This article discusses the performance of the Llama-2 AI model in analyzing a dataset and suggesting a database schema. Llama-2 successfully identifies categorical and confidential columns in the dataset and suggests a database schema with separate tables for different categories. It also provides SQL scripts to create the tables and suggests data quality checks for each column. While Llama-2 performs well overall, there are a few areas where improvement is needed.
In my previous article, I compared GPT-4 and Bard models. Now, we have a new model called Llama-2, and I will analyze how it performs against its competitors. The dataset we will be using is a fake AI-generated dataset containing information about employees.
To analyze the dataset and provide insights, we will ask Llama-2 to perform the following tasks:
1. Identify the categorical columns and confidential columns in the dataset.
2. Suggest a database schema with different tables for the data.
3. Provide SQL scripts to create the tables and their content.
4. Suggest data quality checks for each column of each table.
To test Llama-2, there are several options available:
1. Host the model on a dedicated server in your cloud architecture. This is a good option for heavy-duty applications but can be complex and expensive.
2. Use platforms like Anyscale, which offer a more affordable option for testing the model.
3. Use the Hugging Face platform, which provides a testbed for the model.
After running the benchmark on Llama-2, here are the results:
Categorical columns in the dataset are: Department, Country, Location, and Education.
Confidential columns in the dataset are: Employee_ID, Salary, and Annual_Performance.
Based on the dataset, Llama-2 suggests the following database schema:
– Employee table: Employee_ID, First_Name, Last_Name, Email, Department, Salary, Annual_Performance
– Department table: Department_ID, Department_Name
– Country table: Country_ID, Country_Name
– Location table: Location_ID, Location_Name
– Education table: Education_ID, Education_Name
Llama-2 analyzes the dataset well and properly converts the categorical columns into sub-tables. However, it does not separate the confidential data into a separate table, but still provides a near result.
To create the tables in a SQL database, here are the SQL scripts:
– Employee table
– Department table
– Country table
– Location table
– Education table
For data quality checks, Llama-2 suggests checking for duplicates and invalid characters in most columns. It also suggests checking for valid email addresses and invalid values in the salary and annual_performance columns. However, it does not consider the possibility of duplicate names in the First_Name and Last_Name columns.
Overall, Llama-2 performs well in identifying categorical and confidential data, suggesting a database model, and providing SQL scripts to create tables. However, there are some areas where it can improve its data quality checks.
In conclusion, Llama-2 has shown better performance than Bard in the task of database retro-engineering. The insights provided by Llama-2 are valuable and useful for analyzing datasets.
Action items from the meeting notes are as follows:
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