Text Generation
fastText
Hawaiian
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_polynesian
Instructions to use wikilangs/haw with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- fastText
How to use wikilangs/haw with fastText:
from huggingface_hub import hf_hub_download import fasttext model = fasttext.load_model(hf_hub_download("wikilangs/haw", "model.bin")) - Notebooks
- Google Colab
- Kaggle
Download visualizations/position_encoding_comparison.png from wikilangs/haw: direct link, hf CLI and curl.
- Browser
- Download file 121 kB
-
https://huggingface.co/wikilangs/haw/resolve/main/visualizations/position_encoding_comparison.png
- Command line
-
hf download hf://wikilangs/haw/visualizations/position_encoding_comparison.png
-
curl -L -o position_encoding_comparison.png https://huggingface.co/wikilangs/haw/resolve/main/visualizations/position_encoding_comparison.png
121 kB

- Xet hash:
- 537cc7cf92d1a155ea4d9561d0debc62cb7b4999255956c59c3cef2ee1305728
- Size of remote file:
- 121 kB
- SHA256:
- c4fe1b625ba6cb7ad7f038ee0cb65e86e2e981ea5f52946c928e9e8ab95def32
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.