zgh
Standard Moroccan Tamazight โตโดฐโตโดฐโตฃโตโตโต โตโดฐโตโดฐโตกโดฐโตขโต
35,191 Words in vocabulary
3.84x Best compression
0.7259 Best isotropy
Sample text
Excerpts from Standard Moroccan Tamazight Wikipedia articles.
thumb โดฑโต โดฑโต โตโต โตโต BBC (โต โตโตโดณโตโตโตฃโต: British Broadcasting Corporation) โตโตโดฐโตโตโตโต
โดฐโดณโดฐโดทโดฐโตฃ โดฐโดผโตโดฐโตโตโตโต โตโดณโดฐ โดฐโดณโดทโตโตฃ โดท โดฐโตโดทโดทโต โต โตกโดฐโตโตโตโตฃโต โดณ โตโดฐโดทโดทโตโตโต โตโดฐโดผโตโดฐโตโตโตโตโต, โต โตโตโตโตขโดฐโตโตฃ โดฐโตโดฐโตข...
โตโดฑโดทโตโดผโตโตโดฐโต โตโตโตโตโต (โต โตโดฐโตโตโดฐโดฑโต: ุนุจุฏ ุงููุชุงุญ ุงูุณูุณู), โตโตโตโต โดณ 19 โตโตโตกโดฐโตโดฑโตโต โดณ โตโตโดฐโตโตโตโต, โตโดณ...
Most common words
The 20 most frequently used words in Standard Moroccan Tamazight Wikipedia.
Interactive playground
Explore Standard Moroccan Tamazight 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', 'zgh', 32000)
tokens = tok.tokenize("Your text here")N-gram
from wikilangs import ngram
ng = ngram('latest', 'zgh', gram_size=3)
score = ng.score("Your text here")Markov chain
from wikilangs import markov
mc = markov('latest', 'zgh', depth=3)
text = mc.generate(length=50)Vocabulary
from wikilangs import vocabulary
vocab = vocabulary('latest', 'zgh')
info = vocab.lookup("word")Embeddings
from wikilangs import embeddings
emb = embeddings('latest', 'zgh', 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 1โ5 | Word-level text generation |
| Markov (Subword) | Depth 1โ5 | Subword-level text generation |
| Vocabulary | โ | 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 โ