dty

Dotyali рдбреЛрдЯреЗрд▓реА

ISO 639-3: dty I L
32,797 Words in vocabulary
4.54x Best compression
0.9032 Best isotropy

Sample text

Excerpts from Dotyali Wikipedia articles.

рд╕реБрдЦрд╡рд┐рдВрджрд░ рд╕рд┐рдВрд╣ рднрд╛рд░рддреАрдп рд╕рд╛рдВрдЧреАрддрд┐рдХ рдХреНрд╖реЗрддреНрд░рдХрд╛ рдкрд╛рд╢реНрд╡ рдЧрд╛рдпрдХ рд╣реБрдиред рд╕рдиреНрджрд░реНрдн рдЧрд┐рджрд╛рд░рд╛рдЕрди
рд╕рд┐рдВрдЧреМрдбреА рджреИрд▓реЗрдЦ рдЬрд┐рд▓реНрд▓рд╛рдореА рдкрдбрдбреЗ рдПрдХ рдЧрд╛рдК рд╡рд┐рдХрд╛рд╕ рд╕рдорд┐рддрд┐ рд╣реЛ ред рдпреА рдкрдирд┐ рд╣реЗрд░ рдЬрд┐рд▓реНрд▓рд╛ рд╡рд┐рдХрд╛рд╕ рд╕рдорд┐рдд...
рдмреЗрдирд┐рди рдЕрдлреНрд░рд┐рдХрд╛ рдорд╣рд╛рджреНрд╡реАрдкрдорд╛рдИ рд░рдпрд╛рдХреЛ рдПрдХ рджреЗрд╢ рд╣реЛред рд╕рдиреНрджрд░реНрдн рджреЗрд╢рдЕрди

Most common words

The 20 most frequently used words in Dotyali Wikipedia.

Top 20 words in Dotyali

Performance dashboard

Key metrics for all model types at a glance.

Performance dashboard for Dotyali

Quick start

Tokenizer

from wikilangs import tokenizer
tok = tokenizer('latest', 'dty', 32000)
tokens = tok.tokenize("Your text here")

N-gram

from wikilangs import ngram
ng = ngram('latest', 'dty', gram_size=3)
score = ng.score("Your text here")

Markov chain

from wikilangs import markov
mc = markov('latest', 'dty', depth=3)
text = mc.generate(length=50)

Vocabulary

from wikilangs import vocabulary
vocab = vocabulary('latest', 'dty')
info = vocab.lookup("word")

Embeddings

from wikilangs import embeddings
emb = embeddings('latest', 'dty', dimension=64)
vec = emb.embed_word("word")

Available models

Model Type Variants Description
Tokenizers8k, 16k, 32k, 64kBPE tokenizers with different vocabulary sizes
N-gram (Word)2, 3, 4, 5-gramWord-level language models
N-gram (Subword)2, 3, 4, 5-gramSubword-level language models
Markov (Word)Depth 1тАУ5Word-level text generation
Markov (Subword)Depth 1тАУ5Subword-level text generation
VocabularyтАФWord dictionary with frequency and IDF
Embeddings32d, 64d, 128dPosition-aware word embeddings

Model evaluation

Tokenizer performance

Compression ratios and token statistics across vocabulary sizes.

Tokenizer compression

N-gram evaluation

Perplexity and entropy metrics across n-gram sizes.

N-gram perplexity

Markov chain evaluation

Entropy and branching factor by context depth.

Markov entropy

Vocabulary analysis

Word frequency distribution and Zipf's law analysis.

Zipf's law
Top 20 words

Embeddings evaluation

Isotropy and vector space quality metrics.

Embedding isotropy

Full research report

Access the complete ablation study with all metrics, visualizations, and generated text samples on HuggingFace.

View on HuggingFace тЖТ