Kaggle 'Competitions': LLM Classification Finetuning

Finetune LLMs to Predict Human Preference using Chatbot Arena conversations

This project contains a solution for the LLM Classification Finetuning competition on Kaggle - kaggle notebook

Main objective: Predict which responses users will prefer in a head-to-head battle between chatbots powered by large language models(LLMs).

Steps of the data science process:

1. Understand the original data
2. Data Cleaning
3. Data Exploration
4. Feature Engineering
5. Model Selection and Training
6. Model Evaluation
7. Submission File

Data

• train.csv id - A unique identifier for the row. model_a/b - The identity of model_a/b. Included in train.csv but not test.csv. prompt - The prompt that was given as an input (to both models). response_a/b - The response from model_a/b to the given prompt. winner_model_a/b/tie - Binary columns marking the judge's selection. The ground truth target column.

• test.csv id prompt response_a/b

• sample_submission.csv A submission file in the correct format. id winner_model_a/b/tie - This is what is predicted from the test set. add Codeadd Markdown

What's in this project?

README.md: That's this file, where you can describe your project and how you built it.

llm-classification-finetuning.ipynb: That's python notebook with the code submitted to the competition.