LLM Project 1: Translating, Summarizing, and Paraphrasing (using T5)

Enjoying the LLM journey so far?

I've been introducing LLM concepts so that you can understand what some terms mean and how they appear as code outputs.  This is a reflective post about concepts covered in the previous chapters, namely tokenization, encoding, and decoding.  Don't remember what these words mean?  Have a look at the posts below:

• LLM Part 1: Tokenization
• LLM Part 2: Encoding
• LLM Part 3: Decoding

As promised in the last post, this time we'll be looking at how to actually USE T5 models to do the following tasks: translation, summarization, and paraphrasing.  

For future reference, generating answers using LLM models is called "inference."

See: Hugging Face documentation

Have a look at the Python code below!  

LLM Project 1

You'll be able to see how one can:

1. Import the model
2. Execute a query
3. See the output the model generates
4. See which tasks the model can perform best.

I'm in the mood for trying something new for a change, so here's a quiz based on the code above.  Read the following questions and comment down below!

Q1:  What was the name of the model that I used?

Q2:  What were the tasks that I had the model perform?

Q3:  Which task did the model perform best at?

Ending

Hi everyone!

Are you enjoying the series so far?  I've actually found all of this to be fun to present.  ChatGPT can be useful and entertaining but I started to enjoy them even more once I started using and training my own models.  It doesn't always work in the way that I want it to... but I guess that's part of what makes them interesting to work with.  🤣

Since it's now halfway through 2024, I'm thinking of sharing a post about neurodiversity next month.  Still considering what to write about but I'll let you know when I'm comfortable sharing it. 👊