Sentence Similarity
sentence-transformers
Safetensors
feature-extraction
dense
Generated from Trainer
dataset_size:5424
loss:MultipleNegativesRankingLoss
Instructions to use Neelkumar/my-embedding-gemma-5424 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use Neelkumar/my-embedding-gemma-5424 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("Neelkumar/my-embedding-gemma-5424") sentences = [ "What does the Competition Bureau do?", "What are the requirements for obtaining a Canadian passport?", "The Competition Bureau is an independent law enforcement agency that protects and promotes competition for the benefit of Canadian consumers and businesses.", "Failure to file an annual or interim management’s discussion and analysis (MD&A) or an annual or interim management report of fund performance (MRFP) is a common failure." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
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