Sentence Similarity
sentence-transformers
Safetensors
bert
feature-extraction
dense
Generated from Trainer
dataset_size:858018
loss:MultipleNegativesSymmetricRankingLoss
Eval Results (legacy)
text-embeddings-inference
Instructions to use LamaDiab/MiniLM-V11Data-128BATCH-SemanticEngine with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use LamaDiab/MiniLM-V11Data-128BATCH-SemanticEngine with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("LamaDiab/MiniLM-V11Data-128BATCH-SemanticEngine") sentences = [ "must kindergarten backpack mermazing 2 cases", "wooden islamic", " must backpack ", " gift paper sheet " ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 92e3335e53a0eb275e587c51e8fba0d096b35ec8007dbbad09664b7a56644c01
- Size of remote file:
- 14.2 kB
- SHA256:
- 707e393783bf2d92660b929601d6cd9434bf2329d8bf0b79473f0e993ed33262
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