t5-base-finetuned-break_data-question-retrieval
| Property | Value |
|---|---|
| Base Model | T5-base |
| Training Data | Break Dataset (20,633 samples) |
| Task | Question Decomposition to Natural Language |
| Author | Manuel Romero (mrm8488) |
What is t5-base-finetuned-break_data-question-retrieval?
This model is a specialized version of Google's T5-base transformer, fine-tuned on the Break dataset for converting Question Decomposition Meaning Representations (QDMRs) back into natural language questions. It represents an inverse process of question decomposition, effectively reconstructing complete questions from their broken-down components.
Implementation Details
The model is implemented using the T5 architecture and has been fine-tuned on 17,503 training samples and 3,130 validation samples from the Break dataset. It processes input text in a text-to-text format, treating the task as a specialized form of translation from QDMR syntax to natural language.
- Built on T5-base architecture
- Fine-tuned on Break dataset's QDMR-high-level examples
- Handles complex question reconstruction tasks
- Supports max output length of 64 tokens
Core Capabilities
- Converts decomposed question representations back into natural language
- Handles multiple decomposition steps in a single query
- Maintains semantic coherence in question reconstruction
- Processes complex logical relationships between question components
Frequently Asked Questions
Q: What makes this model unique?
This model is unique in its ability to reverse-engineer decomposed questions back into natural language, making it valuable for question generation and verification systems. It works with the Break dataset, which spans multiple domains including text, images, and databases.
Q: What are the recommended use cases?
The model is ideal for applications involving question generation, validation of question decomposition systems, and educational tools that need to work with both decomposed and natural language questions. It's particularly useful in QA systems that need to verify or generate questions from logical components.
