Sentence Similarity
sentence-transformers
Safetensors
bert
feature-extraction
dense
Generated from Trainer
dataset_size:1006385
loss:MultipleNegativesSymmetricRankingLoss
Eval Results (legacy)
text-embeddings-inference
Instructions to use LamaDiab/MultiMiniLM-V25Data-256BATCH-SemanticEngine with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use LamaDiab/MultiMiniLM-V25Data-256BATCH-SemanticEngine with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("LamaDiab/MultiMiniLM-V25Data-256BATCH-SemanticEngine") sentences = [ "essence multi task concealer 15 natural nude", "tarte 4 in 1 mini mascara", "essence", "face make-up" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 0f781a285f34b5222c9e43b50133556d281868d36f4b4af518b63f9f6031055f
- Size of remote file:
- 90.9 MB
- SHA256:
- a3565b56539747ac0da1ec92d266962cb1d13d73db1d9e5374a3ec3d81b32176
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