Sentence Similarity
sentence-transformers
Safetensors
bert
feature-extraction
dense
Generated from Trainer
dataset_size:790756
loss:MultipleNegativesSymmetricRankingLoss
Eval Results (legacy)
text-embeddings-inference
Instructions to use LamaDiab/MiniLM-v35-SemanticEngine with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use LamaDiab/MiniLM-v35-SemanticEngine with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("LamaDiab/MiniLM-v35-SemanticEngine") sentences = [ "creamy black varnish for black leathers", "shoe accessory", "the first product scented, nourishing, polishing and preserving all types of leather 50 gr.", "steal the scene t-shirt" ] 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 +3 -0
- model.safetensors +1 -1
eval/triplet_evaluation_results.csv
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2.265286315108379,7000,0.9738009572029114
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2.5888062115820123,8000,0.9725332856178284
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2.265286315108379,7000,0.9738009572029114
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2.9123261080556455,9000,0.9735897183418274
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3.2358460045292787,10000,0.9734840393066406
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3.559365901002912,11000,0.9750686883926392
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model.safetensors
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