Sentence Similarity
sentence-transformers
Safetensors
bert
feature-extraction
dense
Generated from Trainer
dataset_size:529974
loss:MultipleNegativesSymmetricRankingLoss
Eval Results (legacy)
text-embeddings-inference
Instructions to use LamaDiab/NewMiniLM-V21Data-128ConstantBATCH-SemanticEngine with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use LamaDiab/NewMiniLM-V21Data-128ConstantBATCH-SemanticEngine with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("LamaDiab/NewMiniLM-V21Data-128ConstantBATCH-SemanticEngine") sentences = [ "essence multi task concealer 15 natural nude", "ahc vitamin c sheet mask", " concealer", "face make-up" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 53412a75bbeb9ecf1eb0deff6f710293bd515a5aaf0b988d8d636c926fec1ea6
- Size of remote file:
- 90.9 MB
- SHA256:
- f0b7f570f919718ab47e3ceb64936d00caddba9125b1a2d47668704740185ad5
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