Text Generation
fastText
Vlax Romani
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_romani
Instructions to use wikilangs/rmy with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- fastText
How to use wikilangs/rmy with fastText:
from huggingface_hub import hf_hub_download import fasttext model = fasttext.load_model(hf_hub_download("wikilangs/rmy", "model.bin")) - Notebooks
- Google Colab
- Kaggle

- Xet hash:
- 0597741b8d84fcd5a9be7a88e8b58df90b41214441cc0d7f1c55c3650bec7350
- Size of remote file:
- 369 kB
- SHA256:
- ee6f0331e85a5a2b6607bb33ec1fd50b273123a64f7a03ca03308c36a0b14ec8
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.