huggingface-tokenizers

name	huggingface-tokenizers
description	Fast tokenizers optimized for research and production. Rust-based implementation tokenizes 1GB in <20 seconds. Supports BPE, WordPiece, and Unigram algorithms. Train custom vocabularies, track alignments, handle padding/truncation. Integrates seamlessly with transformers. Use when you need high-performance tokenization or custom tokenizer training.
category	llm-tools
version	1.0.0
author	Synthetic Sciences
license	MIT
tags	["Tokenization","HuggingFace","BPE","WordPiece","Unigram","Fast Tokenization","Rust","Custom Tokenizer","Alignment Tracking","Production"]
dependencies	["tokenizers","transformers","datasets"]

HuggingFace Tokenizers - Fast Tokenization for NLP

Fast, production-ready tokenizers with Rust performance and Python ease-of-use.

When to use HuggingFace Tokenizers

Use HuggingFace Tokenizers when:

• Need extremely fast tokenization (<20s per GB of text)
• Training custom tokenizers from scratch
• Want alignment tracking (token → original text position)
• Building production NLP pipelines
• Need to tokenize large corpora efficiently

Performance:

• Speed: <20 seconds to tokenize 1GB on CPU
• Implementation: Rust core with Python/Node.js bindings
• Efficiency: 10-100× faster than pure Python implementations

Use alternatives instead:

• SentencePiece: Language-independent, used by T5/ALBERT
• tiktoken: OpenAI's BPE tokenizer for GPT models
• transformers AutoTokenizer: Loading pretrained only (uses this library internally)

Quick start

Installation

# Install tokenizers
pip install tokenizers

# With transformers integration
pip install tokenizers transformers

Load pretrained tokenizer

from tokenizers import Tokenizer

# Load from HuggingFace Hub
tokenizer = Tokenizer.from_pretrained("bert-base-uncased")

# Encode text
output = tokenizer.encode("Hello, how are you?")
print(output.tokens)  # ['hello', ',', 'how', 'are', 'you', '?']
print(output.ids)     # [7592, 1010, 2129, 2024, 2017, 1029]

# Decode back
text = tokenizer.decode(output.ids)
print(text)  # "hello, how are you?"

Train custom BPE tokenizer

from tokenizers import Tokenizer
from tokenizers.models  BPE
 tokenizers.trainers  BpeTrainer
 tokenizers.pre_tokenizers  Whitespace


tokenizer = Tokenizer(BPE(unk_token=))
tokenizer.pre_tokenizer = Whitespace()


trainer = BpeTrainer(
    vocab_size=,
    special_tokens=[, , , , ],
    min_frequency=
)


files = [, ]
tokenizer.train(files, trainer)


tokenizer.save()