Sentence Similarity
sentence-transformers
Safetensors
bert
feature-extraction
dense
Generated from Trainer
dataset_size:291522
loss:MultipleNegativesSymmetricRankingLoss
Eval Results (legacy)
text-embeddings-inference
Instructions to use LamaDiab/MiniLM-256BATCH-V6Data-SemanticEngine with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use LamaDiab/MiniLM-256BATCH-V6Data-SemanticEngine with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("LamaDiab/MiniLM-256BATCH-V6Data-SemanticEngine") sentences = [ "cream 21 baby oil with almond oil", "hi, barbie! bundle", "nourishing baby oil", "material: wooden. size: 15 x 30 cm." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 39f44908ee01ff9800edbf67f3ab2d7986c39eb7b41a17054fb51220d758f254
- Size of remote file:
- 90.9 MB
- SHA256:
- 0de1341106721af21f3461d17d84cdb01ceb68a29d60797b692a6ebc5dc4e341
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