chore(deps): bump tokenizers from 0.19.1 to 0.20.1

[dependabot[bot]]

Bumps tokenizers from 0.19.1 to 0.20.1.

Release notes

Sourced from tokenizers's releases.

Release v0.20.1

What's Changed

The most awaited offset issue with Llama is fixed 🥳

• Update README.md by @​ArthurZucker in huggingface/tokenizers#1608
• fix benchmark file link by @​152334H in huggingface/tokenizers#1610
• Bump actions/download-artifact from 3 to 4.1.7 in /.github/workflows by @​dependabot in huggingface/tokenizers#1626
• [ignore_merges] Fix offsets by @​ArthurZucker in huggingface/tokenizers#1640
• Bump body-parser and express in /tokenizers/examples/unstable_wasm/www by @​dependabot in huggingface/tokenizers#1629
• Bump serve-static and express in /tokenizers/examples/unstable_wasm/www by @​dependabot in huggingface/tokenizers#1630
• Bump send and express in /tokenizers/examples/unstable_wasm/www by @​dependabot in huggingface/tokenizers#1631
• Bump webpack from 5.76.0 to 5.95.0 in /tokenizers/examples/unstable_wasm/www by @​dependabot in huggingface/tokenizers#1641
• Fix documentation build by @​ArthurZucker in huggingface/tokenizers#1642
• style: simplify string formatting for readability by @​hamirmahal in huggingface/tokenizers#1632

New Contributors

• @​152334H made their first contribution in huggingface/tokenizers#1610
• @​hamirmahal made their first contribution in huggingface/tokenizers#1632

Full Changelog: https://github.com/huggingface/tokenizers/compare/v0.20.0...v0.20.1

Release v0.20.0: faster encode, better python support

Release v0.20.0

This release is focused on performances and user experience.

Performances:

First off, we did a bit of benchmarking, and found some place for improvement for us! With a few minor changes (mostly #1587) here is what we get on Llama3 running on a g6 instances on AWS https://github.com/huggingface/tokenizers/blob/main/bindings/python/benches/test_tiktoken.py :

Python API

We shipped better deserialization errors in general, and support for __str__ and __repr__ for all the object. This allows for a lot easier debugging see this:

>>> from tokenizers import Tokenizer;
>>> tokenizer = Tokenizer.from_pretrained("bert-base-uncased");
>>> print(tokenizer)
Tokenizer(version="1.0", truncation=None, padding=None, added_tokens=[{"id":0, "content":"[PAD]", "single_word":False, "lstrip":False, "rstrip":False, ...}, {"id":100, "content":"[UNK]", "single_word":False, "lstrip":False, "rstrip":False, ...}, {"id":101, "content":"[CLS]", "single_word":False, "lstrip":False, "rstrip":False, ...}, {"id":102, "content":"[SEP]", "single_word":False, "lstrip":False, "rstrip":False, ...}, {"id":103, "content":"[MASK]", "single_word":False, "lstrip":False, "rstrip":False, ...}], normalizer=BertNormalizer(clean_text=True, handle_chinese_chars=True, strip_accents=None, lowercase=True), pre_tokenizer=BertPreTokenizer(), post_processor=TemplateProcessing(single=[SpecialToken(id="[CLS]", type_id=0), Sequence(id=A, type_id=0), SpecialToken(id="[SEP]", type_id=0)], pair=[SpecialToken(id="[CLS]", type_id=0), Sequence(id=A, type_id=0), SpecialToken(id="[SEP]", type_id=0), Sequence(id=B, type_id=1), SpecialToken(id="[SEP]", type_id=1)], special_tokens={"[CLS]":SpecialToken(id="[CLS]", ids=[101], tokens=["[CLS]"]), "[SEP]":SpecialToken(id="[SEP]", ids=[102], tokens=["[SEP]"])}), decoder=WordPiece(prefix="##", cleanup=True), model=WordPiece(unk_token="[UNK]", continuing_subword_prefix="##", max_input_chars_per_word=100, vocab={"[PAD]":0, "[unused0]":1, "[unused1]":2, "[unused2]":3, "[unused3]":4, ...}))
>>> tokenizer
Tokenizer(version="1.0", truncation=None, padding=None, added_tokens=[{"id":0, "content":"[PAD]", "single_word":False, "lstrip":False, "rstrip":False, "normalized":False, "special":True}, {"id":100, "content":"[UNK]", "single_word":False, "lstrip":False, "rstrip":False, "normalized":False, "special":True}, {"id":101, "content":"[CLS]", "single_word":False, "lstrip":False, "rstrip":False, "normalized":False, "special":True}, {"id":102, "content":"[SEP]", "single_word":False, "lstrip":False, "rstrip":False, "normalized":False, "special":True}, {"id":103, "content":"[MASK]", "single_word":False, "lstrip":False, "rstrip":False, "normalized":False, "special":True}], normalizer=BertNormalizer(clean_text=True, handle_chinese_chars=True, strip_accents=None, lowercase=True), pre_tokenizer=BertPreTokenizer(), post_processor=TemplateProcessing(single=[SpecialToken(id="[CLS]", type_id=0), Sequence(id=A, type_id=0), SpecialToken(id="[SEP]", type_id=0)], pair=[SpecialToken(id="[CLS]", type_id=0), Sequence(id=A, type_id=0), SpecialToken(id="[SEP]", type_id=0), Sequence(id=B, type_id=1), SpecialToken(id="[SEP]", type_id=1)], special_tokens={"[CLS]":SpecialToken(id="[CLS]", ids=[101], tokens=["[CLS]"]), "[SEP]":SpecialToken(id="[SEP]", ids=[102], tokens=["[SEP]"])}), decoder=WordPiece(prefix="##", cleanup=True), model=WordPiece(unk_token="[UNK]", continuing_subword_prefix="##", max_input_chars_per_word=100, vocab={"[PAD]":0, "[unused0]":1, "[unused1]":2, ...}))

The pre_tokenizer.Sequence and normalizer.Sequence are also more accessible now:

from tokenizers import normalizers
norm = normalizers.Sequence([normalizers.Strip(), normalizers.BertNormalizer()])
norm[0]
norm[1].lowercase=False

... (truncated)

Commits

• d98298a 0.20.1
• de305f2 update to ubuntu-22.04
• 1053470 use --interpreter ${{ matrix.interpreter || '3.7 3.8 3.9 3.10 3.11 3.12 pypy3...
• f7c33eb add Cargo
• eca17be v 0.20.1-rc1
• 557fde7 style: simplify string formatting for readability (#1632)
• 3d51a16 Fix documentation build (#1642)
• 294ab86 Bump webpack in /tokenizers/examples/unstable_wasm/www (#1641)
• 2b97a5e Bump send and express in /tokenizers/examples/unstable_wasm/www (#1631)
• 077678d Bump serve-static and express in /tokenizers/examples/unstable_wasm/www (#1630)
• Additional commits viewable in compare view

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