Sentence Similarity
sentence-transformers
Safetensors
bert
feature-extraction
dense
Generated from Trainer
dataset_size:529974
loss:MultipleNegativesSymmetricRankingLoss
Eval Results (legacy)
text-embeddings-inference
Instructions to use LamaDiab/MiniLM-V21Data-256ConstantBATCH-SemanticEngine with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use LamaDiab/MiniLM-V21Data-256ConstantBATCH-SemanticEngine with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("LamaDiab/MiniLM-V21Data-256ConstantBATCH-SemanticEngine") sentences = [ "essence multi task concealer 15 natural nude", "ahc vitamin c sheet mask", " concealer", "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 2
Browse files- eval/triplet_evaluation_results.csv +2 -0
- model.safetensors +1 -1
eval/triplet_evaluation_results.csv
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0.48285852245292127,1000,0.9400568008422852
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0.9657170449058425,2000,0.9479440450668335
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epoch,steps,accuracy_cosine
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0.48285852245292127,1000,0.9400568008422852
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1.448142788229619,3000,0.9563571214675903
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1.930535455861071,4000,0.9602481722831726
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model.safetensors
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size 90864192
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