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Pre-release

This release is a pre-release and may not be stable for production use.

tokenizers Python bindings

This module is still experimental, expect the API to change or break. If used in a production setting, please pin the version number.

Install

uv sync

Usage

from tokenizers import Padding, Tokenizer

tokenizer = Tokenizer.from_file("tokenizer.json")
# Or the `tokenizer.json` of a model repo on the Hub. `huggingface_hub` downloads and caches it,
# with the same cache, login token and offline switch as transformers.
tokenizer = Tokenizer.from_pretrained("openai-community/gpt2", revision="main")

encoding = tokenizer.encode("Hello there, how are you?")
encoding.ids             # [101, 7592, 2045, 1010, 2129, 2024, 2017, 1029, 102]
encoding.type_ids        # [0, 0, 0, 0, 0, 0, 0, 0, 0]
encoding.attention_mask  # [1, 1, 1, 1, 1, 1, 1, 1, 1]

# The same fields as numpy arrays: read-only views over the encoding, not copies. Reading one
# costs nothing, and an array keeps its encoding alive for as long as the array exists.
encoding.ids_array             # array([101, 7592, 2045, 1010, 2129, 2024, 2017, 1029, 102], dtype=uint32)
encoding.type_ids_array        # array([0, 0, 0, 0, 0, 0, 0, 0, 0], dtype=uint32)
encoding.attention_mask_array  # array([1, 1, 1, 1, 1, 1, 1, 1, 1], dtype=uint32)
torch.from_numpy(encoding.ids_array)  # still no copy

tokenizer.encode("Hello", add_special_tokens=False).ids  # no [CLS]/[SEP]
tokenizer.decode(encoding.ids)                           # "hello there, how are you?"
tokenizer.decode(encoding.ids_array)                     # a numpy array works too
tokenizer.decode(encoding.ids, skip_special_tokens=False)

# Override the `Padding` settings. `length=None` pads each batch to its longest
# item; `None` switches padding off.
tokenizer.padding = Padding(direction="left", pad_id=0, pad_token="[PAD]")
short, long = tokenizer.encode_batch(["Hello", "Hello there, how are you?"])
len(short) == len(long)  # True
short.attention_mask     # [0, 0, 0, 0, 0, 0, 1, 1, 1]

tokenizer.padding = Padding(length=16)            # every encoding is exactly 16 ids
tokenizer.padding = Padding(pad_to_multiple_of=8)
tokenizer.padding = None

# Or for one call only, leaving the attribute as it is. `None` switches it off for that call.
tokenizer.encode_batch(["Hello", "Hello there"], padding=Padding(length=16))
tokenizer.encode("Hello", padding=None)

examples/ holds a runnable script for each of these; make examples runs them all.

Worker processes

Every class here can be pickled, so a tokenizer can be sent to a worker process and its encodings sent back: multiprocessing, a PyTorch DataLoader, datasets.map(num_proc=...).

def encode(tokenizer, texts):
    return tokenizer.encode_batch(texts)


with multiprocessing.get_context("spawn").Pool(4) as pool:
    encoded = pool.starmap(encode, [(tokenizer, chunk) for chunk in chunks])

The tokenizer is pickled again with every task it is sent with, so give each worker one big chunk of texts rather than many small tasks. examples/multiprocessing_workers.py is the whole script.

Day to day

  • make develop regenerates the .pyi stub, rebuilds the extension (debug) and reinstalls it in editable mode.
  • make test does the above, then runs tests/ with pytest.
  • make examples does the above, then runs every script in examples/.
  • make stubs regenerates python/tokenizers/tokenizers.pyi, including the members python/tokenizers/__init__.py attaches to the Rust classes.
  • make style / make check-style regenerate the stub, then format/lint the Rust and Python sides and type-check with ty. CI runs make check-style and fails if the regenerated stub differs from the committed one.

Release files for tokenizers 1.0.0rc1

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tokenizers-1.0.0rc1-cp314-cp314t-win_amd64.whl CPython 3.14 CPython 3.14 free-threading Windows x86-64 Details
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tokenizers-1.0.0rc1-cp310-abi3-musllinux_1_2_x86_64.whl CPython 3.10 abi3 Linux musl 1.2+ x86-64 Details
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This release

1.0.0rc1 This release

24 release files

0.9.4

41 release files

0.9.3

17 release files

0.9.2

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0.9.1

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0.8.1

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0.8.0

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0.7.0

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0.5.2

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0.5.1

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0.5.0

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0.4.2

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0.4.1

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0.2.1

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0.2.0

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0.1.1

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0.1.0

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0.0.9

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0.0.8

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0.0.7

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0.0.6

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0.0.5

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0.0.4

13 release files

0.0.1

1 release file

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