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

- Xet hash:
- 7b6d312922562a34326c36245c3d09bf97d7c6f9a944734bc5fda334bdf5bac4
- Size of remote file:
- 137 kB
- SHA256:
- 8bfb68bae359eb9dd5994e65b6bb9fc02c195b48c8d53f352a072393161d6308
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.