run_summarisation.txt

 this time focusing on Text Summarization and explicitly using the popular facebook/bart-large-cnn model.

This model is designed to take a longer piece of text and generate a shorter, abstractive summary (meaning it can use words not present in the original text, like a human would).

Prerequisites:

Ensure you have the necessary libraries. BART models typically require sentencepiece.

Bash

pip install transformers sentencepiece torch
# Or: pip install transformers sentencepiece tensorflow
(Ensure your existing virtual environment is active).

Explicit Model Choice:

We'll use facebook/bart-large-cnn, a large BART model fine-tuned on the CNN/Daily Mail news dataset, which is a standard benchmark for summarization.

How to Run:

Make sure you've run pip install transformers sentencepiece torch (or the tensorflow equivalent) in your activated virtual environment.
Save the code above into a file named run_summarization.py.
Open your Ubuntu terminal.
Make sure your virtual environment is activated (source .venv/bin/activate).
Run the script:
Bash

python run_summarization.py
What to Expect:

First Run: It will download the facebook/bart-large-cnn model files. This is another large model (over 1.5GB), so the download may take some time. It will be cached locally.
Summarization Execution: The model will process the long input text about Perth traffic.
Output: It will print a concise summary of the provided text, aiming for a length between the min_length and max_length specified. The summary should capture the main points: Friday afternoon traffic delays in Perth due to an accident, heavy volume, and road closures for an upcoming festival, with advice to allow extra travel time.