Sentence Similarity
sentence-transformers
Safetensors
bert
feature-extraction
dense
Generated from Trainer
dataset_size:704378
loss:MultipleNegativesSymmetricRankingLoss
Eval Results (legacy)
text-embeddings-inference
Instructions to use LamaDiab/MiniLM-V15Data-128BATCH-SemanticEngine with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use LamaDiab/MiniLM-V15Data-128BATCH-SemanticEngine with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("LamaDiab/MiniLM-V15Data-128BATCH-SemanticEngine") sentences = [ "must kindergarten backpack mermazing 2 cases", "wide leg popline pants b22", " kindergarten mermazing backpack ", "bag" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 4e7e0d445805a954c66b243909894839a7a8ca8761c125752be88ba50d9c5f60
- Size of remote file:
- 1.06 kB
- SHA256:
- 318e8f1b67bddc9ba1ee8882ab32676737dcc7564196a50f9d649799cc553dd4
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