Sentence Similarity
sentence-transformers
Safetensors
bert
feature-extraction
dense
Generated from Trainer
dataset_size:713598
loss:MultipleNegativesSymmetricRankingLoss
Eval Results (legacy)
text-embeddings-inference
Instructions to use LamaDiab/MiniLM-V23Data-256ConstantShuffleBATCH-SemanticEngine with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use LamaDiab/MiniLM-V23Data-256ConstantShuffleBATCH-SemanticEngine with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("LamaDiab/MiniLM-V23Data-256ConstantShuffleBATCH-SemanticEngine") sentences = [ "must kindergarten backpack mermazing 2 cases", "100 horse riding sleeveless gilet - black", " must backpack ", "bag" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 415c445006f6b01af181364a258d88a0a46c53a21d05d3a56f6506b4fe827ee5
- Size of remote file:
- 90.9 MB
- SHA256:
- aa9b832d94315858367dde1fa086f4a94f4adf1d8c034b7e33bdf23a762f75c1
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