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Byte Pair Encoding (BPE) Tokenizer is a hard coding interview question covering nlp, strings, deep learning — the kind asked at FAANG (Facebook/Meta, Apple, Amazon, Netflix, Google) and AI-first companies like Anthropic, OpenAI, and Nvidia.
Implement a simplified Byte Pair Encoding (BPE) tokenizer, the algorithm behind GPT tokenization (tiktoken).
Algorithm: 1. Initialize: split each word into characters + end-of-word marker 2. For n_merges iterations: a. Count all adjacent symbol pairs across the corpus (weighted by word frequency) b. Find the most frequent pair c. Merge that pair into a new token everywhere 3. Return the list of merge rules in order
Example: corpus={'low': 5, 'lower': 2, 'newest': 6, 'widest': 3}, n_merges=3
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