Sentence Similarity
sentence-transformers
Safetensors
bert
feature-extraction
dense
Generated from Trainer
dataset_size:903776
loss:MultipleNegativesSymmetricRankingLoss
Eval Results (legacy)
text-embeddings-inference
Instructions to use LamaDiab/MiniLM-v34-SemanticEngine with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use LamaDiab/MiniLM-v34-SemanticEngine with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("LamaDiab/MiniLM-v34-SemanticEngine") sentences = [ "pink n' yellow", "mirror", "pink mirror", "cups set of 3" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- f9c8aed22e3eb140f3a94ef9ca56cdce01a9e8b8e69c28e0523bf173329dd07c
- Size of remote file:
- 988 Bytes
- SHA256:
- 700d5d9e49145a4e90906139ef10e3ae8da8b85b40b4c7d137a86e7ac0f4aa6b
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