Model Card for automated_task_prioritizer

This model is a multi-task regression model designed to predict the importance, due date, duration, and type of tasks based on their text description. It uses sentence embeddings as input and outputs predicted values for these three task characteristics.

Model Details

Model Description

This model is a multi-task regression model designed to predict the importance, due date, duration, and type of tasks based on their text description. It uses sentence embeddings as input and outputs predicted values for these three task characteristics.

• Developed by: Sam Der and Smriti Chopra
• Model type: Multi-Task MLP
• License: MIT

Uses

Direct Use

This model is intended to be used as part of an automated task prioritization system. It can help to automatically categorize and prioritize tasks based on learned patterns from a dataset of labeled tasks.

Downstream Use

This model is designed to be a component of an automated task prioritization app. It takes task descriptions as input and provides predictions for importance, due date, duration, and type. These predictions are then used by a separate function to calculate a priority score for each task, allowing the app to reorder tasks in a prioritized list for the user.

Bias, Risks, and Limitations

• The model's performance is dependent on the quality and diversity of the training data.
• The model does not account for external factors that might influence task priority beyond the text description and provided due date.

Training Details

Training Data

The model was trained on the samder03/Project1 dataset containing tasks with corresponding labels for importance (1-10), duration (hours), and due date (days). The dataset was loaded from a Google Sheet and preprocessed before training.

AI Usage

Used GenAI to design and implement the one trunk multi head neural network, choose losses and metrics, tune hyperparameters, and debug training issues.