text_summarization by Falconsai
| Property | Value |
|---|---|
| Parameter Count | 60.5M |
| Tensor Type | F32 |
| License | Apache 2.0 |
| Downloads | 42,349 |
What is text_summarization?
text_summarization is a fine-tuned variant of the T5-small model specifically optimized for generating concise and coherent summaries of input text. Built by Falconsai, this model has been carefully trained with a batch size of 8 and learning rate of 2e-5 to ensure optimal performance in text summarization tasks.
Implementation Details
The model leverages the T5 architecture and has been fine-tuned on a diverse dataset of documents with human-generated summaries. It uses F32 precision and implements the transformers pipeline for easy integration.
- Built on T5-small architecture
- Optimized hyperparameters (batch size: 8, learning rate: 2e-5)
- Evaluation Rouge Score: 0.95 (F1)
- Supports variable length summaries
Core Capabilities
- Generate concise text summaries
- Handle long-form content effectively
- Maintain coherence and fluency in output
- Support for custom length constraints
Frequently Asked Questions
Q: What makes this model unique?
This model stands out for its optimized performance in text summarization, achieving a high Rouge Score of 0.95 F1. It's been specifically fine-tuned for summarization tasks with carefully selected hyperparameters.
Q: What are the recommended use cases?
The model is ideal for document summarization, news article condensation, and content summarization tasks. It's particularly well-suited for applications requiring automated summary generation while maintaining content accuracy.
