Medical Text Summarization Using T5

Introduction

The T5 Large for Medical Text Summarization is a transformer model variant fine-tuned specifically for medical text summarization. It is designed to generate concise summaries of medical documents, research papers, and clinical notes, providing a valuable tool for healthcare professionals.

Architecture

The model is based on the T5 (Text-to-Text Transfer Transformer) architecture, leveraging its ability to handle various text processing tasks. It has been pre-trained on a diverse range of medical literature to effectively capture medical terminology and extract essential information.

Training

The T5 Large model is fine-tuned with a batch size of 8 and a learning rate of 2e-5 for optimal performance. It uses a dataset of medical documents and human-generated summaries to ensure accurate and coherent summarizations. The evaluation metrics include an evaluation loss of 0.0123 and a Rouge Score of 0.95 (F1).

Guide: Running Locally

To use the model for summarization, follow these steps:

1. Install Transformers Library: Ensure you have the transformers library installed.

   pip install transformers
   

2. Load the Summarization Pipeline:

   from transformers import pipeline
   summarizer = pipeline("summarization", model="Falconsai/medical_summarization")
   

3. Summarize Medical Text: Input your medical document to generate a summary.

   MEDICAL_DOCUMENT = "Your medical document text here."
   print(summarizer(MEDICAL_DOCUMENT, max_length=2000, min_length=1500, do_sample=False))
   

Cloud GPUs

For enhanced performance, consider using cloud GPU services such as AWS EC2, Google Cloud, or Azure.

License

This model is licensed under the Apache-2.0 License, allowing for both academic and commercial use with appropriate attribution.