Gideon: A GPT-based Model for Physics, Electronics, and Computer Science

Overview

Gideon is a Generative Pre-trained Transformer (GPT) model trained on a dataset encompassing Physics, Electronics, and Computer Science. This model aims to generate text and assist users in tasks related to these domains.

Dependencies

TensorFlow
Transformers

Installation

To install the dependencies, use the following commands:

pip install tensorflow
pip install transformers

Usage Data Collection and Preprocessing: Gather a diverse dataset of text related to Physics, Electronics, and Computer Science. Preprocess the data by cleaning it, removing noise, and tokenizing the text. Model Training: Train the Gideon model using the preprocessed dataset. Define model architecture, training parameters, and optimizer. Evaluation: Evaluate the performance of the trained model using appropriate metrics. Qualitative assessment by inspecting generated text samples. Fine-tuning: Fine-tune the model on specific tasks or datasets within each domain for improved performance. Deployment: Deploy the trained model in applications related to Physics, Electronics, and Computer Science. Example Code Below is an example of how to use Gideon to generate text:

python Copy code import tensorflow as tf from transformers import GPT2Tokenizer, TFGPT2LMHeadModel

Load pre-trained GPT2 tokenizer and model

tokenizer = GPT2Tokenizer.from_pretrained("gideon_tokenizer") model = TFGPT2LMHeadModel.from_pretrained("gideon_model", pad_token_id=tokenizer.eos_token_id)

Example usage: Generate text

prompt = "The laws of physics"
input_ids = tokenizer(prompt, return_tensors="tf")["input_ids"]
output_ids = model.generate(input_ids, max_length=100, num_return_sequences=1)
generated_text = tokenizer.decode(output_ids[0], skip_special_tokens=True)

print("Generated text:", generated_text)

License

This project is licensed under the MIT License. See the LICENSE file for details.

Acknowledgements

This project was inspired by Gideon from "The Flash" series (2014). Special thanks to the TensorFlow and Hugging Face teams for their contributions to the field of natural language processing.

anesu398 / Gideon