Instructions to use NAMAA-Space/bojji with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Model2Vec
How to use NAMAA-Space/bojji with Model2Vec:
from model2vec import StaticModel model = StaticModel.from_pretrained("NAMAA-Space/bojji") embeddings = model.encode(["It's dangerous to go alone!", "It's a secret to everybody."]) print(embeddings.shape) - sentence-transformers
How to use NAMAA-Space/bojji with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("NAMAA-Space/bojji") sentences = [ "The weather is lovely today.", "It's so sunny outside!", "He drove to the stadium." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] - Notebooks
- Google Colab
- Kaggle
Download tokenizer.json from NAMAA-Space/bojji: direct link, hf CLI and curl.
- Browser
- Download file 18.5 MB
-
https://huggingface.co/NAMAA-Space/bojji/resolve/main/tokenizer.json
- Command line
-
hf download hf://NAMAA-Space/bojji/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/NAMAA-Space/bojji/resolve/main/tokenizer.json
18.5 MB
- Xet hash:
- 5435e15a564e5cb51a9dff8a6ddfa8750209b45e7b71a8390bf1ccd096a308d8
- Size of remote file:
- 18.5 MB
- SHA256:
- 27fefe4ea507582636c2c481a6f8de9cc95289a7c99aabd80e542a01bbd31e1a
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