Tod Kat

Transformer encoder-decoder for emotion detection in dialogues

AI & Machine LearningPythonMIT

Abstract

Tod Kat is an open-source AI & Machine Learning project. Transformer encoder-decoder for emotion detection in dialogues. python TodKat_dd.py for DailyDialogue. python TodKat_emory.py for EmoryNLP. It is built using Python. The complete source code is publicly available on GitHub under the MIT License, making it a useful reference for students building an AI & Machine Learning mini project or final-year project.

1. Introduction

python TodKat_dd.py for DailyDialogue. python TodKat_emory.py for EmoryNLP. python TodKat_iemocap.py for IEMOCAP. python TodKat_meld.py for MELD.

Transformer encoder-decoder for emotion detection in dialogues

Bug fix: the sklearn was used erroneously, causing the unusual high macro-F1s of MELD and EMORY. A hot fix is provided. Below lists the updated performance: MELD EMORY avg-macro-F1 micro-F1 avg-macro-F1 micro-F1 0.6547 0.6724 0.3869 0.4238

2. Objective

Transformer encoder-decoder for emotion detection in dialogues

This project demonstrates how Python can be applied to a real-world AI & Machine Learning problem.

4. Technology Stack

Python

5. System Requirements

General requirements for this technology stack — check the README for exact versions.

  • Python 3.8 or later
  • pip / virtualenv for dependencies
  • VS Code, PyCharm or Jupyter Notebook
  • Git (to clone the repository)

6. Installation & Setup

git clone https://github.com/somethingx678/TodKat.git
cd TodKat
  1. Download the pre-trained models save.zip , unzip the zip file to ./TodKat/save
  2. Locate to the ./TodKat/src and execute the following command in terminal:

Full setup instructions are in the project README.

7. Future Enhancements

Suggested extensions you can add to make this your own project.

  • Deploy the model as a web app with Streamlit, Flask or FastAPI
  • Compare against an additional model and report the metric difference
  • Add explainability (SHAP / Grad-CAM)

8. Viva / Review Questions

Common questions examiners ask for projects in this domain.

  1. What dataset does the project use and how was it pre-processed?
  2. Which algorithm / model architecture is used and why was it chosen over alternatives?
  3. How are training and testing data split, and how is overfitting avoided?
  4. Which evaluation metrics (accuracy, precision, recall, F1) are reported and what do they mean here?
  5. How would you deploy this model for real users?

9. Source Code & License

This project is developed by somethingx678 and published on GitHub under the MIT License. Please follow the license terms and credit the original author when you use or modify this code.

View Source on GitHubDownload ZIP

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