Sentence Similarity
sentence-transformers
Safetensors
bert
feature-extraction
dense
Generated from Trainer
dataset_size:902990
loss:MultipleNegativesSymmetricRankingLoss
Eval Results (legacy)
text-embeddings-inference
Instructions to use LamaDiab/MiniLM-v2-v32-SemanticEngine with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use LamaDiab/MiniLM-v2-v32-SemanticEngine with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("LamaDiab/MiniLM-v2-v32-SemanticEngine") sentences = [ "dove deodorant stick fresh", "women's deodorant", "antiperspirant deodorant stick", "bubbles natur.bottle w.hand pink(6)m280m" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 49c4c8684547663da4ad984bfe8753f0a36a52294a3be3615b484c40e38cdf37
- Size of remote file:
- 5.75 kB
- SHA256:
- 916676aa662409e3b1685d44fe5feb5a2d5c5b2107c6b5707018cc75e0872705
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