Text Generation
fastText
Twi
wikilangs
nlp
tokenizer
embeddings
n-gram
markov
wikipedia
feature-extraction
sentence-similarity
tokenization
n-grams
markov-chain
text-mining
babelvec
vocabulous
vocabulary
monolingual
family-atlantic_kwa
Instructions to use wikilangs/tw with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- fastText
How to use wikilangs/tw with fastText:
from huggingface_hub import hf_hub_download import fasttext model = fasttext.load_model(hf_hub_download("wikilangs/tw", "model.bin")) - Notebooks
- Google Colab
- Kaggle
Download visualizations/position_encoding_comparison.png from wikilangs/tw: direct link, hf CLI and curl.
- Browser
- Download file 114 kB
-
https://huggingface.co/wikilangs/tw/resolve/main/visualizations/position_encoding_comparison.png
- Command line
-
hf download hf://wikilangs/tw/visualizations/position_encoding_comparison.png
-
curl -L -o position_encoding_comparison.png https://huggingface.co/wikilangs/tw/resolve/main/visualizations/position_encoding_comparison.png
114 kB

- Xet hash:
- d1287a1c3e49ecd00aee304cab3ae3363fdf043340068df4d0e219af814c6004
- Size of remote file:
- 114 kB
- SHA256:
- 9a32237ed3fec198a92b2b46c618cac377ecd19ca25adc47044177cf656220af
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.