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Lab Workbook: Building a Minimal Viable Product (MVP) of ChatGPT

Last edited 157 days ago by System Writer

Learning Outcomes:

How to make your own Minimal Viable product, "toy" version of Chat GTP so you will understand the principles

Objective

This lab workbook is designed to help students understand the principles behind ChatGPT by guiding them through the creation of their own minimal viable product (MVP) or "toy" version.

Introduction to ChatGPT

Preparing the Dataset

Preprocessing the Text Data

Building a Simple Language Model

Training the Model

Generating Text with the Model

Evaluating the Model

Conclusion

1. Introduction to ChatGPT

In this section, provide a brief overview of ChatGPT, its applications, and its limitations. This will help students understand the context of the project they are about to undertake.

2. Preparing the Dataset

Before students can build their own MVP, they need a dataset to train their model. In this section, guide them through the process of selecting and downloading relevant text data. Some possible options are:

Project Gutenberg (https://www.gutenberg.org/)

Common Crawl (https://commoncrawl.org/)

The Brown Corpus (https://en.wikipedia.org/wiki/Brown_Corpus)

3. Preprocessing the Text Data

In this section, teach students how to preprocess the data by:

Tokenizing the text

Removing special characters and numbers

Converting text to lowercase

Creating a vocabulary of unique words

4. Building a Simple Language Model

Learning outcomes:

Examine the concept of a simple language model, such as an n-gram model.

Walk students through the process of building an n-gram model, including:

Selecting the n-gram size (e.g., bigrams or trigrams)

Calculating the probabilities of each n-gram

Creating a probability distribution for text generation

5. Training the Model

Learning outcomes:

Examine the process of training our simple language model using the preprocessed dataset.

Explain the importance of splitting the data into training and validation sets.

6. Generating Text with the Model

Learning outcomes:

How to use their trained model to generate text by:

Selecting a seed word or phrase

Generating the next word based on the probability distribution

Repeating the process to generate a sequence of words

Explain how the "temperature" parameter can be used to control the randomness and creativity of the generated text.

7. Evaluating the Model

Learning outcomes:

Discuss methods for evaluating the performance of the language model, such as perplexity or human evaluation.

Encourage students to analyze the strengths and weaknesses of their MVP and compare it to ChatGPT.

8. Conclusion

Wrap up the lab workbook by discussing the lessons learned from building a simple MVP of ChatGPT. Encourage students to think about potential improvements to their model and explore more advanced techniques, such as deep learning and transformer models.

Where and how do I host this code?

To create a minimal viable product (MVP) of ChatGPT using Python, you can use the nltk library for text preprocessing and an n-gram model for text generation. For hosting the code, you can use a Jupyter Notebook, GitHub, or an online Python IDE like Repl.it.

Here's a step-by-step guide on how to create the MVP:

Install the required libraries

pip install nltk

Import the necessary modules

import nltk

from nltk import FreqDist

from nltk.util import ngrams

from random import choices

Load and preprocess the dataset

# Load the dataset (replace this with your own dataset)

with open('your_dataset.txt', 'r', encoding='utf-8') as file:

text = file.read()

# Tokenize the text

tokens = nltk.word_tokenize(text.lower())

# Remove special characters and numbers

tokens = [token for token in tokens if token.isalpha()]

Create a bigram model

# Generate bigrams

bigrams = list(ngrams(tokens, 2))

# Calculate bigram frequencies

bigram_freq = FreqDist(bigrams)

Generate text using the bigram model

def generate_text(seed, bigram_freq, num_words=20):

current_word = seed

generated_text = [current_word]

for _ in range(num_words):

# Get the next word based on the current word

next_words = [bigram[1] for bigram in bigram_freq if bigram[0] == current_word]

next_word_freqs = [bigram_freq[bigram] for bigram in bigram_freq if bigram[0] == current_word]

if not next_words:

break

# Choose the next word using the bigram frequencies

next_word = choices(next_words, next_word_freqs)[0]

generated_text.append(next_word)

current_word = next_word

return ' '.join(generated_text)

# Generate text with a seed word

generated_text = generate_text('artificial', bigram_freq)

print(generated_text)

To host the code, you can create a Jupyter Notebook and run the code cells in order. Alternatively, you can use an online Python IDE like Repl.it (https://repl.it/) and run the code there. To share the project, you can create a GitHub repository and upload the code files or share the Repl.it link with your students.

References:

[1]

How to Develop a Natural Language Processing App⁠

⁠

[2]

The Ultimate GPT-4 Guide⁠

⁠

[3]

ChatGPT⁠

⁠

Here is a more detailed and specific guide to help a second-term student with some introductory Python knowledge build a basic language model using bigrams.

Import necessary libraries:

import re

import nltk

from nltk.tokenize import word_tokenize

from nltk.util import ngrams

from collections import Counter, defaultdict

from random import choices

Download the necessary NLTK data:

nltk.download('punkt')

Load a text dataset (use any .txt file containing a large amount of text, e.g., a book or a collection of articles):

with open('your_text_file.txt', 'r', encoding='utf-8') as f:

text = f.read()

Preprocess the text:

def preprocess_text(text):

text = text.lower()

text = re.sub(r'\d+', '', text)

text = re.sub(r'[^\w\s]', '', text)

return text

cleaned_text = preprocess_text(text)

Tokenize words and create bigrams:

tokens = word_tokenize(cleaned_text)

bigrams = list(ngrams(tokens, 2))

Count the bigram frequencies:

bigram_freq = defaultdict(int)

for bigram in bigrams:

bigram_freq[bigram] += 1

Generate text using the bigram model:

def generate_text(seed_word, bigram_freq, num_words=50):

current_word = seed_word

generated_text = [current_word]

for i in range(num_words - 1):

next_words = [bigram[1] for bigram in bigram_freq if bigram[0] == current_word]

next_word_freqs = [bigram_freq[bigram] for bigram in bigram_freq if bigram[0] == current_word]

if not next_words:

break

next_word = choices(next_words, next_word_freqs)[0]

generated_text.append(next_word)

current_word = next_word

return ' '.join(generated_text)

Test the text generation:

generated_text = generate_text('artificial', bigram_freq)

print(generated_text)

This code will build a simple bigram language model that can generate text. Although this model is not as comprehensive as ChatGPT, it provides a framework that can be extended and improved upon by students as they gain more knowledge and experience in natural language processing and machine learning.

References:

[1]

Data Preprocessing in Python⁠

⁠

[2]

Easy Guide To Data Preprocessing In Python⁠

⁠

[3]

How to Preprocess Data in Python⁠

⁠

Learning Outcomes:

Objective