Sentence Similarity
sentence-transformers
Safetensors
bert
feature-extraction
dense
Generated from Trainer
dataset_size:799002
loss:MultipleNegativesSymmetricRankingLoss
Eval Results (legacy)
text-embeddings-inference
Instructions to use LamaDiab/MiniLM-v29-SemanticEngine with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use LamaDiab/MiniLM-v29-SemanticEngine with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("LamaDiab/MiniLM-v29-SemanticEngine") sentences = [ "essence multi task concealer 15 natural nude", "face make-up", "natural nude concealer", "mixsoon * una (master repair cream) enriched", "cosmetics" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [5, 5] - 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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1.2810499359795133,4000,0.9773898124694824
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1.6011523687580027,5000,0.9776001572608948
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1.9212548015364916,6000,0.9786518216133118
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1.2810499359795133,4000,0.9773898124694824
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1.6011523687580027,5000,0.9776001572608948
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1.9212548015364916,6000,0.9786518216133118
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2.2413572343149806,7000,0.9782311320304871
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2.881562099871959,9000,0.9782311320304871
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model.safetensors
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