Text Generation
fastText
Bambara
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_other
Instructions to use wikilangs/bm with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- fastText
How to use wikilangs/bm with fastText:
from huggingface_hub import hf_hub_download import fasttext model = fasttext.load_model(hf_hub_download("wikilangs/bm", "model.bin")) - Notebooks
- Google Colab
- Kaggle

- Xet hash:
- b6dc2eac331f0db3c81e6b2efaae8bea4856772f17f954ba39786fe68bdc5202
- Size of remote file:
- 248 kB
- SHA256:
- 9dfb8be1202f031b2af56dcf0a4d48f78cc1390328cf3ad321fbc237b2059a1b
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.