openpecha / BoKenlm-syl

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Model's Last Updated: March 06 2026

Introduction of BoKenlm-syl

Model Details of BoKenlm-syl

BoKenlm-syl - Tibetan KenLM Language Model

A KenLM n-gram language model trained on Tibetan text, tokenized with syllable tokenizer.

Model Details
Parameter Value
Model Type Modified Kneser-Ney 5-gram
Tokenizer Tibetan syllable-based (split on tseg / shad )
Training Corpus bo_corpus.txt
Pruning 0 0 1
Tokens 62,199,201
Vocabulary Size 101,462
N-gram Statistics
Order Count D1 D2 D3+
1 101,462 0.7273 0.9932 1.2615
2 1,656,776 0.7098 1.0359 1.3537
3 3,201,003 0.7505 1.0850 1.3624
4 5,576,668 0.8147 1.1485 1.3783
5 6,215,679 0.7784 1.2430 1.4828
Memory Estimates
Type MB Details
probing 348 assuming -p 1.5
probing 408 assuming -r models -p 1.5
trie 165 without quantization
trie 90 assuming -q 8 -b 8 quantization
trie 146 assuming -a 22 array pointer compression
trie 71 assuming -a 22 -q 8 -b 8 array pointer compression and quantization
Training Resources
Metric Value
Peak Virtual Memory 12,333 MB
Peak RSS 2,981 MB
Wall Time 46.4s
User Time 45.4s
System Time 15.7s
Usage
import kenlm

model = kenlm.Model("BoKenlm-syl.arpa")

# Score a tokenized sentence
score = model.score("བོད་ སྐད་ ཀྱི་ ཚིག་ གྲུབ་ འདི་ ཡིན།")
print(score)
Files
  • BoKenlm-syl.arpa — ARPA format language model
  • README.md — This model card
License

Apache 2.0

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