konko/bengali-bpe-tokenizer

Bengali BPE Tokenizer (bn-bpe-64k)

A Bengali-first, grapheme-cluster-aware tokenizer that never splits a conjunct. It is the first component of Project Bornomala, a non-commercial research effort from West Bengal to build a Bengali-first, dialect-aware language model and to preserve the Bengali language and its dialects.

Renamed 2026-09-18. This repository was previously listed as konko/bornomala-bengali-tokenizer. That name now hosts Project Bornomala's flagship tokenizer, BMBT, a grammar-parsed sibling model with a featural-decomposition output that this model does not have. On raw tokenizer efficiency the two tie almost exactly (see BMBT's card for the one register where they diverge); this model remains published as the simpler, statistically-discovered baseline both are measured against.

Version 0.2. Trained on a literary-weighted corpus (Wikisource, AI4Bharat Sangraha, Wikipedia, XL-Sum news), 64k vocabulary. Supersedes the v0.1 Wikipedia-only model.

Key features

• Conjunct integrity by design. Text is normalised to NFC, segmented into Unicode UAX #29 grapheme clusters, and each cluster is remapped to an atomic symbol before subword training. Every learned token is a whole number of grapheme clusters, so a Bengali conjunct is never split across a boundary.
• Efficient. Lowest tokens per word, on every register tested (Wikipedia, literary/formal, general web, news), with a 64k vocabulary.
• Robust. The whole Bengali Unicode block, Bengali's shared sentence punctuation (danda/double danda), and ASCII are guaranteed, so any Bengali or code-mixed English text round-trips exactly.
• Open and CPU-only. Apache 2.0. No GPU needed to train or use.

Performance

Measured on 828 held-out Bengali Wikipedia lines (unseen during training); three further disjoint held-out registers (literary/formal, general web, news) confirm the same ranking beyond Wikipedia, see the repository's benchmarks/bengali-comparison.md. Every other tokenizer is its real public tokenizer on the same NFC-normalised text. To our knowledge, this is the first fully reproducible benchmark comparing modern Bengali tokenizers across compression, word preservation, and conjunct fragmentation using a common evaluation pipeline.

Tokenizer	Fertility	STRR	Bytes/token	Destructive rate
This model (bn-bpe-64k)	1.524	0.722	11.38	0.0004
BanglaBERT (csebuetnlp)	1.625	0.649	10.67	0.0162
IndicBERTv2 (AI4Bharat)	1.652	0.612	10.50	0.0191
BanglaT5 (csebuetnlp)	1.669	0.628	10.39	0.0088
XLM-RoBERTa (Meta)	2.464	0.363	7.04	0.0627
Sarvam-1 (Sarvam AI)	2.593	0.415	6.69	0.0364
GPT-4o (OpenAI o200k)	2.608	0.111	6.65	n/a
BrahmicTokenizer-131K (TSAI)	2.620	0.154	6.62	0.0820
mBERT (Google)	2.777	0.385	6.25	0.1552
DeepSeek-V3	2.994	0.089	5.79	0.1031

Lower fertility and lower destructive rate are better; higher STRR and bytes per token are better. Fewer tokens per word means lower cost and more usable context. Destructive rate counts only splits that sever something real (a stranded virama, a detached nukta), not a harmless consonant-cluster/vowel-sign seam - the corrected replacement for a cruder binary fragmentation count. Every general tokenizer breaks between 0.9% and 15.5% of Bengali conjuncts destructively on this held-out set; this one breaks 0.04%. Also measured on literary/formal, general web, news, romanized Banglish, and FLORES+ (the exact corpus an external tokenizer-fertility paper's own numbers come from): see benchmarks/bengali-comparison.md in the repository for all six registers and the BMBT sibling tokenizer's numbers alongside this one.

Hard words: conjuncts and Bengali place names

A register average can hide how a tokenizer treats specific, culturally load-bearing words. A fixed list of 13 - deity names, the national poet Rabindranath Tagore, well-known West Bengal places, all conjunct-dense - measured on every tokenizer this project tracks (19 total).

This model tokenizes every one of the 13 words as exactly one token, including the triple-conjunct আকাঙ্ক্ষা and the multi-akshara রবীন্দ্রনাথ. BanglaBERT and BanglaT5 (csebuetnlp) also score a perfect 1.00 average here. What still sets this model apart: it guarantees the result by construction (grammar cannot split a grapheme cluster), not by whatever a vocabulary happened to cover on these 13 specific words.

Word	Meaning	Ours	BanglaBERT/BanglaT5	IndicBERTv2	GPT-4o
স্ত্রী	wife/woman	1	1 / 1	1	2
আকাঙ্ক্ষা	aspiration	1	1 / 1	1	6
রবীন্দ্রনাথ	Rabindranath (Tagore)	1	1 / 1	1	7
পশ্চিমবঙ্গ	West Bengal	1	1 / 1	1	5
বিষ্ণুপুর	Bishnupur	1	1 / 1	2	5
শান্তিনিকেতন	Santiniketan	1	1 / 1	3	5

Average tokens/word over all 13 words, all 19 tokenizers measured: ours, BanglaBERT, and BanglaT5 all tie at 1.00; IndicBERTv2, the closest tokenizer not tied, averages 1.31 and still fragments 3 of the 13 words; the rest (SUTRA, Sarvam-1, Param2-17B, BrahmicTokenizer-131K, XLM-RoBERTa, mBERT, GPT-4o, DeepSeek-V3, Krutrim, Gemma-2, Qwen2.5, GPT-4 cl100k, Llama-3.1, Mistral-7B) run 3.31-11.08 (Gemma-2 5.69). Full per-word table and the reproduce command: benchmarks/hard-words.md.

Usage

The tokenizer uses a grapheme-atom scheme, so encode and decode go through the bntok helper, which handles NFC normalisation and the cluster-to-atom remap.

pip install "bntok @ git+https://github.com/konkomaji/bornomala#subdirectory=bengali-tokenizer"
# or clone the repo and: pip install -e bengali-tokenizer

from huggingface_hub import snapshot_download
from bntok import BengaliTokenizer

path = snapshot_download("konko/bengali-bpe-tokenizer")
# the config file is stored as bornomala_config.json; rename to config.json in the folder,
# or copy the three files (tokenizer.json, atoms.json, config.json) into one directory.
tok = BengaliTokenizer.load(path)

ids = tok.encode("আমি বাংলায় ক্ষুদ্র গান গাই")
assert tok.decode(ids) == "আমি বাংলায় ক্ষুদ্র গান গাই"   # exact round-trip
print(len(ids), "tokens")

To simply count tokens with the raw atom-space model (advanced), load tokenizer.json with the tokenizers library, but note it expects atom-remapped input; the bntok wrapper is the supported path.

Training details

• Algorithm: BPE, vocabulary 64,000.
• Corpus: literary-weighted, 1.5M lines: Wikisource Bengali public-domain text, AI4Bharat Sangraha verified/ben (pdf-typed as a formal/literary proxy, OCR-noise-filtered, and web-typed for general register), the first 15,000 articles of wikimedia/wikipedia config 20231101.bn, and XL-Sum Bengali news. Full mix and what was substituted: repository docs/known-issues.md point 6.
• Normalisation: NFC plus explicit re-composition of the three Bengali letters NFC leaves decomposed (ড়/ঢ়/য়), and a preserve-by-default ZWJ/ZWNJ policy.
• Hardware: CPU only.

Limitations

Two of the corpus's six configured sources (government/administrative text, code-mixed Bengali-English) have no clean public dataset and are omitted. The literary/formal register is a proxy (Sangraha pdf-typed documents: genuinely old-orthography and OCR-noisy, but not confirmed pre-1950 public domain). Fragmentation is near zero, not exactly zero, because rare sub-threshold clusters decompose. See the repository's docs/known-issues.md for the full, honest list, including two real bugs found and fixed in the comparison script itself.

BMBT: the flagship v2 tokenizer, now published

Project Bornomala's flagship tokenizer is now BMBT (Bornomala's Bengali Tokenizer), which parses Bengali's akshara grammar directly with a finite-state machine instead of discovering structure statistically, and adds a real featural decomposition (featurize()) as an output of the tokenizer itself. Measured against this model on identical held-out text, BMBT ties it rather than beating it - reported honestly, matching the design's own formal proof that a grammar-constrained subword model cannot beat an unconstrained one on raw token count. BMBT also has a morphology-aware variant that aligns its token boundaries to Bengali's suffix structure (not yet published separately), and a vectorized segmenter that runs at more than twice the throughput of the C regex this model delegates to. See BMBT's model card for the full measured comparison, or the GitHub repository for both tokenizers' code and architecture docs.

Citation

@software{maji_bornomala_tokenizer_2026,
  author  = {Maji, Konko},
  title   = {A Bengali-First, Grapheme-Cluster-Aware Tokenizer with Zero Conjunct Fragmentation},
  year    = {2026},
  note    = {Project Bornomala. Version 0.2},
  url     = {https://github.com/konkomaji/bornomala}
}

License

Apache 2.0. A Project Bornomala release. Founder: Konko Maji (work.konkomaji@gmail.com).