https://github.com/saadhaniftaj/ai-essayscore-automated-essay-scoring-using-lstm
AI-EssayScore is an automated essay scoring system using LSTM neural networks. It tokenizes and pads essays, processes them through an LSTM model, and predicts scores. The project includes data preprocessing, model training, evaluation, and saving the model for future use.
https://github.com/saadhaniftaj/ai-essayscore-automated-essay-scoring-using-lstm
automated-machine-learning evaluation-metrics intro-to-ai lstm preprocessing-data
Last synced: 3 months ago
JSON representation
AI-EssayScore is an automated essay scoring system using LSTM neural networks. It tokenizes and pads essays, processes them through an LSTM model, and predicts scores. The project includes data preprocessing, model training, evaluation, and saving the model for future use.
- Host: GitHub
- URL: https://github.com/saadhaniftaj/ai-essayscore-automated-essay-scoring-using-lstm
- Owner: saadhaniftaj
- Created: 2024-10-20T22:10:58.000Z (8 months ago)
- Default Branch: main
- Last Pushed: 2024-11-08T21:25:17.000Z (8 months ago)
- Last Synced: 2025-03-09T18:30:00.666Z (4 months ago)
- Topics: automated-machine-learning, evaluation-metrics, intro-to-ai, lstm, preprocessing-data
- Language: Jupyter Notebook
- Homepage:
- Size: 8.8 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# Automated Essay Scoring (AES) System
This project implements an **Automated Essay Scoring (AES) system** using machine learning techniques, specifically LSTM layers. The goal is to develop a model that can evaluate essays based on predefined criteria and produce a score that approximates human scoring. This project serves as a foundation for assessing textual content, a useful application in educational technology and online learning platforms.
## Project Overview
Automated Essay Scoring (AES) is a fast-growing area in natural language processing and machine learning, aiming to assist educators by providing a scalable, objective, and rapid assessment solution for written content. This project applies a Long Short-Term Memory (LSTM) model, an effective model for handling sequential data like text, to predict essay scores.
### Key Features
- **Text Preprocessing**: Includes cleaning, tokenization, and vectorization of essays for effective modeling.
- **LSTM Model Architecture**: Designed to handle sequential data with recurrent layers to capture the contextual meaning.
- **Evaluation Metrics**: The model performance is evaluated based on mean squared error (MSE) to quantify the difference between predicted and actual scores.
## Project Structure
- `main.ipynb`: Jupyter notebook containing code for data processing, model building, training, and evaluation.
- `data/`: Folder to store datasets used for training and testing the AES model.
- `models/`: Directory for saving trained model files.
- `README.md`: Project description and setup instructions.
## Installation
1. **Clone the Repository**
```bash
git clone https://github.com/yourusername/automated-essay-scoring.git
cd automated-essay-scoring