Sourced from tokenizers's releases.

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

What's Changed

• remove enforcement of non special when adding tokens by @​ArthurZucker in huggingface/tokenizers#1521
• [BREAKING CHANGE] Ignore added_tokens (both special and not) in the decoder by @​Narsil in huggingface/tokenizers#1513
• Make USED_PARALLELISM atomic by @​nathaniel-daniel in huggingface/tokenizers#1532
• Fixing for clippy 1.78 by @​Narsil in huggingface/tokenizers#1548
• feat(ci): add trufflehog secrets detection by @​McPatate in huggingface/tokenizers#1551
• Switch from cached_download to hf_hub_download in tests by @​Wauplin in huggingface/tokenizers#1547
• Fix "dictionnary" typo by @​nprisbrey in huggingface/tokenizers#1511
• make sure we don't warn on empty tokens by @​ArthurZucker in huggingface/tokenizers#1554
• Enable dropout = 0.0 as an equivalent to none in BPE by @​mcognetta in huggingface/tokenizers#1550
• Revert "[BREAKING CHANGE] Ignore added_tokens (both special and not) … by @​ArthurZucker in huggingface/tokenizers#1569
• Add bytelevel normalizer to fix decode when adding tokens to BPE by @​ArthurZucker in huggingface/tokenizers#1555
• Fix clippy + feature test management. by @​Narsil in huggingface/tokenizers#1580
• Bump spm_precompiled to 0.1.3 by @​MikeIvanichev in huggingface/tokenizers#1571
• Add benchmark vs tiktoken by @​Narsil in huggingface/tokenizers#1582
• Fixing the benchmark. by @​Narsil in huggingface/tokenizers#1583
• Tiny improvement by @​Narsil in huggingface/tokenizers#1585
• Enable fancy regex by @​Narsil in huggingface/tokenizers#1586
• Fixing release CI strict (taken from safetensors). by @​Narsil in huggingface/tokenizers#1593
• Adding some serialization testing around the wrapper. by @​Narsil in huggingface/tokenizers#1594

... (truncated)

• a5adaac version 0.20.0
• a8def07 Merge branch 'fix_release' of github.com:huggingface/tokenizers into branch_v...
• fe50673 Fix CI
• b253835 push cargo
• fc3bb76 update dependencies
• bfd9cde Perf improvement 16% by removing offsets. (#1587)
• bd27fa5 add deserialize for pre tokenizers (#1603)
• 56c9c70 Tests + Deserialization improvement for normalizers. (#1604)
• 49dafd7 Fix strip python type (#1602)
• bded212 Support None to reset pre_tokenizers and normalizers, and index sequences (...
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