Sentence Similarity
sentence-transformers
ONNX
Safetensors
bert
feature-extraction
Generated from Trainer
dataset_size:68
loss:MultipleNegativesRankingLoss
text-embeddings-inference
Instructions to use Cescofors75/baco-embeddings with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use Cescofors75/baco-embeddings with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("Cescofors75/baco-embeddings") sentences = [ "Pollo al curri rojo", "Vino blanco seco, ligero, refrescante, delicado y sauve. Con sabor ligero y poco denso, acostumbran a ser vinos jóvenes. Con aromas a cítricos, manzana verde, melocotón, piña e hinojo.", "Vinos rosados envejecidos en barrica, y también los vinos rosados elaborados a partir de Cabernet, Merlot o Syrah. Vinos rosados redondos, afrutados, de color intenso y sabor potente y sabroso. Con maceración de la piel.", "Vino blanco afrutado de medio cuerpo, con aromas a melocotón, piña, uva, fruta de la pasión, queroseno y flores. Ejemplos de variedades: los Verdejo de Rueda, Valencia Moscatell, los Malvasía de Canarias, los Riesling de Alsacia i Alemania. los Gerwüztraminer." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
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