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

- Xet hash:
- 032a3271f9479b99bda60533f5d9e62893e18d6fa875a296aec84371b7e68dbc
- Size of remote file:
- 266 kB
- SHA256:
- 0bc9a7f6720b2687cb1adde85016e380d73903a575cd9ce9c8716cd6ba3d2d86
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.