got
Gothic ๐ฒ๐ฟ๐๐น๐๐บ
10,445 Words in vocabulary
2.88x Best compression
0.1831 Best isotropy
Sample text
Excerpts from Gothic Wikipedia articles.
๐บ๐ฐ๐ฝ๐ฐ๐ณ๐ฐ ๐น๐๐ ๐ป๐ฐ๐ฝ๐ณ ๐ฐ๐ฝ๐ฐ ๐ฐ๐น๐๐ธ๐ฐ๐ณ๐ฐ๐น๐ป๐ฐ๐น ๐ฝ๐ฐ๐ฟ๐๐ธ๐ฐ๐ผ๐ฐ๐น๐๐น๐บ๐ฐ ๐พ๐ฐ๐ท ๐ฒ๐ฐ๐ผ๐ฐ๐๐บ๐๐ธ ๐ฒ๐ฐ๐ฒ๐ฐ๐ท๐ฐ๐๐๐น๐ณ๐ฐ ๐๐ด๐น๐บ๐พ๐ฐ๐น. ...
๐ฐ๐๐ป๐ โ ๐ฐ๐บ๐๐ฐ๐ฝ ๐ฐ๐๐ป๐ฐ๐ฑ๐ฐ๐ฒ๐ผ๐ด ๐พ๐ฐ๐ท ๐ ๐ฐ๐น๐ป๐ฐ๐บ๐ฟ๐ฝ๐ธ๐ฐ ๐๐๐ณ๐ด๐น๐ฝ๐ ๐น๐๐ยท
๐บ๐ฐ๐ฟ๐ป๐ฟ๐ผ๐ฑ๐พ๐ฐ (Colombia) ๐น๐๐ ๐ป๐ฐ๐ฝ๐ณ ๐น๐ฝ ๐๐ฟ๐ฝ๐ธ๐๐ฐ๐ฐ๐ผ๐ฐ๐น๐๐น๐บ๐ฐ๐น. ๐ฐ๐ผ๐ด๐๐น๐บ๐ฐ This page is brought t...
Most common words
The 20 most frequently used words in Gothic Wikipedia.
Interactive playground
Explore Gothic 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', 'got', 32000)
tokens = tok.tokenize("Your text here")N-gram
from wikilangs import ngram
ng = ngram('latest', 'got', gram_size=3)
score = ng.score("Your text here")Markov chain
from wikilangs import markov
mc = markov('latest', 'got', depth=3)
text = mc.generate(length=50)Vocabulary
from wikilangs import vocabulary
vocab = vocabulary('latest', 'got')
info = vocab.lookup("word")Embeddings
from wikilangs import embeddings
emb = embeddings('latest', 'got', 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