https://github.com/animesh-chourey/businessanalysis_genai

Fine Tuning GPT-3.5 and retrain new LLM to extract important information from customer complaints
https://github.com/animesh-chourey/businessanalysis_genai

api gpt-3 json large-language-models machine-learning matplotlib openai pandas python

Last synced: 3 months ago
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Fine Tuning GPT-3.5 and retrain new LLM to extract important information from customer complaints

• Host: GitHub
• URL: https://github.com/animesh-chourey/businessanalysis_genai
• Owner: Animesh-Chourey
• Created: 2024-08-04T11:32:34.000Z (10 months ago)
• Default Branch: master
• Last Pushed: 2024-08-05T11:01:00.000Z (10 months ago)
• Last Synced: 2025-01-11T19:45:32.650Z (4 months ago)
• Topics: api, gpt-3, json, large-language-models, machine-learning, matplotlib, openai, pandas, python
• Language: Jupyter Notebook
• Homepage:
• Size: 80.1 KB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# Business Analysis Using Generative AI with Fine-Tuning LLM

The objective of this project is to fine-tune GPT-3.5 using OpenAI API and retrain the new LLM on customer complaints data which has the ability to retrieve the required data in the needed format.

## Description

The end goal is to improve customer satisfaction by examining customer complaints, and fix the issues. For that we will need a LLM that is efficient to extract certain needed details, mainly, topic of the problem, the problem itself, and the dissatisfaction-level of the customer, ranging from 0-100 denoting the level of irritability. 

### Steps:

* Install the environment dependencies and the module and libraries. Ensure to store the API key and ORG ID in a .env file.

* Convert each row of the data in the following format to be able to use it for fine-tuning purpose.

![](JSON_format.png)

* Fine-tuning the model (GPT-3.5) by training it on the data:

    * Importing the training data and creating the fine-tuning job.

    * Train the new fine-tuned model created in the step above.

    * Evaluate the results and adjust accordingly. Here we can adjust the hyperparameters, i.e. number of epochs, batch size, learning rate.

    * Employ the fine-tuned model.

* While evaluating the performance of the fine-tuned model, focus on the two aspects of the training:

    * Training loss (should be decreasing with each step taken).

    * Training mean accuracy (should be increasing with each step taken).

* Deploy the model and observe how it is performing on some test statements.