huggingface-tokenizers

SKILL.md preview

---
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.
version: 1.0.0
author: Orchestra Research
license: MIT
dependencies: [tokenizers, transformers, datasets]
platforms: [linux, macos, windows]
metadata:
  hermes:
    tags: [Tokenization, HuggingFace, BPE, WordPiece, Unigram, Fast Tokenization, Rust, Custom Tokenizer, Alignment Tracking, Production]

---

# 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
)

# Train on files
files = ["train.txt", "validation.txt"]
tokenizer.train(files, trainer)

# Save
tokenizer.save("my-tokenizer.json")
```

**Training time**: ~1-2 minutes for 100MB corpus, ~10-20 minutes for 1GB

### Batch encoding with padding

```python
# Enable padding
tokenizer.enable_padding(pad_id=3, pad_token="[PAD]")

# Encode batch
texts = ["Hello world", "This is a longer sentence"]
encodings = tokenizer.encode_batch(texts)

for encoding in encodings:
    print(encoding.ids)
# [101, 7592, 2088, 102, 3, 3, 3]
# [101, 2023, 2003, 1037, 2936, 6251, 102]
```

## Tokenization algorithms

### BPE (Byte-Pair Encoding)

**How it works**:
1. Start with character-level vocabulary
2. Find most frequent character pair
3. Merge into new token, add to vocabulary
4. Repeat until vocabulary size reached

**Used by**: GPT-2, GPT-3, RoBERTa, BART, DeBERTa

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

tokenizer = Tokenizer(BPE(unk_token="<|endoftext|>"))
tokenizer.pre_tokenizer = ByteLevel()

trainer = BpeTrainer(
    vocab_size=50257,
    special_tokens=["<|endoftext|>"],
    min_frequency=2
)

tokenizer.train(files=["data.txt"], trainer=trainer)
```

**Advantages**:
- Handles OOV words well (breaks 

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