English
North America, Europe, Oceania, Africa, Asia
Spoken in
* primaryAustralia* , Canada* , Ireland* , New Zealand* , South Africa* , United Kingdom* , United States* , Ghana , India , Jamaica , Kenya , Malaysia , Malta , Nigeria , Philippines , Singapore , Trinidad and Tobago
Sample text
Excerpts from English Wikipedia articles.
Albrecht Achilles may refer to: Albrecht III Achilles, Elector of Brandenburg Al…
Alexander V may refer to: Alexander V of Macedon (died 294 BCE) Antipope Alexand…
Two antipopes used the regnal name Victor IV: Antipope Victor IV Antipope Victor…
Most common words
The 20 most frequently used words in English Wikipedia.
Interactive playground
Explore English 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', 'en', 32000)
tokens = tok.tokenize("Your text here")N-gram
from wikilangs import ngram
ng = ngram('latest', 'en', gram_size=3)
score = ng.score("Your text here")Markov chain
from wikilangs import markov
mc = markov('latest', 'en', depth=3)
text = mc.generate(length=50)Vocabulary
from wikilangs import vocabulary
vocab = vocabulary('latest', 'en')
info = vocab.lookup("word")Embeddings
from wikilangs import embeddings
emb = embeddings('latest', 'en', 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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