Text Generation
fastText
Hebrew
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_hebrew
Instructions to use wikilangs/he with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- fastText
How to use wikilangs/he with fastText:
from huggingface_hub import hf_hub_download import fasttext model = fasttext.load_model(hf_hub_download("wikilangs/he", "model.bin")) - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 2df7a9a38a4b0e7042cf58e9f0a578614a9b8f1dd93ea8bd86aeacef9279983e
- Size of remote file:
- 1.58 MB
- SHA256:
- 80b760144f1e0828c8af7d47c300ab316d7c76c2e634af752ad12a514e0ef38d
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.