Sentence Similarity
sentence-transformers
Safetensors
bert
feature-extraction
dense
Generated from Trainer
dataset_size:647236
loss:MultipleNegativesSymmetricRankingLoss
Eval Results (legacy)
text-embeddings-inference
Instructions to use LamaDiab/MiniLM-V22Data-256ConstantBATCH-SemanticEngine with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use LamaDiab/MiniLM-V22Data-256ConstantBATCH-SemanticEngine with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("LamaDiab/MiniLM-V22Data-256ConstantBATCH-SemanticEngine") sentences = [ "essence multi task concealer 15 natural nude", "pure oxygen 20 vol", "essence", "face make-up" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
Training in progress, epoch 3
Browse files- eval/triplet_evaluation_results.csv +2 -0
- model.safetensors +1 -1
eval/triplet_evaluation_results.csv
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1.1860924535756618,3000,0.9593017101287842
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1.581193204267088,4000,0.962666928768158
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1.9762939549585146,5000,0.9638237357139587
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1.1860924535756618,3000,0.9593017101287842
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1.9762939549585146,5000,0.9638237357139587
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2.3713947056499407,6000,0.9671889543533325
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2.7664954563413673,7000,0.9656115174293518
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model.safetensors
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size 90864192
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