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-128BATCH-SemanticEngine with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use LamaDiab/MiniLM-V10Data-128BATCH-SemanticEngine with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("LamaDiab/MiniLM-V10Data-128BATCH-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:
- ff8dde135b462c0d263173cd928053ad3923775a0386f33ecdc7e0af8732893e
- Size of remote file:
- 1.06 kB
- SHA256:
- baf312d3e639adcd71d4caa650b4367b66386891bae1a95526e0634eb2d6969c
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