ro

Romanian română

ISO 639-1: ro ISO 639-3: ron I L
1,063,320 Words in vocabulary
2 Countries
1 Continent
4.39x Best compression
0.7633 Best isotropy

Europe

Spoken in

* primary

Moldova* (ro), Romania* (ro)

Sample text

Excerpts from Romanian Wikipedia articles.

Student la Iași este un film românesc din regizat de Iancu Moscu. Prezentare Not...
Dellys (în ) este o comună din provincia Boumerdès, Algeria. Populația comunei e...
Districtul Ghanzi este o unitate administrativă de gradul I a Botswanei. Reședin...

Most common words

The 20 most frequently used words in Romanian Wikipedia.

Top 20 words in Romanian

Performance dashboard

Key metrics for all model types at a glance.

Performance dashboard for Romanian

Quick start

Tokenizer

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

N-gram

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

Markov chain

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

Vocabulary

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

Embeddings

from wikilangs import embeddings
emb = embeddings('latest', 'ro', 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 1–5Word-level text generation
Markov (Subword)Depth 1–5Subword-level text generation
Vocabulary—Word 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.

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