utilizes the transformers library and can process text sequences up to 512 tokens in length.
- Built on BERT architecture with cased tokenization
- Supports batch processing with padding
- Implements softmax activation for 3-class classification
- Uses PyTorch framework with transformers library support
Core Capabilities
- Three-class sentiment classification (Neutral: 0, Positive: 1, Negative: 2)
- Handles Russian text input effectively
- Supports inference with batched inputs
- Provides probability distributions across sentiment classes
Frequently Asked Questions
Q: What makes this model unique?
This model is specifically optimized for Russian sentiment analysis, utilizing the comprehensive RuSentiment dataset. With over 600,000 downloads, it has proven to be a reliable choice for Russian language sentiment classification tasks.
Q: What are the recommended use cases?
The model is ideal for analyzing sentiment in Russian social media posts, customer reviews, and general text content. It's particularly suitable for applications requiring three-way sentiment classification in Russian language contexts.