ft-tokenize 0.1.7

Tokenizer for NLP

FT Tokenize

I created this small C++17 tokenizer library as a personal project to better understand how tokenization works.
It supports both word-level and BPE (Byte Pair Encoding) tokenization and provides a simple Python API via pybind11.
The library is also published on PyPI, so it can be easily installed with pip to train, save, load, encode, and decode text.

---

Features

• Word-level tokenizer
• BPE tokenizer with merge-rule learning
• Training from text files
• Vocabulary export / import
• Token ↔ ID conversions
• Thread-safe operations using std::mutex
• Python bindings via pybind11

---

Requirements

• Python 3.8+

---

Installation

You can install the package directly with:

pip install ft_tokenize

---

Python Usage Example

from ft_tokenize import TokenizerModel

# Create a tokenizer
tok = TokenizerModel()

# Train from a text file
tok.train_from_textfile(
    "data.txt", 
    vocab_size=10000, 
    user_defined_symbols=["<sos>", "<eos>"], 
    mode="BPE"
)


# Save / load model
tok.save_model("vocab.txt")
tok.load_model("vocab.txt")

# Encode / decode some sample text
ids = tok.encode_as_ids("My name is Tommaso")
tokens = tok.encode_as_tokens("My name is Tommaso")

# Print the token IDs to see how the text is split internally
print(ids)  # Example output: [5, 8, 9, 12]

# Print the actual token strings
print(tokens)  # Example output: ['My', 'name', 'is', 'Tommaso']

# Decode back from IDs and tokens to check correctness
print(tok.decode_ids(ids))        # Should return "My name is Tommaso"
print(tok.decode_tokens(tokens))  # Should return "My name is Tommaso"

# Check vocabulary details
print("Vocab size:", tok.get_token_size())        # How many tokens are in the vocabulary
print("Token for ID 10:", tok.id_to_token(10))    # Look up a token by its ID

---

Project Structure

src/
  ft_tokenizer.cpp        # pybind11 module
  tokenizer_model.hpp     # Tokenizer class definition
  tokenizer_model.cpp     # Implementation

---

How It Works

WORD mode

• Splits text by whitespace
• Builds a frequency-sorted vocabulary
• Maps unknown words to <unk>

BPE mode

• Splits words into characters
• Iteratively merges the most frequent symbol pairs
• Stores merge rules inside the vocabulary
• Encodes text greedily (longest match)

The TokenizerModel class provides the following methods:

Training

• train_from_textfile(input_file, vocab_size=10000, user_defined_symbols=[], mode="WORD") Train the tokenizer from a text file.

  • input_file: path to a text file for training
  • vocab_size: maximum number of tokens in the vocabulary
  • user_defined_symbols: list of extra tokens to include
  • mode: "WORD" or "BPE"

• train_word_level(input_file, vocab_size, user_defined_symbols) Train a word-level tokenizer. Usually called internally.

• train_bpe(input_file, vocab_size, user_defined_symbols) Train a BPE tokenizer. Usually called internally.

Saving and Loading

• save_model(model_path) Save the current vocabulary to a file.

• load_model(model_path) Load a vocabulary from a file.

Encoding

• encode_as_ids(text) → List of integers Convert a string into token IDs.

• encode_as_tokens(text) → List of strings Convert a string into token strings.

Decoding

• decode_ids(ids) → String Convert a list of token IDs back into a string.

• decode_tokens(tokens) → String Convert a list of token strings back into a string.

Utility

• token_to_id(token) → Integer Get the ID of a token. Returns the <unk> ID if the token is not found.

• id_to_token(id) → String Get the token corresponding to an ID. Returns <unk> if the ID is invalid.

• get_token_size() → Integer Returns the size of the vocabulary.

• get_vocab() → List of strings Returns the full vocabulary.