Sentence Similarity
sentence-transformers
Safetensors
bert
feature-extraction
dense
Generated from Trainer
dataset_size:1006385
loss:MultipleNegativesSymmetricRankingLoss
Eval Results (legacy)
text-embeddings-inference
Instructions to use LamaDiab/MultiMiniLM-V25Data-256BATCH-SemanticEngine with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use LamaDiab/MultiMiniLM-V25Data-256BATCH-SemanticEngine with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("LamaDiab/MultiMiniLM-V25Data-256BATCH-SemanticEngine") sentences = [ "essence multi task concealer 15 natural nude", "tarte 4 in 1 mini mascara", "essence", "face make-up" ] 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 +4 -0
- model.safetensors +1 -1
eval/triplet_evaluation_results.csv
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0.254323499491353,1000,0.9444736838340759
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0.508646998982706,2000,0.9498369693756104
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0.762970498474059,3000,0.9575139284133911
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0.254323499491353,1000,0.9444736838340759
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0.508646998982706,2000,0.9498369693756104
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0.762970498474059,3000,0.9575139284133911
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1.0172852058973056,4000,0.9621411561965942
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1.5256736146415861,6000,0.964454710483551
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1.7798678190137265,7000,0.9657166600227356
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model.safetensors
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version https://git-lfs.github.com/spec/v1
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size 90864192
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