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Provides an implementation of today's most used tokenizers, with a focus on performance and versatility.

• Train new vocabularies and tokenize, using today's most used tokenizers.
• Extremely fast (both training and tokenization), thanks to the Rust implementation. Takes less than 20 seconds to tokenize a GB of text on a server's CPU.
• Easy to use, but also extremely versatile.
• Designed for research and production.
• Normalization comes with alignments tracking. It's always possible to get the part of the original sentence that corresponds to a given token.
• Does all the pre-processing: Truncate, Pad, add the special tokens your model needs.

Performances can vary depending on hardware, but running the ~/bindings/python/benches/test_tiktoken.py should give the following on a g6 aws instance:

We provide bindings to the following languages (more to come!):

• Rust (Original implementation)
• Python
• Node.js
• Ruby (Contributed by @ankane, external repo)

Choose your model between Byte-Pair Encoding, WordPiece or Unigram and instantiate a tokenizer:

from tokenizers import Tokenizer
from tokenizers.models import BPE

tokenizer = Tokenizer(BPE())

You can customize how pre-tokenization (e.g., splitting into words) is done:

from tokenizers.pre_tokenizers import Whitespace

tokenizer.pre_tokenizer = Whitespace()

Then training your tokenizer on a set of files just takes two lines of codes:

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)

Once your tokenizer is trained, encode any text with just one line:

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

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

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. More details about how to use the Normalizers are available on the Hugging Face blog
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.

use tokenizers::tokenizer::{Result, Tokenizer};

fn main() -> Result<()> {
    # #[cfg(feature = "http")]
    # {
        let tokenizer = Tokenizer::from_pretrained("bert-base-cased", None)?;

        let encoding = tokenizer.encode("Hey there!", false)?;
        println!("{:?}", encoding.get_tokens());
    # }
    Ok(())
}

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

fn main() -> Result<()> {
    let bpe_builder = BPE::from_file("./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(bpe);

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

    Ok(())
}

use tokenizers::decoders::DecoderWrapper;
use tokenizers::models::bpe::{BpeTrainerBuilder, BPE};
use tokenizers::normalizers::{strip::Strip, unicode::NFC, utils::Sequence, NormalizerWrapper};
use tokenizers::pre_tokenizers::byte_level::ByteLevel;
use tokenizers::pre_tokenizers::PreTokenizerWrapper;
use tokenizers::processors::PostProcessorWrapper;
use tokenizers::{AddedToken, Model, Result, TokenizerBuilder};

use std::path::Path;

fn main() -> Result<()> {
    let vocab_size: usize = 100;

    let mut trainer = BpeTrainerBuilder::new()
        .show_progress(true)
        .vocab_size(vocab_size)
        .min_frequency(0)
        .special_tokens(vec![
            AddedToken::from(String::from("<s>"), true),
            AddedToken::from(String::from("<pad>"), true),
            AddedToken::from(String::from("</s>"), true),
            AddedToken::from(String::from("<unk>"), true),
            AddedToken::from(String::from("<mask>"), true),
        ])
        .build();

    let mut tokenizer = TokenizerBuilder::new()
        .with_model(BPE::default())
        .with_normalizer(Some(Sequence::new(vec![
            Strip::new(true, true).into(),
            NFC.into(),
        ])))
        .with_pre_tokenizer(Some(ByteLevel::default()))
        .with_post_processor(Some(ByteLevel::default()))
        .with_decoder(Some(ByteLevel::default()))
        .build()?;

    let pretty = false;
    tokenizer
        .train_from_files(
            &mut trainer,
            vec!["path/to/vocab.txt".to_string()],
        )?
        .save("tokenizer.json", pretty)?;

    Ok(())
}

• 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_THREADS environment variable. As an example setting RAYON_RS_NUM_THREADS=4 will allocate a maximum of 4 threads. Please note this behavior may evolve in the future

progressbar: The progress bar visualization is enabled by default. It might be disabled if compilation for certain targets is not supported by the termios dependency of the indicatif progress bar.

081235bd (Initial commit)