This article details the integration of Large Language Models (LLMs), specifically the “Flan T5” model, with Apache Spark for text data transformations such as sentiment analysis. It provides instructions on setting up Apache Spark and Python, installing necessary libraries, and writing code to create a Spark User-Defined Function (UDF) for sentiment analysis on a dataset. The future potential for Spark and LLMs in data and model processing is also discussed.
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DATA ENGINEERING & GENERATIVE AI: A Practical Guide
Transform Your Data: Leverage Large Language Models (LLMs) to convert unstructured data into actionable insights. Perfect for data engineers looking to enhance their toolkit with cutting-edge AI capabilities!
LLMs: Powerful Tools for Transformations
LLMs can perform complex text transformations such as extracting names, conducting sentiment analysis, masking sensitive information, translating languages, and summarizing content, thus enriching your data sets with more valuable information.
Step-by-Step Guide Using Apache Spark
Follow our guide to apply LLMs within Apache Spark – a robust data processing system – to perform sentiment analysis on your data with precision.
Set up the project
Get started with Apache Spark and Python 3.8 on your system. Install necessary libraries using pip commands:
- PySpark for Spark jobs
- Transformers library from Hugging Face to access LLMs
- Torch, urllib3 for supporting operations
Coding Time
Create a Python file and build an example Spark DataFrame for sentiment analysis using the Flan T5 Model from Hugging Face.
- Import required libraries
- Start a new Spark session
- Define and register a Spark User-Defined Function (UDF) for sentiment analysis
Apply the UDF to your data and reveal insights with clear results.
Future of Spark and LLMs
Unlock potential applications in batch and stream processing for real-time data analysis. Dive into the synergies between Spark and LLMs, where endless opportunities await.
Evolve Your Company with AI
Stay ahead of the curve by integrating AI with our Large Models Meet Big Data: Spark and LLMs in Harmony approach.
- Identify Automation Opportunities: Pinpoint customer interaction points for AI intervention.
- Define KPIs: Set clear metrics to measure AI’s business impact.
- Select an AI Solution: Opt for tools that meet your specific needs and offer customization options.
- Implement Gradually: Start small with a pilot, collect data, and scale AI implementation wisely.
For tailored AI KPI management advice, email us at hello@itinai.com. Stay informed with our latest AI insights on Telegram (t.me/itinainews) or Twitter (@itinaicom).
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