| --- |
| library_name: transformers |
| pipeline_tag: summarization |
| tags: |
| - summarization |
| - t5 |
| - flan-t5 |
| language: |
| - en |
| --- |
| |
| # Text Summarizer (T5 Fine-Tuned) |
|
|
| This model is a fine-tuned version of T5 designed for abstractive text summarization. It condenses long articles, documents, or paragraphs into short, accurate, and context-aware summaries. |
|
|
| ## Model Details |
|
|
| ### Model Description |
|
|
| - **Developed by:** Kartik Sharma |
| - **Model type:** Sequence-to-Sequence (Encoder-Decoder) |
| - **Language(s):** English |
| - **Base Model:** T5 Architecture |
| - **Task:** Text Summarization |
|
|
| --- |
|
|
| ## How to Get Started with the Model |
|
|
| You can load and test the model using the Hugging Face `transformers` pipeline or direct model classes: |
|
|
| ### Using Transformers Pipeline |
|
|
| ```python |
| from transformers import pipeline |
| |
| summarizer = pipeline("summarization", model="UX4567/Text-Summarizer-25B") |
| |
| text = """ |
| Artificial Intelligence (AI) is transforming industries across the globe. From healthcare to finance, |
| machine learning models are enabling automation, improving efficiency, and driving innovation. |
| As AI technology continues to evolve, ethical considerations and proper implementation become |
| critical for sustainable integration. |
| """ |
| |
| summary = summarizer(text, max_length=60, min_length=25, do_sample=False) |
| print(summary[0]['summary_text']) |
