mt5-small-turkish-summarization
| Property | Value |
|---|---|
| Model Size | 300M parameters (1.2GB) |
| License | MIT |
| Training Dataset | MLSUM Turkish News |
| Research Paper | MLSUM Paper |
What is mt5-small-turkish-summarization?
This model is a fine-tuned version of Google's Multilingual T5-small, specifically optimized for Turkish text summarization tasks. Built on PyTorch Lightning, it has been trained on the MLSUM Turkish news dataset to generate concise, accurate summaries of longer texts. The model represents a significant advancement in Turkish language processing, combining the power of transformer architecture with specialized training for summarization tasks.
Implementation Details
The model was fine-tuned using carefully selected hyperparameters: 10 epochs, 8 batch size, and 10e-4 learning rate, with training completed in approximately 4 hours. It handles input texts up to 784 tokens and generates summaries up to 64 tokens in length.
- Trained on 20K news articles with 4K validation samples
- Built on mT5 architecture pre-trained on mC4
- Implements beam search with num_beams=2 for generation
- Uses repetition penalty of 2.5 and length penalty of 2.0
Core Capabilities
- Turkish news article summarization
- Efficient processing of long-form content
- Controllable summary generation with customizable parameters
- Support for both short and medium-length article processing
Frequently Asked Questions
Q: What makes this model unique?
This model is specifically optimized for Turkish language summarization, using a significant portion of the MLSUM dataset and incorporating carefully tuned parameters for news article processing. It bridges the gap in Turkish language processing tools while maintaining high-quality output.
Q: What are the recommended use cases?
The model is particularly well-suited for news article summarization, content condensation for media outlets, and automated headline generation for Turkish content. It performs best with well-structured news articles and can handle various journalistic writing styles.
