Yanked
This release has been yanked by its maintainers, and will be ignored by installers, except when explicitly specified.
Consider using release 0.23.3 instead.
Reason given by maintainers: Removing support for python3.7 and 3.8 is breaking and is move to v0.21.0
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 -e .
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 BPEByteLevelBPETokenizer: The byte level version of the BPESentencePieceBPETokenizer: A BPE implementation compatible with the one used by SentencePieceBertWordPieceTokenizer: 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?")
4 maintainers
Metadata
4 maintainers
Release files for tokenizers 0.20.4
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| tokenizers-0.20.4.tar.gz | 343.0 kB | Details |
Built distributions (wheels)
Total release size: 64.9 MB
Release files / tokenizers-0.20.4.tar.gz
| Download URL | tokenizers-0.20.4.tar.gz |
|---|---|
| Size | 343.0 kB |
| Tags | Source |
|
SHA-256 checksum How to use checksums |
db50ac15e92981227f499268541306824f49e97dbeec05d118ebdc7c2d22322c
|
|
BLAKE2b-256 checksum How to use checksums |
1a980df883ea6201e35e286a97f5fb2a601bfb5b52e4165f7688a76e4553eeec
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
maturin/1.7.5
|
Release files / tokenizers-0.20.4-cp39-abi3-win_amd64.whl
| Download URL | tokenizers-0.20.4-cp39-abi3-win_amd64.whl |
|---|---|
| Size | 2.4 MB |
| Tags | CPython 3.9 Windows x86-64 abi3 |
|
SHA-256 checksum How to use checksums |
6cba92b87969ddf5a7e2f2293577c30129d8c22c6f68e8c626d3e76b8d52412c
|
|
BLAKE2b-256 checksum How to use checksums |
e0e3c7e4adf727ddcdefc1831945bf95fe07dd6b4b879613a62b5719be539ce4
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
maturin/1.7.5
|
Release files / tokenizers-0.20.4-cp39-abi3-win32.whl
| Download URL | tokenizers-0.20.4-cp39-abi3-win32.whl |
|---|---|
| Size | 2.2 MB |
| Tags | CPython 3.9 Windows x86-32 abi3 |
|
SHA-256 checksum How to use checksums |
60ea37c885a9bb8efa53b7542ea83561cd00eb3ffb47a77f5ae622d9f7f66ffe
|
|
BLAKE2b-256 checksum How to use checksums |
a11c0c9ebd40b4e6bb51b178cff6b148e985a728fc899d12f4a6ebe49044d4e2
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
maturin/1.7.5
|
Release files / tokenizers-0.20.4-cp39-abi3-musllinux_1_2_x86_64.whl
| Download URL | tokenizers-0.20.4-cp39-abi3-musllinux_1_2_x86_64.whl |
|---|---|
| Size | 9.4 MB |
| Tags | CPython 3.9 Linux musl 1.2+ x86-64 abi3 |
|
SHA-256 checksum How to use checksums |
a6d392a20ca70692aaba8a636677b57f6c67655879773ba2b6be8cb4a19ce6b8
|
|
BLAKE2b-256 checksum How to use checksums |
9e6241dc66bda0b1d45f36f324d01a3c4389caac0526d4b4ccc2d2006c7722b1
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
maturin/1.7.5
|
Release files / tokenizers-0.20.4-cp39-abi3-musllinux_1_2_i686.whl
| Download URL | tokenizers-0.20.4-cp39-abi3-musllinux_1_2_i686.whl |
|---|---|
| Size | 9.2 MB |
| Tags | CPython 3.9 Linux musl 1.2+ x86-32 abi3 |
|
SHA-256 checksum How to use checksums |
84bf8b4a7bbf1c6bb78775ae309a5c69d08dadf7b88125d6d19ccb4738a87350
|
|
BLAKE2b-256 checksum How to use checksums |
94082d541e0e437e87e5cae903c55fc832fd032812c85e095bb7833d5fe65dfd
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
maturin/1.7.5
|
Release files / tokenizers-0.20.4-cp39-abi3-musllinux_1_2_armv7l.whl
| Download URL | tokenizers-0.20.4-cp39-abi3-musllinux_1_2_armv7l.whl |
|---|---|
| Size | 8.9 MB |
| Tags | CPython 3.9 Linux musl 1.2+ ARMv7l abi3 |
|
SHA-256 checksum How to use checksums |
e59a405459ed31b73426b364752c2e7c73f4a94210a63fd7acd161a774af7bd2
|
|
BLAKE2b-256 checksum How to use checksums |
245661b8c0cd217a5be05c4206263afdfa937848796696fee7cc14fab6c94a49
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
maturin/1.7.5
|
Release files / tokenizers-0.20.4-cp39-abi3-musllinux_1_2_aarch64.whl
| Download URL | tokenizers-0.20.4-cp39-abi3-musllinux_1_2_aarch64.whl |
|---|---|
| Size | 9.0 MB |
| Tags | CPython 3.9 Linux musl 1.2+ ARM64 abi3 |
|
SHA-256 checksum How to use checksums |
3e960ad5c467a95e5665e518151ed9024e7aa111d2c54ff1938162cc7c2b8959
|
|
BLAKE2b-256 checksum How to use checksums |
efbf135de1cf1c10f53a10c8db5f067e7cf71adebd79ed2b5cf5729eded209af
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
maturin/1.7.5
|
Release files / tokenizers-0.20.4-cp39-abi3-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
| Download URL | tokenizers-0.20.4-cp39-abi3-manylinux_2_17_x86_64.manylinux2014_x86_64.whl |
|---|---|
| Size | 3.0 MB |
| Tags | CPython 3.9 Linux glibc 2.17+ x86-64 abi3 |
|
SHA-256 checksum How to use checksums |
05c2bab579c1f31292b48bb79b6334b5346c1ec87dac81089e6098b8a20b2fd4
|
|
BLAKE2b-256 checksum How to use checksums |
b91ccc1ca0c4e4c5763b2d872935de7149633765d158f0d1a46b48d73f3a9c58
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
maturin/1.7.5
|
Release files / tokenizers-0.20.4-cp39-abi3-manylinux_2_17_s390x.manylinux2014_s390x.whl
| Download URL | tokenizers-0.20.4-cp39-abi3-manylinux_2_17_s390x.manylinux2014_s390x.whl |
|---|---|
| Size | 3.4 MB |
| Tags | CPython 3.9 Linux glibc 2.17+ IBM System/390x abi3 |
|
SHA-256 checksum How to use checksums |
735ffc9bba65d20f8ab5f82dfbab262bb066afc7dee3684c5e5435e7a5da445d
|
|
BLAKE2b-256 checksum How to use checksums |
f6c934c6a304529a0b3375533e0ccc4ffd4bcfa401cb57cdab5997efaf9836fc
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
maturin/1.7.5
|
Release files / tokenizers-0.20.4-cp39-abi3-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl
| Download URL | tokenizers-0.20.4-cp39-abi3-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl |
|---|---|
| Size | 3.1 MB |
| Tags | CPython 3.9 Linux glibc 2.17+ PowerPC 64-le abi3 |
|
SHA-256 checksum How to use checksums |
eee647ccba9cbd36b5ec4e8e73d25dbd586ec06de7a43ff83a3dad9fec466a29
|
|
BLAKE2b-256 checksum How to use checksums |
6549e4647d5e496e64f2f639aeba9df134f59b648822a90d63ceb7b4b309ba4a
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
maturin/1.7.5
|
Release files / tokenizers-0.20.4-cp39-abi3-manylinux_2_17_i686.manylinux2014_i686.whl
| Download URL | tokenizers-0.20.4-cp39-abi3-manylinux_2_17_i686.manylinux2014_i686.whl |
|---|---|
| Size | 3.1 MB |
| Tags | CPython 3.9 Linux glibc 2.17+ x86-32 abi3 |
|
SHA-256 checksum How to use checksums |
aa392bae7f0a36e4c97ad43100390ad84f2a1bfff6742604774210f7d7a4fa13
|
|
BLAKE2b-256 checksum How to use checksums |
d0298dff1d57d1d1bcae5f0d701759ca3f503ca31ecd443f75571d6ef3083043
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
maturin/1.7.5
|
Release files / tokenizers-0.20.4-cp39-abi3-manylinux_2_17_armv7l.manylinux2014_armv7l.whl
| Download URL | tokenizers-0.20.4-cp39-abi3-manylinux_2_17_armv7l.manylinux2014_armv7l.whl |
|---|---|
| Size | 2.8 MB |
| Tags | CPython 3.9 Linux glibc 2.17+ ARMv7l abi3 |
|
SHA-256 checksum How to use checksums |
075635cd7e6936cc4b3a13901c1a05690d5b533ce3d0f035dee21117dd4f04ae
|
|
BLAKE2b-256 checksum How to use checksums |
b7d1e70b4497da0e97111ef75eba32526a98bf04268ce2a7388d9759ec89acdb
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
maturin/1.7.5
|
Release files / tokenizers-0.20.4-cp39-abi3-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
| Download URL | tokenizers-0.20.4-cp39-abi3-manylinux_2_17_aarch64.manylinux2014_aarch64.whl |
|---|---|
| Size | 2.9 MB |
| Tags | CPython 3.9 Linux glibc 2.17+ ARM64 abi3 |
|
SHA-256 checksum How to use checksums |
7786004e180fab72e6e873e982ccd18b3cfa31521d397b6c024cc19175abf91b
|
|
BLAKE2b-256 checksum How to use checksums |
66aa70de5a7c96621c1962e8399245efac6d6ab3bbe873457b9beb26c406c4f0
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
maturin/1.7.5
|
Release files / tokenizers-0.20.4-cp39-abi3-macosx_11_0_arm64.whl
| Download URL | tokenizers-0.20.4-cp39-abi3-macosx_11_0_arm64.whl |
|---|---|
| Size | 2.6 MB |
| Tags | CPython 3.9 abi3 macOS 11.0+ ARM64 |
|
SHA-256 checksum How to use checksums |
f41df992797ad0ff9472e8a2c7a3ef7178667935d984639b73da7d19b33ea4e2
|
|
BLAKE2b-256 checksum How to use checksums |
bb26a4b8dfe37c92205a4ba267d35ed7a6ca0ae1cbb2a1ed8acf84813cf48704
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
maturin/1.7.5
|
Release files / tokenizers-0.20.4-cp39-abi3-macosx_10_12_x86_64.whl
| Download URL | tokenizers-0.20.4-cp39-abi3-macosx_10_12_x86_64.whl |
|---|---|
| Size | 2.6 MB |
| Tags | CPython 3.9 abi3 macOS 10.12+ x86-64 |
|
SHA-256 checksum How to use checksums |
25f59ebc5b79e7bbafe86bfec62696468016627157d8a9ceba5092486796a156
|
|
BLAKE2b-256 checksum How to use checksums |
b7a1c8285e835dd185307f40bc4a308224dc357ee4d25f3fd83ef013101a5060
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
maturin/1.7.5
|
