pn-summary-mt5-small
| Property | Value |
|---|---|
| Author | HooshvareLab |
| Model Type | Text Summarization |
| Base Architecture | mT5-small |
| Model URL | HuggingFace |
What is pn-summary-mt5-small?
pn-summary-mt5-small is a specialized text summarization model built on the mT5-small architecture, designed specifically for summarizing articles. Developed by HooshvareLab, this model demonstrates strong performance in generating concise and accurate summaries, as evidenced by its impressive ROUGE scores across multiple metrics.
Implementation Details
The model is based on Google's mT5-small architecture and has been fine-tuned on the pnSummary dataset. It shows consistent performance across both validation and test sets, with particularly strong results in ROUGE-1 scores, indicating good unigram overlap between generated and reference summaries.
- ROUGE-1 F-Measure: 43.57% (validation) and 43.40% (test)
- ROUGE-2 F-Measure: 25.63% (validation) and 25.63% (test)
- ROUGE-L F-Measure: 37.60% (validation) and 37.37% (test)
Core Capabilities
- Effective text summarization with strong precision and recall metrics
- Consistent performance across different ROUGE evaluation metrics
- Balanced performance between validation and test sets, indicating good generalization
- Multilingual capabilities through mT5 architecture
Frequently Asked Questions
Q: What makes this model unique?
The model's strength lies in its consistent performance across different evaluation metrics and its ability to maintain similar scores between validation and test sets, suggesting robust generalization capabilities. It achieves particularly strong results in ROUGE-1 scores, with F-measures above 43%.
Q: What are the recommended use cases?
This model is particularly well-suited for article summarization tasks, especially in contexts where both Persian and English language support might be needed. It's ideal for applications requiring automatic text summarization with good preservation of key information and semantic meaning.
