Sentence Similarity
sentence-transformers
Safetensors
bert
feature-extraction
dense
Generated from Trainer
dataset_size:169967
loss:MultipleNegativesSymmetricRankingLoss
Eval Results (legacy)
text-embeddings-inference
Instructions to use LamaDiab/V2MiniLM-SemanticEngine with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use LamaDiab/V2MiniLM-SemanticEngine with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("LamaDiab/V2MiniLM-SemanticEngine") sentences = [ "blue dianne", "soap", "maximize the freshness of your food for 12 hours with the blue dianne thermal bag. its triple compartments, spacious storage, heat resistance, and 100% leakproof design will keep it fresh. this bpa-free and pvc-free bag is also 100% non-toxic and comes with a 3-month guarantee. ideal for everyday food storage.", "trolley backpack coral high colors 17 l 3 zippers 23977" ] 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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1.1295180722891567,1500,0.9353724718093872
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1.1295180722891567,1500,0.9353724718093872
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1.8825301204819276,2500,0.9407992362976074
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2.2590361445783134,3000,0.9412925243377686
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model.safetensors
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