Sentence Similarity
sentence-transformers
Safetensors
bert
feature-extraction
dense
Generated from Trainer
dataset_size:604740
loss:MultipleNegativesSymmetricRankingLoss
Eval Results (legacy)
text-embeddings-inference
Instructions to use LamaDiab/MiniLM-V26Data-256ShuffleBATCH-SemanticEngine with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use LamaDiab/MiniLM-V26Data-256ShuffleBATCH-SemanticEngine with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("LamaDiab/MiniLM-V26Data-256ShuffleBATCH-SemanticEngine") sentences = [ "casa chandelier", "new eleganza - 6-999-x", "casa chandelier", "chandlier" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 137a3ab08c44a91b96b873544163c65a18ca68a247b5469e99c9538e9c73d486
- Size of remote file:
- 90.9 MB
- SHA256:
- d86e09111400004838a7461d5b7f4fd0cf2f08fb9ea9819a49c01f32744d214c
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