R Notebook

—-USING CODEX—

pip install openai

import openai  

# Set up your OpenAI API key  
openai.api_key = 'your-api-key-here'  

# Define a prompt for Codex  
prompt = "Write a Python function to calculate the factorial of a number."  

# Make a request to Codex  
response = openai.ChatCompletion.create(  
    model="code-davinci-002",  # or another Codex model  
    messages=[  
        {"role": "user", "content": prompt}  
    ],  
    max_tokens=150  
)  

# Print the generated code  
generated_code = response['choices'][0]['message']['content']  
print(generated_code)

# or

import openai  

openai.api_key = 'your-api-key'  

response = openai.Completion.create(  
  engine="code-davinci-002",  # Codex engine  
  prompt="Write a Python function to calculate the factorial of a number.",  
  max_tokens=100,  
  temperature=0.5  
)  

print(response.choices[0].text.strip())

Set Up Environment

Using the OpenAI API: 1. Get Key

pip install openai

GitHub Copilot Setup: 1. I can install GitHub Copilot directly in Visual Studio Code. 2. After logging in with my GitHub account, I’ll configure Copilot to assist as I code.

Making API Calls to Codex

Here’s how I can interact with Codex through API calls:

import openai

# Set up OpenAI API key
openai.api_key = 'your-api-key'

# Define my prompt for Codex
prompt = "Write a Python function to calculate the factorial of a number."

# Make a request to Codex
response = openai.Completion.create(
    engine="code-davinci-002",  # Codex engine
    prompt=prompt,
    max_tokens=100,
    temperature=0.5
)

# Print the generated code
print(response.choices[0].text.strip())

Key Parameters: - Prompt: Describes the task or question I have. - Max Tokens: Limits the response length. - Temperature: Controls output randomness (lower values yield more deterministic code).

Using GitHub Copilot in My IDE

With Copilot in Visual Studio Code: 1. I start typing code or add comments, and Copilot suggests completions. 2. To accept, I press Tab. I can also explore alternatives with Ctrl + ]. 3. Writing clear comments in natural language helps Copilot understand what I’m trying to achieve.

Experimenting with Prompts

Prompt engineering can help me get the most from Codex. I can ask Codex to: - Generate entire functions or classes. - Explain code snippets. - Debug existing code by providing some context.

Fine-Tuning for Specialized Tasks

If needed, I can fine-tune Codex on specific data (when accessible) for more tailored code generation.

Best Practices

• Iterate on Prompts: Rephrasing prompts or adding context improves Codex’s output.
• Review and Test: I always review generated code to ensure it’s correct and secure.
• Use in Combination: Codex can supplement my coding but isn’t a replacement; I’ll combine it with my own expertise.

–Examine code from codex using dev tools and reverse engineering to see how mechanism works

–Initial Steps to create a bert “Jbert” that has accuracy of bert with Casual Lnaguage Modeling and Text Generating (but better) as a Gpt.

To build my own BERT model, I’ll follow these steps:

1. Defining My Objectives

• Purpose: I’ll determine the specific task I want my BERT model to perform (e.g., text classification, named entity recognition, question answering).
• Dataset: I’ll select the appropriate dataset for training and evaluating my model.

2. Data Collection and Preprocessing

• Dataset: I’ll gather a large text corpus relevant to my chosen task. Potential sources include Wikipedia, BookCorpus, or a domain-specific dataset.
• Preprocessing:
  • Tokenization: I’ll use a BERT-compatible tokenizer, likely from the Hugging Face Transformers library, to break down the text into tokens.
  • Input Formatting: I’ll format the input data as required by BERT, including Input IDs, Attention Masks, and Token Type IDs.

3. Choosing a Framework

• Libraries: I’ll use either Hugging Face Transformers or TensorFlow to implement BERT, leaning towards Hugging Face for its ease of use with pre-trained models.

4. Model Architecture

• Pre-trained BERT: I’ll likely start with a pre-trained BERT model and fine-tune it for my task to save time and resources.
• Loading Pre-trained Model: I’ll use code like this to load the pre-trained model and tokenizer:

from transformers import BertTokenizer, BertForSequenceClassification
import torch

# Load pre-trained BERT tokenizer and model
tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
model = BertForSequenceClassification.from_pretrained('bert-base-uncased', num_labels=2)  # I'll adjust num_labels as needed

5. Training the Model

• Training Loop: I’ll set up a training loop to fine-tune the model using my prepared dataset. This will involve selecting an optimizer (e.g., AdamW) and a loss function (e.g., CrossEntropyLoss).
• Example Training Code: I’ll adapt the following code:

from transformers import Trainer, TrainingArguments

training_args = TrainingArguments(
    output_dir='./results',
    num_train_epochs=3,
    per_device_train_batch_size=16,
    per_device_eval_batch_size=64,
    warmup_steps=500,
    weight_decay=0.01,
    logging_dir='./logs',
)

trainer = Trainer(
    model=model,
    args=training_args,
    train_dataset=train_dataset,  # My training dataset
    eval_dataset=eval_dataset,     # My evaluation dataset
)

trainer.train()

6. Evaluation

• Metrics: I’ll choose appropriate metrics (e.g., accuracy, F1 score) to evaluate the model’s performance on my task.
• Testing: I’ll test the model on a held-out test set to get an unbiased assessment of its performance.

7. Deployment

• Model Saving: I’ll save the trained model and tokenizer for later use:

model.save_pretrained('./my_bert_model')
tokenizer.save_pretrained('./my_bert_model')

• Inference: I’ll load the saved model to make predictions on new, unseen data.

8. Continuous Improvement

• Fine-tuning: I’ll continue to fine-tune the model with more data or adjust hyperparameters as needed to improve performance.
• Feedback Loop: I’ll incorporate user feedback to iteratively refine the model.

Use pre-trained models and the Hugging Face Transformers library to help streamline. See my doc on Hugging Face on my io wesbite.

JTML