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/V2MiniLM-SemanticEngine with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use LamaDiab/V2MiniLM-SemanticEngine with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("LamaDiab/V2MiniLM-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:
- 09083ac7d727acf98211d1c2961a99380999889616e01f1705604b1d6073e365
- Size of remote file:
- 90.9 MB
- SHA256:
- 514476828b04b380a581ca1e7cabb9c858b6af7db6dfad0fedc1c98ed6649018
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