tokenizers - Accurate and Efficient Tokenization for Cutting-Edge NLP Solutions

Introduction to the Tokenizers Project

The Tokenizers project, spearheaded by Hugging Face, is a robust implementation focused on tokenizing text efficiently and effectively. This open-source project is an essential tool for NLP (Natural Language Processing) and machine learning enthusiasts who require fast and flexible text processing.

Key Features

The Tokenizers project offers a suite of features making it a top choice for developers and researchers:

Vocabulary Training and Tokenization: It allows users to train new vocabularies and handle tokenization using today's most advanced tokenizers.
Speed and Performance: The implementation in Rust ensures exceptional speed, with the ability to tokenize a gigabyte of text in under 20 seconds on a standard server CPU.
Ease of Use and Versatility: While it's user-friendly, Tokenizers also provides flexibility, catering to both research and production needs.
Alignment Tracking: A standout feature is its normalization process, which tracks alignments, enabling users to trace which part of the original text matches a specific token.
Comprehensive Pre-processing: Tokenizers handle all pre-processing tasks, including truncation, padding, and adding special tokens needed by various machine learning models.

Performance Overview

The performance of the Tokenizers can vary depending on the hardware. However, performance benchmarks on a g6 AWS instance demonstrate its efficiency and effectiveness.

Language Bindings

Tokenizers support multiple languages, ensuring broad accessibility:

Rust: The original implementation is in Rust, providing a backend for other bindings.
Python: Thanks to the Python bindings, it's widely used in machine learning and data science communities.
Node.js and Ruby: These bindings extend its utility, catering to various development environments. Ruby integration is notably contributed by @ankane via an external repository.

Quick Start with Python

For those eager to dive in, here's a quick example using Python:

Choose and Instantiate a Model:

You can opt for models like Byte-Pair Encoding, WordPiece, or Unigram for your tokenization task:
```
from tokenizers import Tokenizer
from tokenizers.models import BPE

tokenizer = Tokenizer(BPE())
```
Customize Pre-tokenization:

Customize your tokenizer, for instance, by using whitespace for splitting:
```
from tokenizers.pre_tokenizers import Whitespace

tokenizer.pre_tokenizer = Whitespace()
```

Train the Tokenizer:

Training is streamlined and can be accomplished in just a few lines:

from tokenizers.trainers import BpeTrainer

trainer = BpeTrainer(special_tokens=["[UNK]", "[CLS]", "[SEP]", "[PAD]", "[MASK]"])
tokenizer.train(files=["wiki.train.raw", "wiki.valid.raw", "wiki.test.raw"], trainer=trainer)

Tokenize Your Text:

Once trained, encoding text is straightforward:

output = tokenizer.encode("Hello, y'all! How are you 😁 ?")
print(output.tokens)
# Output: ["Hello", ",", "y", "'", "all", "!", "How", "are", "you", "[UNK]", "?"]

For more details, users are encouraged to explore the documentation or take a quick tour to fully harness the power of Tokenizers.

In summary, the Tokenizers project is designed to meet both the rigorous demands of production environments and the exploratory needs of research, offering a versatile and high-performance solution for text processing tasks.