Sentence Similarity
sentence-transformers
Safetensors
bert
feature-extraction
dense
Generated from Trainer
dataset_size:30
loss:CosineSimilarityLoss
text-embeddings-inference
Instructions to use maheen9/slo-matching-model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use maheen9/slo-matching-model with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("maheen9/slo-matching-model") sentences = [ "Why are different fuses used in different devices?", "Explain the role of fuses with different ratings in protecting electrical appliances.", "Differentiate between electric energy and electric power.", "Identify causes and effects of circuit overload." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
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