Text Generation
fastText
Sundanese
wikilangs
nlp
tokenizer
embeddings
n-gram
markov
wikipedia
feature-extraction
sentence-similarity
tokenization
n-grams
markov-chain
text-mining
babelvec
vocabulous
vocabulary
monolingual
family-austronesian_javanese
Instructions to use wikilangs/su with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- fastText
How to use wikilangs/su with fastText:
from huggingface_hub import hf_hub_download import fasttext model = fasttext.load_model(hf_hub_download("wikilangs/su", "model.bin")) - Notebooks
- Google Colab
- Kaggle
Download visualizations/position_encoding_comparison.png from wikilangs/su: direct link, hf CLI and curl.
- Browser
- Download file 116 kB
-
https://huggingface.co/wikilangs/su/resolve/main/visualizations/position_encoding_comparison.png
- Command line
-
hf download hf://wikilangs/su/visualizations/position_encoding_comparison.png
-
curl -L -o position_encoding_comparison.png https://huggingface.co/wikilangs/su/resolve/main/visualizations/position_encoding_comparison.png
116 kB

- Xet hash:
- 0afbac3ca48b4df4d7a24d5eef3b1d0cad5af4055bf8ca9f4f28c14c8e7bc38c
- Size of remote file:
- 116 kB
- SHA256:
- 35918c74cebdac570a60f073df66c8e72f956b8c873089033e7d8809bcd0dd93
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.