Sentence Similarity
sentence-transformers
Safetensors
bert
feature-extraction
dense
Generated from Trainer
dataset_size:458830
loss:MultipleNegativesSymmetricRankingLoss
Eval Results (legacy)
text-embeddings-inference
Instructions to use LamaDiab/MiniLM-V10Data-256BATCH-SemanticEngine with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use LamaDiab/MiniLM-V10Data-256BATCH-SemanticEngine with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("LamaDiab/MiniLM-V10Data-256BATCH-SemanticEngine") sentences = [ "derby cap toe shoes - brown", "chained strapped block heeled sandals", "100% premium natural leather - high quality sole.", "puppy treats biscuits" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 586d21d3d5a766adb085c3752386b8889cd22733df8c0b14895b8809dd602ff7
- Size of remote file:
- 90.9 MB
- SHA256:
- 6cadf7c5dd643cc25d4afc69f62dc6be6eabe8c240cd3a7d3f1744df49554428
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