Arabic العربية
Africa, Asia
Spoken in
* primaryAlgeria* (ar), Egypt* (ar), Iraq* (ar), Jordan* (ar), Kuwait* (ar), Lebanon* (ar), Libya* (ar), Mauritania* (ar), Morocco* (ar), Oman* (ar), Qatar* (ar), Saudi Arabia* (ar), Somalia* (ar), Sudan* (ar), Syria* (ar), Tunisia* (ar), United Arab Emirates* (ar), Yemen* (ar), Djibouti (ar)
Sample text
Excerpts from Arabic Wikipedia articles.
استوديوهات أفلام والت ديزني أفلام والت ديزني منتجع والت ديزني العالمي ديزني لاند…
باسكال قد تعني: الباسكال، وحدة قياس الضغط لغة باسكال، لغة برمجة الفيلسوف باسكال،…
جمهورية الكونغو الديمقراطية، زائير سابقًا، عاصمتها كينشاسا. جمهورية الكونغو، عاص…
Most common words
The 20 most frequently used words in Arabic Wikipedia.
Interactive playground
Explore Arabic 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', 'ar', 32000)
tokens = tok.tokenize("Your text here")N-gram
from wikilangs import ngram
ng = ngram('latest', 'ar', gram_size=3)
score = ng.score("Your text here")Markov chain
from wikilangs import markov
mc = markov('latest', 'ar', depth=3)
text = mc.generate(length=50)Vocabulary
from wikilangs import vocabulary
vocab = vocabulary('latest', 'ar')
info = vocab.lookup("word")Embeddings
from wikilangs import embeddings
emb = embeddings('latest', 'ar', 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
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