RawanAlwadeya/t5-summarization-brevitybot

Model Card for BrevityBot: T5-Based Text Summarization Model

BrevityBot is a high-performance NLP model fine-tuned from Google’s t5-small architecture using the XSum dataset. Designed for abstractive summarization, it generates concise, high-quality summaries of long English documents and news articles. The model leverages the encoder-decoder structure of T5 and was optimized using Hugging Face’s Seq2SeqTrainer, making it well-suited for applications that require fast and accurate content summarization.

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Model Details

Key Features:

• Abstractive summarization trained on real-world, high-quality data (XSum)
• Based on google-t5/t5-small, a reliable and well-researched architecture
• Deployed in a Flutter mobile app via FastAPI backend for real-time use
• Evaluated using ROUGE metrics to ensure output quality

Skills & Technologies Used:

• Hugging Face Transformers and Datasets
• Fine-tuning with Seq2SeqTrainer
• Google Colab for training (GPU acceleration)
• FastAPI for backend API integration

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• Developed by: Rawan Alwadeya
• Model type: Sequence-to-Sequence (Encoder-Decoder)
• Language(s): English (en)
• License: MIT
• Finetuned from: google-t5/t5-small

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Uses

Used to generate short abstractive summaries from long English documents or news articles. Works well for personal productivity, education, media apps, and more.

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👩‍💻 Author

Rawan Alwadeya
AI Engineer | Generative AI Engineer | Data Scientist

• 📧 Email: [email protected]
• 🌐 LinkedIn Profile

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Example Usage

from transformers import pipeline

summarizer = pipeline("summarization", model="RawanAlwadeya/t5-summarization-brevitybot")

text = """
The British prime minister said today that the new policies will help boost economic growth over the next five years.
"""

summary = summarizer(text, max_length=64, min_length=30, do_sample=False)
print(summary[0]['summary_text'])