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/NewMiniLM-V25Data-256BATCH-SemanticEngine with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use LamaDiab/NewMiniLM-V25Data-256BATCH-SemanticEngine with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("LamaDiab/NewMiniLM-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:
- 50734039e905542a65dff104b82f61d19a491e638f70d9f32f8089b34c9821e5
- Size of remote file:
- 90.9 MB
- SHA256:
- 9d5a5d66f392a8c82dd6af1221b5223764e9ea536c4c7bad76c57a3003d46474
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