Sentence Similarity
sentence-transformers
Safetensors
bert
feature-extraction
dense
Generated from Trainer
dataset_size:858018
loss:MultipleNegativesSymmetricRankingLoss
Eval Results (legacy)
text-embeddings-inference
Instructions to use LamaDiab/MiniLM-V11Data-128BATCH-SemanticEngine with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use LamaDiab/MiniLM-V11Data-128BATCH-SemanticEngine with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("LamaDiab/MiniLM-V11Data-128BATCH-SemanticEngine") sentences = [ "must kindergarten backpack mermazing 2 cases", "wooden islamic", " must backpack ", " gift paper sheet " ] 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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0.29832935560859186,1000,0.9628772735595703
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0.5966587112171837,2000,0.9732884764671326
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0.8949880668257757,3000,0.9795982837677002
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0.29832935560859186,1000,0.9628772735595703
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0.5966587112171837,2000,0.9732884764671326
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0.8949880668257757,3000,0.9795982837677002
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1.1933174224343674,4000,0.9819118976593018
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1.4916467780429594,5000,0.9834893345832825
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1.7899761336515514,6000,0.984015166759491
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model.safetensors
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size 90864192
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