sentence-transformers
Safetensors
bert
embeddings
retrieval
northeast-india
low-resource
multilingual
RAG
Instructions to use MWirelabs/ne-embed with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use MWirelabs/ne-embed with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("MWirelabs/ne-embed") 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
- Xet hash:
- b31b253cae103b71c3d102d22159a742c26a5ea337cf7940f8c349e93daa99b8
- Size of remote file:
- 1.88 GB
- SHA256:
- c702259539c45b1fec2e11ddb9d7f58b04cb51ae5115de4ef2cbbdaf369e190b
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.