ruDialoGPT-small
| Property | Value |
|---|---|
| Base Model | sberbank-ai/rugpt3small_based_on_gpt2 |
| Training Context Size | 3 |
| Sensibleness Score | 0.64 |
| Specificity Score | 0.50 |
| SSA Score | 0.57 |
| Author | t-bank-ai |
What is ruDialoGPT-small?
ruDialoGPT-small is a specialized Russian language dialogue generation model built upon the rugpt3small architecture. It's specifically designed for creating conversational AI agents, with a focus on producing coherent and contextually appropriate responses in dialogue scenarios.
Implementation Details
The model utilizes the Hugging Face transformers library and implements a dialogue-oriented architecture trained on an extensive corpus of conversational data. It features controllable generation parameters including top-k, top-p sampling, and beam search for response generation.
- Trained with context window size of 3 for maintaining conversation coherence
- Implements temperature and repetition penalty for response diversity
- Supports no-repeat n-gram size configuration
- Uses special tokens (@@ПЕРВЫЙ@@, @@ВТОРОЙ@@) for speaker identification
Core Capabilities
- Context-aware dialogue generation in Russian
- Sensibleness score of 0.64 for logical response coherence
- Specificity score of 0.50 for response relevance
- Combined SSA (Sensibleness Specificity Average) of 0.57
- Configurable generation parameters for different use cases
Frequently Asked Questions
Q: What makes this model unique?
The model's specialization in Russian dialogue generation, combined with its measured performance metrics for sensibleness and specificity, makes it particularly suitable for practical conversational AI applications. Its architecture allows for fine-tuned control over response generation parameters.
Q: What are the recommended use cases?
The model is best suited for building Russian language chatbots, dialogue systems, and conversational agents where context-aware responses are crucial. It's particularly useful in applications requiring maintained conversation context and natural-sounding responses.
