hyw
Western Armenian Արեւմտահայերէն
163,300 Words in vocabulary
4.07x Best compression
0.8364 Best isotropy
Sample text
Excerpts from Western Armenian Wikipedia articles.
թուական, ոչ նահանջ տարի, 17րդ դարու 89րդ տարին է Դէպքեր Ծնունդներ 16px 120px Մոն...
թուական, ոչ նահանջ տարի, 17րդ դարու 69րդ տարին է Դէպքեր Անծանօթ ամսաթիւով՝ Օսման...
թուական, ոչ նահանջ տարի, 16րդ դարու 70րդ տարին է Դէպքեր Ծնունդներ 16px 120px Սեպ...
Most common words
The 20 most frequently used words in Western Armenian Wikipedia.
Interactive playground
Explore Western Armenian interactively with browser-based demos.
Performance dashboard
Key metrics for all model types at a glance.
Quick start
Tokenizer
from wikilangs import tokenizer
tok = tokenizer('latest', 'hyw', 32000)
tokens = tok.tokenize("Your text here")N-gram
from wikilangs import ngram
ng = ngram('latest', 'hyw', gram_size=3)
score = ng.score("Your text here")Markov chain
from wikilangs import markov
mc = markov('latest', 'hyw', depth=3)
text = mc.generate(length=50)Vocabulary
from wikilangs import vocabulary
vocab = vocabulary('latest', 'hyw')
info = vocab.lookup("word")Embeddings
from wikilangs import embeddings
emb = embeddings('latest', 'hyw', dimension=64)
vec = emb.embed_word("word")Available models
| Model Type | Variants | Description |
|---|---|---|
| Tokenizers | 8k, 16k, 32k, 64k | BPE tokenizers with different vocabulary sizes |
| N-gram (Word) | 2, 3, 4, 5-gram | Word-level language models |
| N-gram (Subword) | 2, 3, 4, 5-gram | Subword-level language models |
| Markov (Word) | Depth 1–5 | Word-level text generation |
| Markov (Subword) | Depth 1–5 | Subword-level text generation |
| Vocabulary | — | Word dictionary with frequency and IDF |
| Embeddings | 32d, 64d, 128d | Position-aware word embeddings |
Model evaluation
Tokenizer performance
Compression ratios and token statistics across vocabulary sizes.
N-gram evaluation
Perplexity and entropy metrics across n-gram sizes.
Markov chain evaluation
Entropy and branching factor by context depth.
Vocabulary analysis
Word frequency distribution and Zipf's law analysis.
Embeddings evaluation
Isotropy and vector space quality metrics.
Full research report
Access the complete ablation study with all metrics, visualizations, and generated text samples on HuggingFace.
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