t5-base-korean-summarization
| Property | Value |
|---|---|
| Parameter Count | 276M |
| Model Type | T5 Transformer |
| Training Datasets | Korean Papers, Books, and Reports |
| Author | eenzeenee |
What is t5-base-korean-summarization?
This is a specialized Korean text summarization model based on the T5 architecture, fine-tuned on a diverse collection of Korean language datasets. The model builds upon the paust/pko-t5-base foundation and has been optimized for generating concise, accurate summaries of Korean text.
Implementation Details
The model utilizes a T5 architecture with 276M parameters and implements a text-to-text framework specifically adapted for Korean language processing. It was trained using a linear learning rate scheduler with a 4e-05 learning rate over 3 epochs, employing mixed precision training (FP16) for optimal performance.
- Achieves impressive ROUGE-2 Precision scores (>90%) across all dataset types
- Supports variable length outputs with configurable generation parameters
- Implements beam search with num_beams=3 for optimal summary generation
Core Capabilities
- Academic paper summarization with 96.67% precision
- Book content summarization with 97.18% precision
- Report generation and summarization with 92.77% precision
- Handles input sequences up to 512 tokens
Frequently Asked Questions
Q: What makes this model unique?
The model's specialization in Korean text summarization and its training on diverse high-quality datasets (academic papers, books, and reports) makes it particularly effective for Korean language content summarization. The high precision scores across different content types demonstrate its versatility.
Q: What are the recommended use cases?
The model is ideal for summarizing Korean academic papers, books, and reports. It's particularly well-suited for applications requiring high-precision summaries of formal Korean text, such as academic research tools, content curation systems, and automated report generation.
