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

- Xet hash:
- 2c0a9f6b0224b1c3e08f9430e5f694e6d04440920856fd6bdcea54a3a8f5f4a3
- Size of remote file:
- 109 kB
- SHA256:
- eda3311632ac46d18dff33fd7ffae78969a407b4d33fc43cbd715fe635cff93a
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.