/Analyzing-Car-Reviews-with-LLMs

Primary LanguageJupyter Notebook

Analyzing Car Review with LLMs

This repository showcases practical implementations of pre-trained Large Language Models (LLMs) aimed at enhancing the operations of an auto dealership company.

Project Description

As an AI developer for an auto dealership company, your role is to leverage pre-trained LLMs to achieve the following tasks:

  1. Analyze and Classify Customer Reviews:

    • Utilize LLMs to classify the sentiment of car reviews.
    • Evaluate the classification accuracy and F1 score of predictions to gauge customer satisfaction.

  2. Respond to Inquiries About Car Models:

    • Implement LLM-based responses to provide detailed information about available car models.

  3. Translate English Text Data:

    • Translate English text data into other languages to serve a diverse customer base.
    • Assess translation quality using BLEU score metrics.

Project Structure

  • car_reviews.csv: Contains customer reviews of cars for sentiment analysis.
  • reference_translations.txt: File with reference translations for BLEU score calculation.
  • sentiment_analysis.py: Script to analyze and classify customer reviews.
  • translation_and_bleu.py: Script for translating text and calculating BLEU score.
  • qa_and_summarization.py: Script for handling QA tasks and summarizing reviews.

Installation

To run the scripts in this repository, ensure you have Python and the necessary libraries installed. You can install the required libraries using pip:

pip install transformers pandas nltk sklearn