Tokenizers convert raw text into numerical tokens that models can process — a critical step in every NLP pipeline.

How Tokenization Works

from transformers import AutoTokenizer

tokenizer = AutoTokenizer.from_pretrained("bert-base-uncased")

tokens = tokenizer("Hello, how are you?")
print(tokens['input_ids'])   # [101, 7592, 1010, 2129, 2024, 2017, 1029, 102]
print(tokenizer.decode(tokens['input_ids']))  # [CLS] hello, how are you? [SEP]

Why Tokenization Matters

• Different models use different tokenizers — always use the matching one
• Tokenization determines context window usage
• Subword tokenization handles unknown words gracefully
• Truncation and padding for batch processing

Reference:

Tokenizers documentation

https://huggingface.co/docs/transformers/tokenizer_summary