mai
Maithili рдореИрдерд┐рд▓реА
49,509 Words in vocabulary
4.37x Best compression
0.8575 Best isotropy
Sample text
Excerpts from Maithili Wikipedia articles.
рдПрдХ рдЕрд╡рдзреА рд╡реНрдпрдВрдЬрди рдЫреАред рдПрдХрд░ рдореБрдЦреНрдп рдШрдЯрдХ рдмрд╛рд╕рдорддреА рдЪрд╛рд╡рд▓ рдЫреАред рднрд╛рд░рдд рдХ рдЦрд╛рдирд╛ рдХреЗ рд╡реНрдпрдВрдЬрди
рдПрдХ рдкреВрд░реНрд╡реА рднрд╛рд░рддрдХ рдЙрдбрд╝рд┐рдпрд╛ рд╡реНрдпрдВрдЬрди рдЫреАред рд╕рдиреНрджрд░реНрдн рд╕рд╛рдордЧреНрд░реАрд╕рдн рдмрд╛рд╣реНрдп рдЬрдбреАрд╕рдн рдПрд╣реЛ рд╕рдн рджреЗрдЦреА рдЦрд╛рдирдк...
рдПрдХ рджрдХреНрд╖рд┐рдг рднрд╛рд░рддреАрдп рдЦрд╛рдирд╛ рдЫреАред рд╕рдиреНрджрд░реНрдн рд╕рд╛рдордЧреНрд░реАрд╕рдн рдмрд╛рд╣реНрдп рдЬрдбреАрд╕рдн рдПрд╣реЛ рд╕рдн рджреЗрдЦреА рднрд╛рд░рддреАрдп рдЦрд╛рдирд╛
Most common words
The 20 most frequently used words in Maithili Wikipedia.
Interactive playground
Explore Maithili 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', 'mai', 32000)
tokens = tok.tokenize("Your text here") N-gram
from wikilangs import ngram
ng = ngram('latest', 'mai', gram_size=3)
score = ng.score("Your text here") Markov chain
from wikilangs import markov
mc = markov('latest', 'mai', depth=3)
text = mc.generate(length=50) Vocabulary
from wikilangs import vocabulary
vocab = vocabulary('latest', 'mai')
info = vocab.lookup("word") Embeddings
from wikilangs import embeddings
emb = embeddings('latest', 'mai', 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.
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