kelliyeen/StudyBot

license: unknown

Model Card for Study Buddy AI

Model Details

Model Name: Study Buddy AI
Version: 1.0
Type: NLP - Educational Assistant
Author: [Elliyeen]
Date: [July 15th,2024]

Model Description

Purpose:
This AI model is designed to assist users in studying and learning a wide range of subjects. It provides explanations, answers questions, offers study tips, and can generate practice questions. The model aims to make studying more interactive and accessible by leveraging natural language processing to understand and respond to user queries in a helpful and educational manner. Whether the user is preparing for exams, learning a new topic, or looking for study resources, this AI serves as a versatile study companion.

Architecture:
Transformer-based architecture (e.g., GPT-3, GPT-4).

Dataset:
The model is trained on a diverse dataset comprising educational content, including textbooks, academic articles, question-answer pairs, and study guides across various subjects.

Training Details

Training Data:
Educational content from diverse sources. Preprocessing includes tokenization and normalization.

Training Environment:
Trained on a GPU with Python, TensorFlow/PyTorch, and Hugging Face's Transformers library.

Training Duration:
Approximately 12 hours on a single NVIDIA V100.

Evaluation

Evaluation Metrics:
Perplexity, BLEU score, human evaluation for accuracy and helpfulness.

Performance:
Perplexity: 12, BLEU Score: 0.30, Human Evaluation: 90% accuracy and helpfulness rate.

Validation Data:
A separate validation dataset consisting of educational content from various subjects.

Usage

Intended Use:
This model is intended to assist users in studying various subjects by providing explanations, answering questions, and offering study resources.

Limitations:
The model may not always provide the most comprehensive or accurate answers, especially for highly specialized topics. It should not replace professional educational guidance.

Ethical Considerations:
Ensure the model does not propagate misinformation or biases present in the training data. Regular updates and monitoring are recommended to maintain accuracy and relevance.

How to Use

Code Example:

from transformers import pipeline

study_buddy = pipeline('question-answering', model='YourModelName')
question = "What is the theory of relativity?"
response = study_buddy(question=question, context="Albert Einstein developed the theory of relativity, which includes the famous equation E=mc^2.")
print(response)