flexitok/bpe_boundless_coverage_AllL_128000

Byte-Level BPE Tokenizer: ['arb_Arab', 'ces_Latn', 'cmn_Hani', 'dan_Latn', 'deu_Latn', 'ell_Grek', 'fra_Latn', 'fw_edu', 'hun_Latn', 'ind_Latn', 'ita_Latn', 'jpn_Jpan', 'nld_Latn', 'pol_Latn', 'por_Latn', 'rus_Cyrl', 'spa_Latn', 'swe_Latn', 'tur_Latn', 'vie_Latn'] (128K)

A Byte-Level BPE tokenizer trained on ['arb_Arab', 'ces_Latn', 'cmn_Hani', 'dan_Latn', 'deu_Latn', 'ell_Grek', 'fra_Latn', 'fw_edu', 'hun_Latn', 'ind_Latn', 'ita_Latn', 'jpn_Jpan', 'nld_Latn', 'pol_Latn', 'por_Latn', 'rus_Cyrl', 'spa_Latn', 'swe_Latn', 'tur_Latn', 'vie_Latn'] data from Fineweb-2-HQ.

Training Details

Parameter	Value
Algorithm	Byte-Level BPE
Language	['arb_Arab', 'ces_Latn', 'cmn_Hani', 'dan_Latn', 'deu_Latn', 'ell_Grek', 'fra_Latn', 'fw_edu', 'hun_Latn', 'ind_Latn', 'ita_Latn', 'jpn_Jpan', 'nld_Latn', 'pol_Latn', 'por_Latn', 'rus_Cyrl', 'spa_Latn', 'swe_Latn', 'tur_Latn', 'vie_Latn']
Target Vocab Size	128,000
Final Vocab Size	130,765
Pre-tokenizer	custom:boundless_bpe
Number handling	ltr_3digit
Contraction handling	True
Normalizer	NFC
Special Tokens	<s>, </s>, <pad>, <unk>
Training Shards	40

Usage

from transformers import AutoTokenizer

tokenizer = AutoTokenizer.from_pretrained("flexitok/bpe_boundless_coverage_AllL_128000")
tokens = tokenizer.encode("Hello, world!")

Files

• tokenizer.json — Full HuggingFace tokenizer
• vocab.json — Vocabulary mapping
• merges.txt — BPE merge rules

Sample Encoding

Text	Tokens	Token IDs
Hello, world! 12345 This is a test. こんにちは	H, ello, ,, Ġworld, !, Ġ, 123, 45, ĠThis, Ġis, Ġa, Ġtest, ., Ġãģ, ĵ, ãĤ, ĵ, ãģ«, ãģ, ¡	42, 54335, 14, 27328, 3, 223, 24137, 3871, 18980, 3996, 1197, 38098, 16, 44396, 244, 18464, 244, 82907, 11232, 97