Sentence Similarity
sentence-transformers
Safetensors
bert
feature-extraction
dense
Generated from Trainer
dataset_size:681637
loss:MultipleNegativesSymmetricRankingLoss
Eval Results (legacy)
text-embeddings-inference
Instructions to use LamaDiab/MiniLM-V13Data-256BATCH-SemanticEngine with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use LamaDiab/MiniLM-V13Data-256BATCH-SemanticEngine with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("LamaDiab/MiniLM-V13Data-256BATCH-SemanticEngine") sentences = [ "essence multi task concealer 15 natural nude", "one in shower cream sensitive 40 gr fruity", "natural nude concealer", "best ab wheel" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
Training in progress, epoch 4
Browse files- eval/triplet_evaluation_results.csv +3 -0
- model.safetensors +1 -1
eval/triplet_evaluation_results.csv
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1.8775816748028538,5000,0.9651908874511719
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2.253098009763425,6000,0.9658218622207642
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1.8775816748028538,5000,0.9651908874511719
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2.6286143447239954,7000,0.965927004814148
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3.755163349605708,10000,0.9690819382667542
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model.safetensors
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