Sentence Similarity
sentence-transformers
ONNX
Safetensors
Spanish
bert
embeddings
contrastive-learning
e-commerce
semantic-search
product-recommendation
spanish
text-embeddings-inference
Instructions to use Mateo-Rua/embeddings-productos-ecommerce-es with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use Mateo-Rua/embeddings-productos-ecommerce-es with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("Mateo-Rua/embeddings-productos-ecommerce-es") sentences = [ "Esa es una persona feliz", "Ese es un perro feliz", "Esa es una persona muy feliz", "Hoy es un día soleado" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 952174f03b93ed9e19ea539a90be89b05116b6d8fb83abaaaa47f61528df688d
- Size of remote file:
- 17.1 MB
- SHA256:
- fbcc3c7348739d765de2c86fce14c9c5e2dd47696dbb3cfeed294edfc712d8f0
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