tokenizers 0.10.0

The core of tokenizers, written in Rust. Provides an implementation of today's most used tokenizers, with a focus on performance and versatility.

What is a Tokenizer

A Tokenizer works as a pipeline, it processes some raw text as input and outputs an Encoding. The various steps of the pipeline are:

1. The Normalizer: in charge of normalizing the text. Common examples of normalization are the unicode normalization standards, such as NFD or NFKC.
2. The PreTokenizer: in charge of creating initial words splits in the text. The most common way of splitting text is simply on whitespace.
3. The Model: in charge of doing the actual tokenization. An example of a Model would be BPE or WordPiece.
4. The PostProcessor: in charge of post-processing the Encoding to add anything relevant that, for example, a language model would need, such as special tokens.

Quick example

use tokenizers::tokenizer::{Result, Tokenizer, EncodeInput};
use tokenizers::models::bpe::BPE;

fn main() -> Result<()> {
    let bpe_builder = BPE::from_files("./path/to/vocab.json", "./path/to/merges.txt")?;
    let bpe = bpe_builder
        .dropout(0.1)
        .unk_token("[UNK]".into())
        .build()?;

    let mut tokenizer = Tokenizer::new(Box::new(bpe));

    let encoding = tokenizer.encode(EncodeInput::Single("Hey there!".into()))?;
    println!("{:?}", encoding.get_tokens());

    Ok(())
}

Additional information

• tokenizers is designed to leverage CPU parallelism when possible. The level of parallelism is determined by the total number of core/threads your CPU provides but this can be tuned by setting the RAYON_RS_NUM_CPUS environment variable. As an example setting RAYON_RS_NUM_CPUS=4 will allocate a maximum of 4 threads. Please note this behavior may evolve in the future