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/FinetuningMiniLM-V23Data-256ConstantBATCH-SemanticEngine with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use LamaDiab/FinetuningMiniLM-V23Data-256ConstantBATCH-SemanticEngine with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("LamaDiab/FinetuningMiniLM-V23Data-256ConstantBATCH-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:
- c0456b56fbade8ffffc928e715dc3d9a924821db86949dca00c6fe41d9d13cff
- Size of remote file:
- 133 MB
- SHA256:
- dc7bd88319d45e161425d355a30be5576c14abf65566bfd3cd42936527d9a856
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