wuu
Wu Chinese 吴语
32,292 Words in vocabulary
2.14x Best compression
0.6410 Best isotropy
Sample text
Excerpts from Wu Chinese Wikipedia articles.
感觉系统（英语：sensory system）是神经系统中处理感觉信息个一部分。感觉系统包括感受器、神经通路搭子大脑中搭感觉知觉有关个部分。
大事记 明代宗为了筹募经费而开始贩卖度牒，直到明末，导致僧尼剧增，寺院林立。 德里苏丹国赛义德王朝锡林德总督巴赫鲁尔·洛迪佔据了德里，赛义德王朝被洛迪王朝取代。...
吉兰丹州（）是马来西亚拉西马北部个一個州，首府為哥打峇鲁。該州北接泰国，东北为南中国海，西接霹雳州，南临彭亨州，东南为登嘉樓州。吉兰丹国号为Darul Naim...
Most common words
The 20 most frequently used words in Wu Chinese Wikipedia.
Interactive playground
Explore Wu Chinese 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', 'wuu', 32000)
tokens = tok.tokenize("Your text here")N-gram
from wikilangs import ngram
ng = ngram('latest', 'wuu', gram_size=3)
score = ng.score("Your text here")Markov chain
from wikilangs import markov
mc = markov('latest', 'wuu', depth=3)
text = mc.generate(length=50)Vocabulary
from wikilangs import vocabulary
vocab = vocabulary('latest', 'wuu')
info = vocab.lookup("word")Embeddings
from wikilangs import embeddings
emb = embeddings('latest', 'wuu', 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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