Text Generation
fastText
Mossi
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/mos with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- fastText
How to use wikilangs/mos with fastText:
from huggingface_hub import hf_hub_download import fasttext model = fasttext.load_model(hf_hub_download("wikilangs/mos", "model.bin")) - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- b6ee6e8819fdafc8646a8a52e87872e43147ed975f795ec3fbd75d98ac7fd391
- Size of remote file:
- 773 kB
- SHA256:
- 4b426b07baf8907c24b723975b769a2d658b65e89ae93e3b4425bf6a46553abc
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.