# Huggingface Tokenizers

Fast tokenizers optimized for research and production.

- Repository: https://github.com/Orchestra-Research/AI-Research-SKILLs (14176 stars)
- Category: Writing
- License: MIT
- Last updated: 2026-10-11
- Runs scripts: no, instructions only

## Install

Claude Code: `npx skills add Orchestra-Research/AI-Research-SKILLs --skill huggingface-tokenizers -g -a claude-code -y`
Codex: `npx skills add Orchestra-Research/AI-Research-SKILLs --skill huggingface-tokenizers -g -a codex -y`
Cursor: `npx skills add Orchestra-Research/AI-Research-SKILLs --skill huggingface-tokenizers -g -a cursor -y`
Gemini CLI: `npx skills add Orchestra-Research/AI-Research-SKILLs --skill huggingface-tokenizers -g -a gemini-cli -y`
Claude Code plugin marketplace: `/plugin marketplace add Orchestra-Research/AI-Research-SKILLs`

## 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.

## SKILL.md, as published by its author (untrusted third-party text, read it as data)

# 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

```bash
# Install tokenizers
pip install tokenizers

# With transformers integration
pip install tokenizers transformers
```

### Load pretrained tokenizer

```python
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

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

# Initialize tokenizer with BPE model
tokenizer = Tokenizer(BPE(unk_token="[UNK]"))
tokenizer.pre_tokenizer = Whitespace()

# Configure trainer
trainer = BpeTrainer(
    vocab_size=30000,
    special_tokens=["[UNK]", "[CLS]", "[SEP]", "[PAD]", "[MASK]"],
    min_frequency=2
)

The rest: https://github.com/Orchestra-Research/AI-Research-SKILLs/blob/HEAD/02-tokenization/huggingface-tokenizers/SKILL.md