Sentence Similarity
sentence-transformers
Safetensors
bert
feature-extraction
dense
Generated from Trainer
dataset_size:831141
loss:MultipleNegativesSymmetricRankingLoss
Eval Results (legacy)
text-embeddings-inference
Instructions to use LamaDiab/MiniLM-v30-SemanticEngine with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use LamaDiab/MiniLM-v30-SemanticEngine with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("LamaDiab/MiniLM-v30-SemanticEngine") sentences = [ "gerber organic apple spinach with kale", "baby food", "flavor free baby food", "my beauty nail art set" ] 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 +3 -0
- model.safetensors +1 -1
eval/triplet_evaluation_results.csv
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0.3079765937788728,1000,0.9618675112724304
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0.6159531875577456,2000,0.9678884744644165
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0.9239297813366184,3000,0.9701066613197327
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0.3079765937788728,1000,0.9618675112724304
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0.6159531875577456,2000,0.9678884744644165
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0.9239297813366184,3000,0.9701066613197327
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1.231763619575254,4000,0.9738037586212158
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1.5395506309633733,5000,0.9766557812690735
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1.847337642351493,6000,0.9769726395606995
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model.safetensors
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size 90864192
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