Project Title: PubMed Summarization using BART Overview This project aims to demonstrate text summarization techniques using two different models:

BART (Bidirectional and Auto-Regressive Transformers): A transformer model specifically designed for sequence-to-sequence tasks, including text summarization.

BART Model:

Load the facebook/bart-large-cnn model and tokenizer from Hugging Face's Transformers library. Define a tokenization function for BART to prepare inputs and labels suitable for sequence-to-sequence tasks.

Streamlit application: Simply run this cell -> ! pip install streamlit -q !wget -q -O - ipv4.icanhazip.com This will return you some type of IP copy it After running this cell -> !streamlit run app.py & npx localtunnel --port 8501 You have to type 'y' and press enter in the box then wait for a while a url will pop click on it(If it say's 404 not found on the new tab simply go back to the code and click on the link again) there in the tunnel password place the IP that you copied and press submit you will be reached to a the application and then use it