Mastering Dataset Preparation: Techniques and Best Practices

In this fourth lab, we'll focus on dataset preparation for Downstream NLP tasks. We'll explore various techniques programmatically in Python, using libraries like PyTorch Transformers, pandas, NumPy, and Matplotlib.
The dataset we'll work with consists of LinkedIn influencer posts collected in 2021, containing metadata such as the influencer's name, number of followers, timespan, content, media type, and more. After loading the dataset into the S3 bucket, we'll examine its contents, including the number of examples and influencers.
Next, we'll sample a subset of the dataset and begin cleaning it. We'll remove profanity using a threshold approach and conduct quality checks based on the Flesh-Kincaid Grade Level. Additionally, we'll write custom functions to handle whitespace, maximum length, and column selection.
After cleaning the dataset, we'll further refine it by selecting the top-performing posts based on reactions. With the cleaned dataset in hand, we'll utilize H2O GPT to generate titles for the influencer content, employing zero-shot prompting.
For fine-tuning, we'll create instructions for H2O GPT and run it over the entire dataset. Alternatively, we'll explore LLM Data Studio, a tool specifically designed for LLM-based tasks. This tool streamlines the data preparation process by automatically converting files into question-answer pairs and providing options for cleaning, augmenting, and quality checking.
Your homework for this lab is to upload your own documents to Data Studio, experiment with different settings, and observe the outputs. Understanding the nuances of data preparation for LLMs is essential for effectively utilizing these models. Once you've completed this task, we'll move on to the final lab, where we'll learn how to evaluate LLMs.
Here's how to access LLM DataStudio for training purposes:
1. Visit our Aquarium platform at aquarium.h2o.ai.
2. Watch the following video to learn how to create an account on Aquarium: Accessing h2o.ai Aquarium Labs.
3. After you've gained access to Aquarium, navigate to the LLM Data Studio Lab.
4. Start an instance to access the user interface through the LLM Data Studio URL link at the page's bottom.
The instance will be available for you to use for 120 minutes, at the end of which all its data will be erased. Enjoy your training session with LLM Data Studio!
Please be aware that the h2oGPT exercise featured in the current video (found in the One Step Further section of LAB 4 accompanying this notebook) is solely for demonstration purposes. The endpoint used in the demonstration will not function for you.
You can access the influencers_data.csv file at the following link: LinkedIn Influencers' Data
The Link for the Python LAB 4 can be found here: LAB 4 - Data Preparation.ipynb
To access h2oGPT for learning purposes, visit our h2oGPT platform using the link provided: gpt.h2o.ai.
You'll have open access using the credentials:
username: guest
password: guest