question-answering-generative-t5-v1-base-s-q-c
| Property | Value |
|---|---|
| Parameter Count | 248M |
| Model Type | Text2Text Generation |
| Architecture | T5-based Generative Model |
| Training Performance | RougeL: 0.8022 |
What is question-answering-generative-t5-v1-base-s-q-c?
This is a sophisticated question-answering model built on the T5 architecture, specifically designed to generate accurate answers from given context. The model was fine-tuned from a question generation model, achieving impressive performance metrics with a final validation loss of 0.6751 and RougeL score of 0.8022.
Implementation Details
The model utilizes a seq2seq architecture with 248M parameters, trained using the Adam optimizer with carefully tuned hyperparameters. Training was conducted over 5 epochs with a learning rate of 0.0003 and batch size of 3, implementing a linear learning rate scheduler.
- Input format: "question: [query] question_context: [context]"
- Maximum sequence length: 1024 tokens
- Generation parameters: max_length=30, min_length=5, num_beams=2
Core Capabilities
- Generative question answering with context comprehension
- Efficient text generation with beam search
- Handles both short and detailed responses
- Optimized for natural language understanding
Frequently Asked Questions
Q: What makes this model unique?
This model combines the power of T5 architecture with specialized fine-tuning for question answering, offering generative capabilities rather than extractive answers. Its high RougeL score of 0.8022 demonstrates superior performance in generating accurate and relevant responses.
Q: What are the recommended use cases?
The model is ideal for applications requiring contextual question answering, such as chatbots, educational tools, and information retrieval systems. It excels at generating natural language answers based on provided context.
