Text Generation
fastText
Ruthenian
wikilangs
nlp
tokenizer
embeddings
n-gram
markov
wikipedia
feature-extraction
sentence-similarity
tokenization
n-grams
markov-chain
text-mining
babelvec
vocabulous
vocabulary
monolingual
family-slavic_south
Instructions to use wikilangs/rsk with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- fastText
How to use wikilangs/rsk with fastText:
from huggingface_hub import hf_hub_download import fasttext model = fasttext.load_model(hf_hub_download("wikilangs/rsk", "model.bin")) - Notebooks
- Google Colab
- Kaggle

- Xet hash:
- 808dd59cfd2d0170663bfa5c81ab87088da24646a04214debd1b3b5f2be2fcd5
- Size of remote file:
- 149 kB
- SHA256:
- 0107c5cb282a98179cff74ebd9662aa72b40c6a0bbe01bebaa03ebcc67e22172
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.