Sentence Similarity
sentence-transformers
Safetensors
bert
feature-extraction
dense
Generated from Trainer
dataset_size:902672
loss:MultipleNegativesSymmetricRankingLoss
Eval Results (legacy)
text-embeddings-inference
Instructions to use LamaDiab/MiniLM-v33-SemanticEngine with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use LamaDiab/MiniLM-v33-SemanticEngine with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("LamaDiab/MiniLM-v33-SemanticEngine") sentences = [ "dove - nourishing body care moisturizing beauty cream - 75 ml", "body moisturizer", " moisturizing body cream", "skincare set game" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
Training in progress, epoch 2
Browse files- eval/triplet_evaluation_results.csv +3 -0
- model.safetensors +1 -1
eval/triplet_evaluation_results.csv
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1.4173986965145935,5000,0.9743291735649109
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1.7007650892604138,6000,0.9738009572029114
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1.9841314820062341,7000,0.9742235541343689
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1.4173986965145935,5000,0.9743291735649109
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1.7007650892604138,6000,0.9738009572029114
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1.9841314820062341,7000,0.9742235541343689
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2.2674978747520544,8000,0.9735897183418274
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2.5508642674978748,9000,0.9734840393066406
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2.834230660243695,10000,0.9740122556686401
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model.safetensors
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size 90864192
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