Sentence Similarity
sentence-transformers
Safetensors
bert
feature-extraction
dense
Generated from Trainer
dataset_size:458830
loss:MultipleNegativesSymmetricRankingLoss
Eval Results (legacy)
text-embeddings-inference
Instructions to use LamaDiab/MiniLM-V10Data-128BATCH-SemanticEngine with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use LamaDiab/MiniLM-V10Data-128BATCH-SemanticEngine with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("LamaDiab/MiniLM-V10Data-128BATCH-SemanticEngine") sentences = [ "derby cap toe shoes - brown", "chained strapped block heeled sandals", "100% premium natural leather - high quality sole.", "puppy treats biscuits" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
Training in progress, epoch 5
Browse files- eval/triplet_evaluation_results.csv +3 -0
- model.safetensors +1 -1
eval/triplet_evaluation_results.csv
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3.3472803347280333,12000,0.960037887096405
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3.6262203626220364,13000,0.9618256092071533
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3.3472803347280333,12000,0.960037887096405
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3.905160390516039,14000,0.9621411561965942
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4.741980474198048,17000,0.9617204666137695
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model.safetensors
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