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

- Xet hash:
- 530d1915631923372303e29bc411797e4f33501000301670a2fecfa3416c5087
- Size of remote file:
- 270 kB
- SHA256:
- 4ede07cf67b40c577dd73120546926517c2d41644aa4d9e8598261b343319d70
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.