Sentence Similarity
sentence-transformers
Safetensors
bert
feature-extraction
dense
Generated from Trainer
dataset_size:902990
loss:MultipleNegativesSymmetricRankingLoss
Eval Results (legacy)
text-embeddings-inference
Instructions to use LamaDiab/MiniLM-v32-SemanticEngine with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use LamaDiab/MiniLM-v32-SemanticEngine with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("LamaDiab/MiniLM-v32-SemanticEngine") sentences = [ "dove deodorant stick fresh", "women's deodorant", "antiperspirant deodorant stick", "bubbles natur.bottle w.hand pink(6)m280m" ] 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 +4 -0
- model.safetensors +1 -1
eval/triplet_evaluation_results.csv
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2.26685552407932,8000,0.9765476584434509
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2.55014164305949,9000,0.9766532778739929
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2.83342776203966,10000,0.9782379269599915
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2.26685552407932,8000,0.9765476584434509
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2.83342776203966,10000,0.9782379269599915
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3.11671388101983,11000,0.9778153300285339
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3.68328611898017,13000,0.9770758748054504
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3.96657223796034,14000,0.9772871136665344
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model.safetensors
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