GODEL-v1_1-base-seq2seq
| Property | Value |
|---|---|
| License | MIT |
| Author | Microsoft |
| Paper | arXiv:2206.11309 |
| Training Data | 551M multi-turn dialogs + 5M instruction dialogs |
What is GODEL-v1_1-base-seq2seq?
GODEL (Goal-Directed Dialog Pre-training) is a sophisticated language model specifically designed for goal-directed dialog systems. Developed by Microsoft, it represents a significant advancement in conversational AI, utilizing a Transformer-based encoder-decoder architecture trained on an extensive dataset of 551M multi-turn dialogs from Reddit and 5M instruction-based dialogs.
Implementation Details
The model employs a sequence-to-sequence architecture optimized for response generation grounded in external text. It's implemented using the PyTorch framework and integrates seamlessly with the Hugging Face Transformers library.
- Transformer-based encoder-decoder architecture
- Support for external knowledge integration
- Efficient fine-tuning capabilities
- Optimized for both chitchat and knowledge-grounded responses
Core Capabilities
- Multi-turn dialog generation
- Knowledge-grounded response generation
- Empathetic response generation
- Context-aware conversation handling
- Instruction-following in dialog generation
Frequently Asked Questions
Q: What makes this model unique?
GODEL stands out for its ability to incorporate external knowledge into conversations and its efficient fine-tuning capabilities with minimal task-specific data. The model can be adapted to new dialog tasks with just a handful of examples, making it highly versatile for various applications.
Q: What are the recommended use cases?
The model is particularly well-suited for building chatbots that require knowledge-grounded responses, customer service applications, and any conversational systems that need to maintain context-aware, empathetic interactions while incorporating external information.
