Sentence Similarity
sentence-transformers
Safetensors
bert
feature-extraction
dense
Generated from Trainer
dataset_size:169967
loss:MultipleNegativesSymmetricRankingLoss
Eval Results (legacy)
text-embeddings-inference
Instructions to use LamaDiab/MiniLM-SemanticEngine with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use LamaDiab/MiniLM-SemanticEngine with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("LamaDiab/MiniLM-SemanticEngine") sentences = [ "blue dianne", "soap", "maximize the freshness of your food for 12 hours with the blue dianne thermal bag. its triple compartments, spacious storage, heat resistance, and 100% leakproof design will keep it fresh. this bpa-free and pvc-free bag is also 100% non-toxic and comes with a 3-month guarantee. ideal for everyday food storage.", "trolley backpack coral high colors 17 l 3 zippers 23977" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- d24aa94cc7ad632b27f329b522318dd6a989649b8ca59138aece5b4688cf7427
- Size of remote file:
- 90.9 MB
- SHA256:
- 069a1ba1049fd58477cccc1475f3c1a357273bc6d102dfaaef180de8d51959c0
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