hi
Hindi рд╣рд┐рдиреНрджреА
503,387 Words in vocabulary
1 Country
1 Continent
4.25x Best compression
0.8141 Best isotropy
Asia
Sample text
Excerpts from Hindi Wikipedia articles.
рдЬреЗрд░реЛрдо рдЗрд╕рд╛рдХ рдлреНрд░реАрдбрдорди рдЕрдореЗрд░рд┐рдХрд╛ рдХреЗ рдкреНрд░рд╕рд┐рджреНрдж рд╡реИрдЬреНрдЮрд╛рдирд┐рдХ рд╣реИрдВред рдореЗрдВ рдЗрдиреНрд╣реЗрдВ рднреМрддрд┐рдХ рд╡рд┐рдЬреНрдЮрд╛рди рдо...
рдорд╡реИрдпрд╛ рд╣рдВрдбрд┐рдпрд╛, рдЗрд▓рд╛рд╣рд╛рдмрд╛рдж, рдЙрддреНрддрд░ рдкреНрд░рджреЗрд╢ рд╕реНрдерд┐рдд рдПрдХ рдЧрд╛рдБрд╡ рд╣реИред рднреВрдЧреЛрд▓ рдЬрдирд╕рд╛рдВрдЦреНрдпрд┐рдХреА рдпрд╛рддрд╛рдпрд╛рдд...
рдорд╛рдзрд╡реА рд╣рд┐рдиреНрджреА рдлрд┐рд▓реНрдореЛрдВ рдХреА рдПрдХ рдкреНрд░рд╕рд┐рджреНрдз рдЕрднрд┐рдиреЗрддреНрд░реА рд╣реИрдВред рд╡реНрдпрдХреНрддрд┐рдЧрдд рдЬреАрд╡рди рдлрд┐рд▓реНрдореА рд╕рдлрд░ рдкреНрд░...
Most common words
The 20 most frequently used words in Hindi Wikipedia.
Interactive playground
Explore Hindi 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', 'hi', 32000)
tokens = tok.tokenize("Your text here") N-gram
from wikilangs import ngram
ng = ngram('latest', 'hi', gram_size=3)
score = ng.score("Your text here") Markov chain
from wikilangs import markov
mc = markov('latest', 'hi', depth=3)
text = mc.generate(length=50) Vocabulary
from wikilangs import vocabulary
vocab = vocabulary('latest', 'hi')
info = vocab.lookup("word") Embeddings
from wikilangs import embeddings
emb = embeddings('latest', 'hi', 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 1тАУ5 | Word-level text generation |
| Markov (Subword) | Depth 1тАУ5 | Subword-level text generation |
| Vocabulary | тАФ | 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.
View on HuggingFace тЖТ