I Coded My Own Language Model From Scratch

Introduction

In this tutorial, we will explore how to create a language model from scratch, inspired by the video "I Coded My Own Language Model From Scratch" by 8AAFFF. The focus will be on understanding the encoding of language into numbers using word2vec, the unique architecture of the model called REAN, and the training and testing processes involved. This guide is designed for those interested in AI and natural language processing.

Step 1: Understanding word2vec

• What is word2vec?

  • Word2vec is a technique used to convert words into numerical vectors, allowing machines to understand language in a mathematical form.

• Steps to create a word2vec model:

  1. Collect text data: Gather a large dataset of text from which to learn word associations.
  2. Preprocess the data: Clean the text by removing punctuation, lowering the case, and tokenizing the words.
  3. Choose a word2vec model type:
     • Continuous Bag of Words (CBOW): Predicts a word based on its context.
     • Skip-Gram: Predicts context words based on a target word.
  4. Train the model: Use libraries like Gensim in Python to create the word2vec model.

     from gensim.models import Word2Vec
     model = Word2Vec(sentences, vector_size=100, window=5, min_count=1, workers=4)
     

Step 2: Analyzing Language Model Architectures

• Comparison of GPT and REAN:

  • GPT (Generative Pre-trained Transformer) uses a transformer architecture to generate text based on pre-existing data.
  • REAN, the model discussed in this tutorial, is a new architecture designed to improve upon traditional methods.

• Considerations for model design:

  • Think about the structure of the model, the type of data it will process, and how it will learn from that data.

Step 3: Exploring REAN Architecture

• What is REAN?

  • REAN stands for a unique architecture that the creator developed, differing from standard architectures like GPT.

• Key features of REAN:

  • Innovative handling of language encoding.
  • Optimized training processes to improve performance and efficiency.

Step 4: Testing word2vec

• Evaluate the performance of the word2vec model:
  • Use the model to find word similarities and analogies.
  • Check the quality of word embeddings by visualizing them with tools like t-SNE.

Step 5: Assembling the Components

• Integrate word2vec with REAN:
  • Combine the word vectors generated by word2vec into the REAN architecture.
  • Ensure that the architecture can effectively utilize these vectors for training.

Step 6: Training the Model

• Steps for training REAN:
  1. Prepare training data: Format your input data to match the model requirements.
  2. Set training parameters: Define batch size, learning rate, and epochs.
  3. Train the model: Use a framework like TensorFlow or PyTorch to run the training process.

     model.fit(training_data, epochs=10, batch_size=32)
     

Step 7: Testing and Interacting with the Model

• Testing the model's capabilities:
  • Run interactive sessions to test how well REAN can generate text or respond to prompts.
  • Adjust parameters based on the model's performance to improve outcomes.

Conclusion

In this tutorial, we delved into the process of coding a language model from scratch, covering key aspects such as word2vec, the innovative REAN architecture, and the training and testing of the model. By following these steps, you can begin your journey in creating and experimenting with your own language models. Consider exploring further resources on natural language processing and machine learning to enhance your understanding and skills.