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
File size: 553 Bytes
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{
"idx": 0,
"name": "0",
"path": "",
"type": "sentence_transformers.base.modules.transformer.Transformer"
},
{
"idx": 1,
"name": "1",
"path": "1_Pooling",
"type": "sentence_transformers.sentence_transformer.modules.pooling.Pooling"
},
{
"idx": 2,
"name": "2",
"path": "2_Dense",
"type": "sentence_transformers.base.modules.dense.Dense"
},
{
"idx": 3,
"name": "3",
"path": "3_Normalize",
"type": "sentence_transformers.sentence_transformer.modules.normalize.Normalize"
}
] |