Sentence Similarity
sentence-transformers
Safetensors
bert
feature-extraction
dense
Generated from Trainer
dataset_size:485108
loss:MultipleNegativesSymmetricRankingLoss
Eval Results (legacy)
text-embeddings-inference
Instructions to use LamaDiab/MiniLM-V19Data-128ConstantBATCH-SemanticEngine with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use LamaDiab/MiniLM-V19Data-128ConstantBATCH-SemanticEngine with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("LamaDiab/MiniLM-V19Data-128ConstantBATCH-SemanticEngine") sentences = [ "must kindergarten backpack mermazing 2 cases", "olive acid wash t-shirt", " must backpack ", "bag" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 90248a74ab7c731babff9ab549bedb28ef811fdab95c5d14f158d24af9c71213
- Size of remote file:
- 90.9 MB
- SHA256:
- fff3afd2580807868efff4733225d2cb5915dbe8443e5b1b929bdbddfdb290f7
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