Sentence Similarity
sentence-transformers
Safetensors
bert
feature-extraction
Generated from Trainer
dataset_size:1115700
loss:MatryoshkaLoss
loss:MultipleNegativesRankingLoss
Eval Results (legacy)
text-embeddings-inference
Instructions to use Mollel/MultiLinguSwahili-bge-small-en-v1.5-nli-matryoshka with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use Mollel/MultiLinguSwahili-bge-small-en-v1.5-nli-matryoshka with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("Mollel/MultiLinguSwahili-bge-small-en-v1.5-nli-matryoshka") sentences = [ "Ndege mwenye mdomo mrefu katikati ya ndege.", "Panya anayekimbia juu ya gurudumu.", "Mtu anashindana katika mashindano ya mbio.", "Ndege anayeruka." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle