Sentence Similarity
sentence-transformers
Safetensors
bert
feature-extraction
dense
Generated from Trainer
dataset_size:554030
loss:MultipleNegativesSymmetricRankingLoss
Eval Results (legacy)
text-embeddings-inference
Instructions to use LamaDiab/MiniLM-V9Data-256BATCH-SemanticEngine with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use LamaDiab/MiniLM-V9Data-256BATCH-SemanticEngine with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("LamaDiab/MiniLM-V9Data-256BATCH-SemanticEngine") sentences = [ "pacman smoked turkey", "omelette with fresh basil & cherry tomatoes", "mozzarella pacman", " tote " ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 5a734daf273a8a000ef18071e313734702e65c52b94d9ef8416cfd9d0182edc8
- Size of remote file:
- 1.06 kB
- SHA256:
- cd12959b5fd17bd2c00bdb7bc696e0fabe0de5f51d12eb872380405072849754
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