bn
Bangla āĻŦāĻžāĻāĻ˛āĻž
838,913 Words in vocabulary
2 Countries
1 Continent
5.04x Best compression
0.8095 Best isotropy
Asia
Spoken in
* primarySample text
Excerpts from Bangla Wikipedia articles.
āĻā§āĻāĻĄāĻŧāĻŋāĻ¯āĻŧāĻž āĻā§āĻˇā§āĻāĻŋāĻ¯āĻŧāĻž āĻļāĻšāĻ°ā§āĻ° āĻĒā§āĻ°ā§āĻŦ āĻĻāĻŋāĻā§ āĻ āĻŦāĻ¸ā§āĻĨāĻŋāĻ¤ āĻāĻāĻāĻŋ āĻāĻ˛āĻžāĻāĻžāĨ¤ āĻ˛āĻžāĻ˛āĻ¨ āĻļāĻžāĻšā§āĻ° āĻŽāĻžāĻāĻžāĻ° āĻāĻ āĻā§āĻ...
āĻŦāĻ¨ā§ āĻā§āĻ¨āĻžāĻ¨āĻžāĻš () āĻšāĻ˛ āĻāĻ°ā§āĻĄāĻžāĻ¨ā§āĻ° āĻāĻ°āĻŦāĻŋāĻĄ āĻāĻāĻ°ā§āĻ¨āĻ°ā§āĻā§āĻ° āĻāĻāĻāĻŋ āĻā§āĻ˛āĻžāĨ¤ āĻ¤āĻĨā§āĻ¯āĻ¸ā§āĻ¤ā§āĻ° āĻā§āĻ˛āĻž
āĻāĻĒāĻāĻžāĻˇāĻžāĻ¤āĻ¤ā§āĻ¤ā§āĻŦ () āĻāĻžāĻˇāĻžāĻŦāĻŋāĻā§āĻāĻžāĻ¨ā§āĻ° āĻāĻāĻāĻŋ āĻāĻĒāĻļāĻžāĻāĻž āĻ¯ā§āĻāĻžāĻ¨ā§ āĻāĻžāĻˇāĻžāĻ° āĻā§āĻā§āĻ˛āĻŋāĻ āĻŦā§āĻāĻŋāĻ¤ā§āĻ°ā§āĻ¯ āĻ¨āĻŋāĻ¯āĻŧā§ āĻ...
Most common words
The 20 most frequently used words in Bangla Wikipedia.
Interactive playground
Explore Bangla 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', 'bn', 32000)
tokens = tok.tokenize("Your text here")N-gram
from wikilangs import ngram
ng = ngram('latest', 'bn', gram_size=3)
score = ng.score("Your text here")Markov chain
from wikilangs import markov
mc = markov('latest', 'bn', depth=3)
text = mc.generate(length=50)Vocabulary
from wikilangs import vocabulary
vocab = vocabulary('latest', 'bn')
info = vocab.lookup("word")Embeddings
from wikilangs import embeddings
emb = embeddings('latest', 'bn', 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 1â5 | Word-level text generation |
| Markov (Subword) | Depth 1â5 | Subword-level text generation |
| Vocabulary | â | 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 â