The article defines tokens (now officially called “词元”), explains why large language models require numeric input, and details three main tokenization strategies—word‑based, character‑based, and subword—along with the sub‑methods BPE, WordPiece, and Unigram, highlighting their advantages and drawbacks.
This article explains the fundamentals, workflows, examples, and trade‑offs of three major subword tokenization algorithms—Byte Pair Encoding, WordPiece, and SentencePiece—helping practitioners choose the right method for their large language model pipelines.
This article explains how to build the input processing stage of a Transformer—including tokenization with Hugging Face tokenizers, token‑to‑embedding conversion using BERT models, custom BPE tokenizers, and positional encoding—providing complete Python code examples and test results.
This article breaks down the Kaggle toxic comment classification competition, detailing thorough data cleaning, advanced word‑vector techniques, pseudo‑labeling, BPE tokenization, diverse neural models and ensemble strategies, and shares practical insights and pitfalls from the author's nine‑month competition journey.
