Tokenization is the critical first step in natural language processing where text is broken down into smaller units called tokens. These tokens can be characters, words, or sub-words, and they form the basic building blocks that Large Language Models (LLMs) use to understand and generate language.

A tokenizer serves two essential functions:

• Converting raw text into a sequence of tokens, then into numerical token IDs.
  
• Converting token IDs back into tokens and reconstructed text.
  

The vocabulary of a tokenizer is a comprehensive mapping between tokens and their unique numeric IDs. For example:

• Tokens: "large", "language", "model", ...
  
• Token IDs: 123, 456, 789, ...
  

When a tokenizer encounters a word not in its vocabulary, it employs subword tokenization strategies. This approach allows LLMs to handle a virtually unlimited range of words (by combining subword units).
For example (exact splits depend on the tokenizer):

• "tokenizers" → ["token", "izers"]
  
• "transformers" → ["transform", "ers"]
  

Tokenization Challenges:

• Capitalization: "Hello" vs "hello" (may be treated as different tokens).
  
• Numbers: Efficient representation of numerical values.
  
• Multiple languages: Support for cross-lingual text.
  
• Emojis and Unicode: Proper handling of non-ASCII characters.
  
• Whitespace: Significant in many tokenization schemes.
  
• Programming code: Special handling for code syntax.
  
• Domain-specific terminology: Medical, legal, or scientific jargon.
  

Types of Tokens:

• Complete words: Common words that have their own token (e.g., "hello", "world").
  
• Subwords: Parts of words (e.g., "un", "expect", "ed").
  
• Characters: Individual characters for rare or complex words.
  
• Punctuation: Commas, periods, etc. typically have dedicated tokens.
  
• Special tokens: System tokens with specific functions in the model.
  
• Whitespace tokens: Some tokenizers prefix tokens with a space character to mark word boundaries.
  

Tokenizer Parameters:

• Vocabulary size: Typically between 10000-100000 tokens.
  
• Maximum sequence length: The context window size (2K, 4K, 8K, 32K, etc.).
  
• Special tokens: Model-specific control tokens.
  
• Tokenization algorithm: BPE, WordPiece, SentencePiece, Unigram, etc.
  
• Pre-tokenization rules: How raw text is initially segmented.
  

Special Tokens:

• Beginning of text [BOS]: Signals the start of input.
  
• End of text [EOS]: Signals completion of generation.
  
• Padding [PAD]: Fills sequences to uniform length.
  
• Unknown [UNK]: Placeholder for tokens outside vocabulary.
  
• Classification [CLS]: Used for sentence-level classification tasks.
  
• Masking [MASK]: Used in masked language modeling (BERT-style).
  
• Separator [SEP]: Separates distinct text segments.
  

Let's explore a practical implementation of tokenization using the Hugging Face Transformers library:

$ vi tokenizer.py

from transformers import AutoModelForCausalLM, AutoTokenizer

# load a pre-trained model and its tokenizer
model = AutoModelForCausalLM.from_pretrained("microsoft/DialoGPT-small")
tokenizer = AutoTokenizer.from_pretrained("microsoft/DialoGPT-small")

# encode text into token IDs
input_ids = tokenizer.encode("Hello Tokenizers!", return_tensors='pt')

# display the token IDs
print(input_ids) # tensor([[15496, 29130, 11341,     0]])

# display individual tokens by converting IDs back to text
for id in input_ids[0]:
  # convert the input token ID to its corresponding token
  print(tokenizer.decode(id)) # [Hello] [ Token] [izers] [!]

# generate text from the model based on the input
output = model.generate(
  input_ids=input_ids,
  max_new_tokens=50
)

# display the output token IDs
print(output) # tensor([[15496, 29130, 11341,     0, 50256]])

# display individual output tokens
for id in output[0]:
  # convert the output token id to it corresponding token
  print(tokenizer.decode(id)) # [Hello] [ Token] [izers] [!] [<|endoftext|>]

# convert all output token IDs to their corresponding text
print(tokenizer.decode(output[0])) # Hello Tokenizers!<|endoftext|>

Run the Python script:

$ python3 tokenizer.py

Output:

# input tokens ids
tensor([[15496, 29130, 11341,     0]])

# input tokens
Hello
 Token
izers
!

# output tokens ids
tensor([[15496, 29130, 11341,     0, 50256]])

# output tokens
Hello
 Token
izers
!
<|endoftext|>

# output
Hello Tokenizers!<|endoftext|>