Sentence Similarity
sentence-transformers
Safetensors
bert
feature-extraction
dense
Generated from Trainer
dataset_size:647236
loss:MultipleNegativesSymmetricRankingLoss
Eval Results (legacy)
text-embeddings-inference
Instructions to use LamaDiab/Finetunningv2MiniLM-V22Data-128ConstantBATCH-SemanticEngine with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use LamaDiab/Finetunningv2MiniLM-V22Data-128ConstantBATCH-SemanticEngine with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("LamaDiab/Finetunningv2MiniLM-V22Data-128ConstantBATCH-SemanticEngine") sentences = [ "essence multi task concealer 15 natural nude", "pure oxygen 20 vol", "essence", "face make-up" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 4bf26352919642605a4660964600103e3c93bb04fabe19c037df6f9c969f5d1f
- Size of remote file:
- 90.9 MB
- SHA256:
- 3cd1e61b5768d19d49c9b204e248cb3a20bb2bcb19e2c1da6ab768bac7080250
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