Text Generation
fastText
Bihari
wikilangs
nlp
tokenizer
embeddings
n-gram
markov
wikipedia
feature-extraction
sentence-similarity
tokenization
n-grams
markov-chain
text-mining
babelvec
vocabulous
vocabulary
monolingual
family-indoaryan_central
Instructions to use wikilangs/bh with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- fastText
How to use wikilangs/bh with fastText:
from huggingface_hub import hf_hub_download import fasttext model = fasttext.load_model(hf_hub_download("wikilangs/bh", "model.bin")) - Notebooks
- Google Colab
- Kaggle
Download visualizations/zipf_law.png from wikilangs/bh: direct link, hf CLI and curl.
- Browser
- Download file 106 kB
-
https://huggingface.co/wikilangs/bh/resolve/main/visualizations/zipf_law.png
- Command line
-
hf download hf://wikilangs/bh/visualizations/zipf_law.png
-
curl -L -o zipf_law.png https://huggingface.co/wikilangs/bh/resolve/main/visualizations/zipf_law.png
106 kB

- Xet hash:
- 606bbd6b7cd5d87ce43b2256af1be40a8bda49f59016fe197cd182654e6cd35a
- Size of remote file:
- 106 kB
- SHA256:
- 780906e4f325e65d4156e1845467327b3c605ab6a89f70405476c41aa4d4cb8d
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.