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

- Xet hash:
- becd496af26bf1d6b84724f2b960439a73454fbb7192f0ad4bfa8f968fddf3ee
- Size of remote file:
- 147 kB
- SHA256:
- 757f90838805e41815509ad9a19fab9e3fd34977cd4c52a06b6576364bd7f208
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.