long-t5-tglobal-base-sci-simplify
| Property | Value |
|---|---|
| Base Model | google/long-t5-tglobal-base |
| Training Dataset | scientific_lay_summarisation-plos-norm |
| ROUGE-1 Score | 49.15 |
| Author | pszemraj |
| Model URL | Hugging Face |
What is long-t5-tglobal-base-sci-simplify?
This is a specialized language model designed to simplify complex scientific papers into lay-friendly summaries. Built on Google's Long-T5 architecture, it has been fine-tuned specifically on scientific literature to make research more accessible to non-experts by removing technical jargon and complex terminology.
Implementation Details
The model was trained for two epochs using the Adam optimizer with a learning rate of 0.0004 and cosine scheduling. Training was performed on multiple GPUs with a total batch size of 64, achieved through gradient accumulation steps of 16.
- Achieves ROUGE-1 score of 49.15 and ROUGE-2 score of 18.93
- Generates summaries with an average length of 399 tokens
- Implements beam search decoding for optimal results
- Easily integrated using the textsum utility package
Core Capabilities
- Converts complex scientific text into plain language summaries
- Handles long-form documents effectively
- Maintains scientific accuracy while improving accessibility
- Optimized for biomedical and scientific literature
Frequently Asked Questions
Q: What makes this model unique?
This model specifically addresses the challenge of making scientific literature accessible to non-experts through automated simplification, with particular strength in handling long-form content and maintaining accuracy while reducing complexity.
Q: What are the recommended use cases?
The model is best suited for simplifying scientific papers, particularly in the biomedical domain. It's ideal for creating lay summaries for research papers, making scientific content accessible to broader audiences, and helping non-experts understand complex research findings.
