Sentence Similarity
sentence-transformers
Safetensors
bert
feature-extraction
dense
Generated from Trainer
dataset_size:790823
loss:MultipleNegativesSymmetricRankingLoss
Eval Results (legacy)
text-embeddings-inference
Instructions to use LamaDiab/NewMiniLM-V27Data-256BATCH-SemanticEngine with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use LamaDiab/NewMiniLM-V27Data-256BATCH-SemanticEngine with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("LamaDiab/NewMiniLM-V27Data-256BATCH-SemanticEngine") sentences = [ "essence multi task concealer 15 natural nude", "adidas men shower gel 3 in 1", " essence multi task concealer", "face make-up" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- f86b7b06ff08c396729830ea271d3b060638854b8228e652efeb221736f8dd01
- Size of remote file:
- 90.9 MB
- SHA256:
- cb6d07e2706b19bfb655c8c1ae05c5c50333d784c28aeb714eaa4b396866b4ef
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