Create generative model

[david-thrower]

Based on the provided information, here's a step-by-step guide to address the issues and enhance the model:

1. Create a new branch from the specified commit:
2. Duplicate the GPT-2 based phishing classification notebook for this project.
3. Modify the final layer of the model:
   1. Replace the Dense(1) layer with a Dense(VOCAB_SIZE) layer and change the loss function to sparse_categorical_crossentropy (since you're using a softmax activation function).
4. Handle de-tokenization (without clashing with the crossentropy loss):
5. Prepare the training dataset: In progress
6. Create a function that takes a prompt and an expected response and generates a list of text sequences where each has one additional word cumulatively added from the expected response.:
   1. E.g. the data set {"prompt":"Write a haiku about integrity: ", "response":["Integrity is great\nIntegrity is nice.\nIntegrity will get you far" } will become these data and labels: data = ["Write a haiku about integrity:", "Write a haiku about integrity: Integrity", "Write a haiku about integrity: Integrity will", ... ]; labels = ["Integrity", "will", "get", ... ]
7. Create a benchmark training set: like the above.
8. Prototype a benchmark training loop.

[david-thrower]

A prototype of packaging the text samples:

import nltk
import numpy as np
import re

# Text Sample: Class for a text sample


class TextSample:
    def __init__(self, prompt: str, response: str):
        self.prompt = prompt
        self.response = response

# Example data
sample1 = TextSample(prompt="Tell me all about the capitol of France",
                     response="Paris is known as the city of love")
sample2 = TextSample(prompt="Write a haiku about life",
                     response="Life blows.\nYou go to school.\nYou go to work")
sample3 = TextSample(prompt="Write an ode to Silence:",
                     response="Silence is awesome. Silence is rare. Silence is beauty. Silence is nowhere.")
samples = [sample1, sample2, sample3]

# Empty list (may want to change to a dict for scalability) 
data = []
labels = []


def split_string(text: str) -> list:
    try:
        words = nltk.word_tokenize(text)
    except LookupError as err:
        print(f"Looks like punkt is missing: \n {err} \n "
              "Downloading punkt to try resolving this:")
        nltk.download('punkt')
        words = nltk.word_tokenize(text)

    return words


def create_data_and_labels(samples):
    for i in np.arange(len(samples)):
        sample_0 = samples[i]
        prompt_0_str = sample_0.prompt
        response_0_str = sample_0.response

        response_0_list = split_string(response_0_str)

        data_0 = []
        label_0 = []
        data_0 = prompt_0_str
        for j in np.arange(len(response_0_list)):
            if i == 0:
                label_0 = response_0_list[j]
            else:
                data_0 += f" {response_0_list[j - 1]}"
                label_0 = response_0_list[j]
            data.append(data_0)
            labels.append(label_0)

# Test case:
create_data_and_labels(samples)

for i in np.arange(len(data)):

    data_0 = data[i]
    label_0 = labels[i]

    print(f"Sample: {i}")
    print(data_0)
    print(f"Label {i}:")
    print(label_0)
    # print(type(label_0))