Text Generation
fastText
Gagauz
wikilangs
nlp
tokenizer
embeddings
n-gram
markov
wikipedia
feature-extraction
sentence-similarity
tokenization
n-grams
markov-chain
text-mining
babelvec
vocabulous
vocabulary
monolingual
family-turkic_oghuz
Instructions to use wikilangs/gag with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- fastText
How to use wikilangs/gag with fastText:
from huggingface_hub import hf_hub_download import fasttext model = fasttext.load_model(hf_hub_download("wikilangs/gag", "model.bin")) - Notebooks
- Google Colab
- Kaggle
Download visualizations/embedding_tsne_multilingual.png from wikilangs/gag: direct link, hf CLI and curl.
- Browser
- Download file 245 kB
-
https://huggingface.co/wikilangs/gag/resolve/main/visualizations/embedding_tsne_multilingual.png
- Command line
-
hf download hf://wikilangs/gag/visualizations/embedding_tsne_multilingual.png
-
curl -L -o embedding_tsne_multilingual.png https://huggingface.co/wikilangs/gag/resolve/main/visualizations/embedding_tsne_multilingual.png
245 kB

- Xet hash:
- d1afae66375f6ec906530c060c19cf3bb632ccfb9c88afc85367151206365567
- Size of remote file:
- 245 kB
- SHA256:
- b431b34dee345fb51fca5bd7fee55d2f2845011e54bb2b937e0df0e7bce2d549
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