Sentence Similarity
sentence-transformers
Safetensors
bert
feature-extraction
dense
Generated from Trainer
dataset_size:790993
loss:MultipleNegativesSymmetricRankingLoss
Eval Results (legacy)
text-embeddings-inference
Instructions to use LamaDiab/MiniLM-v27-SemanticEngine with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use LamaDiab/MiniLM-v27-SemanticEngine with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("LamaDiab/MiniLM-v27-SemanticEngine") sentences = [ "essence multi task concealer 15 natural nude", "face make-up", "adidas men shower gel 3 in 1", "health_beauty", "beauty", " essence multi task concealer" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [6, 6] - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 9cdd5e53288e04708fd99579b45c2a9e5eaef3f724c792d4578a2a712368de6e
- Size of remote file:
- 5.75 kB
- SHA256:
- aa19b6890fc6323f4d5c5fcafe606e739daa13381207bc69ee6720f8602f7e66
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