Sentence Similarity
sentence-transformers
Safetensors
bert
feature-extraction
dense
Generated from Trainer
dataset_size:790993
loss:MultipleNegativesSymmetricRankingLoss
Eval Results (legacy)
text-embeddings-inference
Instructions to use LamaDiab/MiniLM-v27-SemanticEngine with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use LamaDiab/MiniLM-v27-SemanticEngine with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("LamaDiab/MiniLM-v27-SemanticEngine") sentences = [ "essence multi task concealer 15 natural nude", "face make-up", "adidas men shower gel 3 in 1", "health_beauty", "beauty", " essence multi task concealer" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [6, 6] - 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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1.2943078913324708,4000,0.9668734669685364
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1.6177231565329884,5000,0.9679251313209534
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1.941138421733506,6000,0.9669786691665649
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1.2943078913324708,4000,0.9668734669685364
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1.6177231565329884,5000,0.9679251313209534
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1.941138421733506,6000,0.9669786691665649
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2.2645536869340233,7000,0.9683457612991333
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2.5879689521345406,8000,0.968766450881958
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2.9113842173350584,9000,0.968766450881958
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model.safetensors
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size 90864192
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