Fast and Customizable Tokenizers

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ArthurZucker McPotato Nicolas.Patry xn1t0x

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Project description

Tokenizers

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

Bindings over the Rust implementation. If you are interested in the High-level design, you can go check it there.

Otherwise, let's dive in!

Main features:

Train new vocabularies and tokenize using 4 pre-made tokenizers (Bert WordPiece and the 3 most common BPE versions).
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.

Installation

With pip:

pip install tokenizers

From sources:

To use this method, you need to have the Rust installed:

# Install with:
curl https://sh.rustup.rs -sSf | sh -s -- -y
export PATH="$HOME/.cargo/bin:$PATH"

Once Rust is installed, you can compile doing the following

git clone https://github.com/huggingface/tokenizers
cd tokenizers/bindings/python

# Create a virtual env (you can use yours as well)
python -m venv .env
source .env/bin/activate

# Install `tokenizers` in the current virtual env
pip install setuptools_rust
python setup.py install

Load a pretrained tokenizer from the Hub

from tokenizers import Tokenizer

tokenizer = Tokenizer.from_pretrained("bert-base-cased")

Using the provided Tokenizers

We provide some pre-build tokenizers to cover the most common cases. You can easily load one of these using some vocab.json and merges.txt files:

from tokenizers import CharBPETokenizer

# Initialize a tokenizer
vocab = "./path/to/vocab.json"
merges = "./path/to/merges.txt"
tokenizer = CharBPETokenizer(vocab, merges)

# And then encode:
encoded = tokenizer.encode("I can feel the magic, can you?")
print(encoded.ids)
print(encoded.tokens)

And you can train them just as simply:

from tokenizers import CharBPETokenizer

# Initialize a tokenizer
tokenizer = CharBPETokenizer()

# Then train it!
tokenizer.train([ "./path/to/files/1.txt", "./path/to/files/2.txt" ])

# Now, let's use it:
encoded = tokenizer.encode("I can feel the magic, can you?")

# And finally save it somewhere
tokenizer.save("./path/to/directory/my-bpe.tokenizer.json")

Provided Tokenizers

CharBPETokenizer: The original BPE
ByteLevelBPETokenizer: The byte level version of the BPE
SentencePieceBPETokenizer: A BPE implementation compatible with the one used by SentencePiece
BertWordPieceTokenizer: The famous Bert tokenizer, using WordPiece

All of these can be used and trained as explained above!

Build your own

Whenever these provided tokenizers don't give you enough freedom, you can build your own tokenizer, by putting all the different parts you need together. You can check how we implemented the provided tokenizers and adapt them easily to your own needs.

Building a byte-level BPE

Here is an example showing how to build your own byte-level BPE by putting all the different pieces together, and then saving it to a single file:

from tokenizers import Tokenizer, models, pre_tokenizers, decoders, trainers, processors

# Initialize a tokenizer
tokenizer = Tokenizer(models.BPE())

# Customize pre-tokenization and decoding
tokenizer.pre_tokenizer = pre_tokenizers.ByteLevel(add_prefix_space=True)
tokenizer.decoder = decoders.ByteLevel()
tokenizer.post_processor = processors.ByteLevel(trim_offsets=True)

# And then train
trainer = trainers.BpeTrainer(
    vocab_size=20000,
    min_frequency=2,
    initial_alphabet=pre_tokenizers.ByteLevel.alphabet()
)
tokenizer.train([
    "./path/to/dataset/1.txt",
    "./path/to/dataset/2.txt",
    "./path/to/dataset/3.txt"
], trainer=trainer)

# And Save it
tokenizer.save("byte-level-bpe.tokenizer.json", pretty=True)

Now, when you want to use this tokenizer, this is as simple as:

from tokenizers import Tokenizer

tokenizer = Tokenizer.from_file("byte-level-bpe.tokenizer.json")

encoded = tokenizer.encode("I can feel the magic, can you?")

Project details

These details have been verified by PyPI

Maintainers

ArthurZucker McPotato Nicolas.Patry xn1t0x

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Project links

Homepage

Release history Release notifications | RSS feed

0.20.3

Nov 5, 2024

0.20.3rc0 pre-release

Nov 15, 2024

0.20.2

Nov 4, 2024

0.20.1

Oct 10, 2024

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0.20.0

Aug 8, 2024

0.20.0rc1 pre-release

Aug 8, 2024

0.19.1

Apr 17, 2024

0.19.0

Apr 17, 2024

0.15.2

Feb 12, 2024

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Feb 11, 2024

0.15.1

Jan 22, 2024

0.15.0

Nov 14, 2023

0.14.1

Oct 6, 2023

0.14.0

Sep 7, 2023

0.13.3

Apr 5, 2023

0.13.2

Nov 7, 2022

This version

0.13.1

Oct 6, 2022

0.13.0

Sep 21, 2022

0.12.1

Apr 13, 2022

0.12.0 yanked

Mar 31, 2022

Reason this release was yanked:

Breaking change with unexpected effect

0.11.6

Feb 28, 2022

0.11.5

Feb 16, 2022

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Jan 17, 2022

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Dec 28, 2021

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0.10.1rc1 pre-release

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Oct 15, 2020

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tokenizers 0.13.1

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Tokenizers

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Load a pretrained tokenizer from the Hub

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Provided Tokenizers

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BLAKE2b-256	`fec64cfc87f50c35d8e5c89bcc814c365b3e8f21ddf9c33a5bf9a548bacd2ce2`

Algorithm	Hash digest
SHA256	`126bcb18a77cf65961ece04f54bd10ef3713412195e543d9d3eda2f0e4fd677c`
MD5	`010a86940f6505e891e455559e74be39`
BLAKE2b-256	`39c8b8d95bf1022b8f34c72159e7e6f52207be71ca2cf88d164308e7219811f2`

Algorithm	Hash digest
SHA256	`8b3f97041f7716998e474d3c7ffd19ac6941f117616696aef2b5ba927bf091e3`
MD5	`1f6d756a9e3edcda25ad9630e138bb4a`
BLAKE2b-256	`4816e5eb09f9b628290b32bea573865fcc198ef6bfabfda89450a04f239a21b5`

Algorithm	Hash digest
SHA256	`16434c61c5eb72f6692b9bc52052b07ca92d3eba9dd72a1bc371890e1bdc3f07`
MD5	`85e7cdc0751c67a093aefa7b4a554347`
BLAKE2b-256	`3e305650b453912ba7c4f8e9282540b8966a6a9b04a105b083fc9c8eb3785b31`

Algorithm	Hash digest
SHA256	`3acf3cae4c4739fc9ec49fa0e6cce224c1aa0fabc8f8d1358fd7de5c7d49cdca`
MD5	`b804ec39fe43084956f404a2f74d0b0b`
BLAKE2b-256	`9c9ebf06b261f8d1ea03c5d5dc3689f376d5fedf1cc231c5b515a47a417f4ad7`

Algorithm	Hash digest
SHA256	`db6872294339bf35c158219fc65bad939ba87d14c936ae7a33a3ca2d1532c5b1`
MD5	`21828664f131d51a352f7333b3f7a463`
BLAKE2b-256	`d8d95ce0c0c91fa0d6d879a2adb5db50d45ec8704147bc88e229c46d7c5ec410`

Algorithm	Hash digest
SHA256	`96a1beef1e64d44597627f4e29d794047a66ad4d7474d93daf5a0ee27928e012`
MD5	`e6b0d9b636794491ef7078e1fbaac6b5`
BLAKE2b-256	`aa53f00c2bd1e8f686c954d96a319fcebb80a9b69e7ef004de88b7471e3b1370`

Algorithm	Hash digest
SHA256	`80864f456f715829f901ad5bb85af97e9ae52fc902270944804f6476ab8c6699`
MD5	`22b89e3a1d2fbdc59871cdff91f9f05a`
BLAKE2b-256	`7e046040ac72e1b9a36753cc402a4d57d8f69070d568ad3de948f0f654b2078e`

Algorithm	Hash digest
SHA256	`3c69a8389fd88bc32115e99db70f63bef577ba5c54f40a632580038a49612856`
MD5	`2365fe7b5fb449401eddef0e5b5b9e6e`
BLAKE2b-256	`a06a0cf58a76bffa7da336cacc562fc55c3dda4b4ebe4136e37ed72088511646`

Algorithm	Hash digest
SHA256	`4b3be8af87b357340b9b049d28067287b5e5e296e3120b6e4875d3b8469b67e6`
MD5	`7f3e609301e32cbca42bfdc65a5b3a73`
BLAKE2b-256	`6da97f8bf9875c1584bc8233a0a0e8d5f694c0a5ef1c59509030dbf34e938d8c`