Aml Text Summarization T5

🚀 AML Text Summarization T5 Model

This is a text summarization model based on the T5-Small architecture, developed for the Advanced Machine Learning course at the University of Bremen, aiming to provide efficient text summarization solutions.

🚀 Quick Start

This model is a text summarization solution based on the T5-Small architecture. It's developed as part of the Advanced Machine Learning course at the University of Bremen.

✨ Features

• Fine - Tuned for Summarization: This model is fine - tuned on the CNN/Daily Mail dataset for abstractive text summarization.
• T5 - Small Architecture: It uses the T5 - Small (Text - To - Text Transfer Transformer) architecture.

📦 Installation

No specific installation steps are provided in the original document, so this section is skipped.

💻 Usage Examples

Basic Usage

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM

tokenizer = AutoTokenizer.from_pretrained("s0urin/aml-text-summarization-t5")
model = AutoModelForSeq2SeqLM.from_pretrained("s0urin/aml-text-summarization-t5")

text = "Your long text here..."
inputs = tokenizer("summarize: " + text, return_tensors="pt", max_length=512, truncation=True)
outputs = model.generate(inputs.input_ids, max_length=150, min_length=40, length_penalty=2.0, num_beams=4, early_stopping=True)
summary = tokenizer.decode(outputs, skip_special_tokens=True)

print(summary)

📚 Documentation

Model Information

🔧 Technical Details

No specific technical details are provided in the original document, so this section is skipped.

📄 License

No license information is provided in the original document, so this section is skipped.

👥 Authors

• Sourin Kumar Pal
• Jassim Hameed Ayobkhan