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:
- 35174c72ec6578f97c9117610dff337e292fd6ee532cbabf2e6700ed33c6953f
- Size of remote file:
- 373 kB
- SHA256:
- f812eb174504a46a3df3548ce2b36943ee8709f15da981d20e06bcd090f36da8
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.