Sentence Similarity
sentence-transformers
Safetensors
bert
feature-extraction
dense
Generated from Trainer
dataset_size:790756
loss:MultipleNegativesSymmetricRankingLoss
Eval Results (legacy)
text-embeddings-inference
Instructions to use LamaDiab/MiniLM-v35-SemanticEngine with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use LamaDiab/MiniLM-v35-SemanticEngine with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("LamaDiab/MiniLM-v35-SemanticEngine") sentences = [ "creamy black varnish for black leathers", "shoe accessory", "the first product scented, nourishing, polishing and preserving all types of leather 50 gr.", "steal the scene t-shirt" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
Training in progress, epoch 1
Browse files- eval/triplet_evaluation_results.csv +3 -2
- model.safetensors +1 -1
- training_args.bin +1 -1
eval/triplet_evaluation_results.csv
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