Sentence Similarity
sentence-transformers
Safetensors
bert
feature-extraction
dense
Generated from Trainer
dataset_size:1148773
loss:MultipleNegativesSymmetricRankingLoss
Eval Results (legacy)
text-embeddings-inference
Instructions to use LamaDiab/MiniLM-V16Data-128BATCH-SemanticEngine with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use LamaDiab/MiniLM-V16Data-128BATCH-SemanticEngine with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("LamaDiab/MiniLM-V16Data-128BATCH-SemanticEngine") sentences = [ "rosa / porcelain us andalusia mug", "klara and the sun", " mug", "mug" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 91b7365e44ee7e4b18735199cf68aadfc202272e0dab467e89e41ee45a48d2f5
- Size of remote file:
- 988 Bytes
- SHA256:
- ab248fb2b0dd57e14be54c04fc6d9ad2b2c6f95a5be5aec30ca40640f239d70b
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