Build an LLM from Scratch 2: Working with text data

2025-03-02 · Source: Sebastian Raschka · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Software Development & Engineering, Data Science & Analytics · Depth: Intermediate, extended

Summary

This content details the initial stages of preparing text data for training a large language model (LLM), focusing on tokenization, token ID conversion, and embedding. It begins by downloading a public domain short story, "The Verdict" by Edith Wharton, as a raw text dataset. The process involves tokenizing this text, initially using regular expressions for simple word and punctuation separation, then advancing to Byte Pair Encoding (BPE) via OpenAI's `tiktoken` library, which handles unknown words by breaking them into subwords or individual characters. The tokenized text is then converted into unique integer token IDs by building a vocabulary. Finally, these token IDs are transformed into numerical embedding vectors using an embedding layer, with an additional positional embedding layer introduced to provide sequential context, culminating in the complete input pipeline for an LLM.

Key takeaway

For AI Engineers building LLMs, understanding the data preparation pipeline is critical. You should prioritize robust tokenization methods like Byte Pair Encoding to handle diverse text, and implement both token and positional embeddings to provide comprehensive input to your model. Efficient data loading with PyTorch `DataLoader` will streamline training, ensuring stable loss and optimal resource utilization.

Key insights

Text data preparation for LLMs involves tokenization, converting tokens to IDs, and generating contextual embeddings.

Principles

• Tokenization breaks text into manageable sub-units.
• Positional embeddings add crucial sequence context.
• BPE handles unknown words robustly.

Method

The method involves downloading raw text, tokenizing it with BPE, converting tokens to unique integer IDs via a vocabulary, and then transforming these IDs into combined token and positional embedding vectors for LLM input.

In practice

• Use `tiktoken` for efficient BPE tokenization.
• Employ PyTorch `DataLoader` for efficient batching.
• Set `drop_last=True` in `DataLoader` to prevent loss spikes.

Topics

• Text Data Preparation
• LLM Training Data
• Tokenization
• Byte Pair Encoding
• Token Embeddings

Best for: AI Engineer, Machine Learning Engineer, AI Student

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Editorial summary, takeaway, and curation by AIssential. Original article published by Sebastian Raschka.