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Introduction of BoKenlm-syl-v0.1

Model Details of BoKenlm-syl-v0.1

BoKenlm-syl-v0.1 - 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 (botok-rs SimpleTokenizer)
Training Corpus bo_corpus.txt
Pruning 0 0 1
Tokens 153,703,256
Vocabulary Size 105,195
N-gram Statistics
Order Count D1 D2 D3+
1 105,195 0.7318 1.0392 1.3101
2 2,503,807 0.6749 1.0227 1.3576
3 6,956,090 0.7422 1.0862 1.3739
4 13,423,388 0.8156 1.1560 1.3755
5 16,307,695 0.7422 1.2835 1.5109
Memory Estimates
Type MB Details
probing 806 assuming -p 1.5
probing 937 assuming -r models -p 1.5
trie 379 without quantization
trie 206 assuming -q 8 -b 8 quantization
trie 334 assuming -a 22 array pointer compression
trie 161 assuming -a 22 -q 8 -b 8 array pointer compression and quantization
Training Resources
Metric Value
Peak Virtual Memory 12,333 MB
Peak RSS 8,008 MB
Wall Time 88.5s
User Time 88.9s
System Time 26.3s
Usage
import kenlm

model = kenlm.Model("BoKenlm-syl-v0.1.arpa")

# Score a tokenized sentence
score = model.score("བོད་ སྐད་ ཀྱི་ ཚིག་ གྲུབ་ འདི་ ཡིན།")
print(score)
Files
  • BoKenlm-syl-v0.1.arpa — ARPA format language model
  • README.md — This model card
License

Apache 2.0

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